diff --git a/Dockerfile b/Dockerfile index 9f1a8c15b..3d872bf88 100644 --- a/Dockerfile +++ b/Dockerfile @@ -61,7 +61,7 @@ RUN python3 -m pip --no-cache-dir install \ RUN python3 -m pip install git+https://github.com/tier4/t4-devkit@v0.5.1 # Install autoware-perception-evaluation -RUN python3 -m pip install git+https://github.com/tier4/autoware_perception_evaluation@f67600293e3e74861f913da3efafd6bf436d2ab6 +RUN python3 -m pip install git+https://github.com/tier4/autoware_perception_evaluation@85b78e52bca312911ea6730163dfeaa63c66c628 # Need to dowgrade setuptools to 60.2.0 to fix setup RUN python3 -m pip --no-cache-dir install \ diff --git a/autoware_ml/configs/detection3d/dataset/t4dataset/base.py b/autoware_ml/configs/detection3d/dataset/t4dataset/base.py index 3be587072..7f4be6293 100644 --- a/autoware_ml/configs/detection3d/dataset/t4dataset/base.py +++ b/autoware_ml/configs/detection3d/dataset/t4dataset/base.py @@ -29,6 +29,9 @@ "db_j6gen2_v7", "db_j6gen2_v8", "db_j6gen2_v9", + "db_j6gen2_v10", + "db_j6gen2_v11", + "db_j6gen2_v12", "db_largebus_v1", "db_largebus_v2", "db_largebus_v3", @@ -146,6 +149,8 @@ "traffic_cone": "traffic_cone", "trafficcone": "traffic_cone", "barrier": "barrier", + "other_vehicle": "car", + "other_pedestrian": "pedestrian", } class_names = ["car", "truck", "bus", "bicycle", "pedestrian", "traffic_cone", "barrier"] diff --git a/autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2.py b/autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2.py index 0324e7207..a87166019 100644 --- a/autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2.py +++ b/autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2.py @@ -27,6 +27,9 @@ "db_j6gen2_v7", "db_j6gen2_v8", "db_j6gen2_v9", + "db_j6gen2_v10", + "db_j6gen2_v11", + "db_j6gen2_v12", ] dataset_test_groups = { @@ -127,6 +130,8 @@ "traffic_cone": "traffic_cone", "trafficcone": "traffic_cone", "barrier": "barrier", + "other_vehicle": "car", + "other_pedestrian": "pedestrian", } class_names = [ diff --git a/autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2_base.py b/autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2_base.py index b9ec03f27..ef0141a5b 100644 --- a/autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2_base.py +++ b/autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2_base.py @@ -27,6 +27,9 @@ "db_j6gen2_v7", "db_j6gen2_v8", "db_j6gen2_v9", + "db_j6gen2_v10", + "db_j6gen2_v11", + "db_j6gen2_v12", "db_largebus_v1", "db_largebus_v2", "db_largebus_v3", @@ -133,6 +136,8 @@ "traffic_cone": "traffic_cone", "trafficcone": "traffic_cone", "barrier": "barrier", + "other_vehicle": "car", + "other_pedestrian": "pedestrian", } diff --git a/autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_base.py b/autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_base.py index c08decfa1..0f00a651d 100644 --- a/autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_base.py +++ b/autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_base.py @@ -123,6 +123,8 @@ "traffic_cone": "traffic_cone", "trafficcone": "traffic_cone", "barrier": "barrier", + "other_vehicle": "car", + "other_pedestrian": "pedestrian", } class_names = [ diff --git a/autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_gen2.py b/autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_gen2.py index dbd6e2813..9995cd9b7 100644 --- a/autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_gen2.py +++ b/autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_gen2.py @@ -120,6 +120,8 @@ "traffic_cone": "traffic_cone", "trafficcone": "traffic_cone", "barrier": "barrier", + "other_vehicle": "car", + "other_pedestrian": "pedestrian", } class_names = [ diff --git a/autoware_ml/configs/detection3d/dataset/t4dataset/largebus.py b/autoware_ml/configs/detection3d/dataset/t4dataset/largebus.py index 2212b8e56..cd42362b5 100644 --- a/autoware_ml/configs/detection3d/dataset/t4dataset/largebus.py +++ b/autoware_ml/configs/detection3d/dataset/t4dataset/largebus.py @@ -122,6 +122,8 @@ "traffic_cone": "traffic_cone", "trafficcone": "traffic_cone", "barrier": "barrier", + "other_vehicle": "car", + "other_pedestrian": "pedestrian", } class_names = [ diff --git a/autoware_ml/detection3d/datasets/transforms/__init__.py b/autoware_ml/detection3d/datasets/transforms/__init__.py index 6bc932f1a..a63ff1eea 100644 --- a/autoware_ml/detection3d/datasets/transforms/__init__.py +++ b/autoware_ml/detection3d/datasets/transforms/__init__.py @@ -1,3 +1,4 @@ +from .local_3d_bbox import Local3DBBoxExpand from .object_min_points_filter import ObjectMinPointsFilter -__all__ = ["ObjectMinPointsFilter"] +__all__ = ["ObjectMinPointsFilter", "Local3DBBoxExpand"] diff --git a/autoware_ml/detection3d/datasets/transforms/local_3d_bbox.py b/autoware_ml/detection3d/datasets/transforms/local_3d_bbox.py new file mode 100644 index 000000000..ae06d4005 --- /dev/null +++ b/autoware_ml/detection3d/datasets/transforms/local_3d_bbox.py @@ -0,0 +1,58 @@ +from typing import List + +import numpy as np +from mmcv.transforms import BaseTransform +from mmdet3d.structures.ops import box_np_ops +from mmengine.registry import TRANSFORMS + + +@TRANSFORMS.register_module() +class Local3DBBoxExpand(BaseTransform): + """Locally expand the 3D bounding boxes by scaling the width, which it doesn't scale the points. + + Args: + expand_widths: (List[float]): Uniformly sampled expand width. + width_dim: (int): The dimension of the width. Default is 4, which is the width dimension of the 3D + bounding box. Since 3D Bbox is in the format of [x, y, z, dx, dy, dz, heading], the width dimension is the + 4th dimension. + label_ids: (List[int]): The label IDs to expand. If None, all label IDs will be expanded. + """ + + def __init__(self, expand_widths: List[float], width_dim: int = 4, label_ids: List[int] = None) -> None: + assert isinstance(expand_widths, list) + assert len(expand_widths) == 2 + assert expand_widths[0] < expand_widths[1] + self.expand_widths = expand_widths + self.width_dim = width_dim + self.label_ids = label_ids + + def transform(self, input_dict: dict) -> dict: + """Call function to locally augment the 3D bounding boxes by scaling the width. + + Args: + input_dict (dict): Result dict from loading pipeline. + + Returns: + dict: Results after locally augmenting the 3D bounding boxes by scaling the width, 'gt_bboxes_3d' \ + key is updated in the result dict. + """ + # Label mask + if self.label_ids is not None: + label_masks = [True if label in self.label_ids else False for label in input_dict["gt_labels_3d"]] + else: + label_masks = np.ones(len(input_dict["gt_labels_3d"]), dtype=bool) + + for i in range(len(input_dict["gt_bboxes_3d"])): + if not label_masks[i]: + continue + + expand_width = np.random.uniform(self.expand_widths[0], self.expand_widths[1]) + input_dict["gt_bboxes_3d"].tensor[i, self.width_dim] += expand_width + + return input_dict + + def __repr__(self) -> str: + """str: Return a string that describes the module.""" + repr_str = self.__class__.__name__ + repr_str += f"(expand_widths={self.expand_widths}, width_dim={self.width_dim}, label_ids={self.label_ids})" + return repr_str diff --git a/autoware_ml/detection3d/datasets/transforms/object_min_points_filter.py b/autoware_ml/detection3d/datasets/transforms/object_min_points_filter.py index 3ed510769..610a26767 100644 --- a/autoware_ml/detection3d/datasets/transforms/object_min_points_filter.py +++ b/autoware_ml/detection3d/datasets/transforms/object_min_points_filter.py @@ -34,6 +34,7 @@ def transform(self, input_dict: dict) -> dict: indices = box_np_ops.points_in_rbbox( points.tensor.numpy()[:, :3], gt_bboxes_3d.tensor.numpy()[:, :7], + origin=(0.5, 0.5, 0.0), # Always assume it's center_x, center_y and bottom_z ) num_points_in_gt = indices.sum(0) gt_bboxes_mask = num_points_in_gt >= self.min_num_points @@ -107,6 +108,7 @@ def transform(self, input_dict: dict) -> dict: indices = box_np_ops.points_in_rbbox( points.tensor.numpy()[:, :3], gt_bboxes_3d.tensor.numpy()[:, :7], + origin=(0.5, 0.5, 0.0), # Always assume it's center_x, center_y and bottom_z ) num_points_in_gt = indices.sum(0) diff --git a/autoware_ml/detection3d/evaluation/t4metric/t4metric_v2.py b/autoware_ml/detection3d/evaluation/t4metric/t4metric_v2.py index c7865320f..ffc5fee4c 100644 --- a/autoware_ml/detection3d/evaluation/t4metric/t4metric_v2.py +++ b/autoware_ml/detection3d/evaluation/t4metric/t4metric_v2.py @@ -11,6 +11,7 @@ import torch from mmdet3d.registry import METRICS from mmdet3d.structures import LiDARInstance3DBoxes +from mmdet3d.structures.ops import box_np_ops from mmengine.dist import get_world_size from mmengine.evaluator import BaseMetric from mmengine.logging import MessageHub, MMLogger @@ -243,6 +244,7 @@ def __init__( experiment_name: str, experiment_group_name: str, write_metric_summary: bool, + min_num_points: int = 0, evaluate_frame_prefix: bool = True, checkpoint_path: Optional[Union[Path, str]] = None, scene_batch_size: int = 128, @@ -268,6 +270,7 @@ def __init__( self.experiment_name = experiment_name self.experiment_group_name = experiment_group_name self.name_mapping = name_mapping + self.min_num_points = min_num_points if name_mapping is not None: self.class_names = [self.name_mapping.get(name, name) for name in self.class_names] @@ -435,10 +438,11 @@ def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: # Skip processing if result pickle already exists return - for data_sample in data_samples: + batch_points = data_batch["inputs"]["points"] + for data_sample, points in zip(data_samples, batch_points): current_time = data_sample["timestamp"] scene_id = self._parse_scene_id(data_sample["lidar_path"]) - frame_ground_truth = self._parse_ground_truth_from_sample(current_time, data_sample) + frame_ground_truth = self._parse_ground_truth_from_sample(current_time, data_sample, points) perception_frame = self._parse_predictions_from_sample(current_time, data_sample, frame_ground_truth) self._save_perception_frame(scene_id, data_sample["sample_idx"], perception_frame) @@ -985,11 +989,27 @@ def _aggregate_metrics_data( # Create precision_interpolate and recall_interpolate keys iterable_metrics[ - f"T4MetricV2_label_detection/{label_name}_precisions_{matching_mode}_{threshold}" + f"T4MetricV2_label_detection/{label_name}_interp-precisions_{matching_mode}_{threshold}" ] = ap.precision_interp.tolist() iterable_metrics[ - f"T4MetricV2_label_detection/{label_name}_recalls_{matching_mode}_{threshold}" + f"T4MetricV2_label_detection/{label_name}_interp-recalls_{matching_mode}_{threshold}" ] = ap.recall_interp.tolist() + iterable_metrics[ + f"T4MetricV2_label_detection/{label_name}_interp-confs_{matching_mode}_{threshold}" + ] = ap.conf_interp.tolist() + + # TP error metrics (e.g. ATE, AOE, ASE, AVE, AAE) + if ap.tp_error_metrics is not None: + for tp_error_metric in ap.tp_error_metrics: + mode = tp_error_metric.mode + average_mode = tp_error_metric.average_mode + + iterable_metrics[ + f"T4MetricV2_label_detection/{label_name}_{mode}_values_{matching_mode}_{threshold}" + ] = tp_error_metric.values.tolist() + iterable_metrics[ + f"T4MetricV2_label_detection/{label_name}_{mode}_interp-values_{matching_mode}_{threshold}" + ] = tp_error_metric.interpolated_values.tolist() return iterable_metrics @@ -1044,6 +1064,40 @@ def _process_metrics_for_aggregation(self, metrics_score: MetricsScore, evaluato ap.optimal_precision ) + # Number of prediction matches (TPs) and matches at the optimal confidence threshold + metric_dict[f"T4MetricV2_label/{label_name}_num-match_{matching_mode}_{threshold}"] = ap.num_tp + metric_dict[f"T4MetricV2_label/{label_name}_min-recall-num-match_{matching_mode}_{threshold}"] = ( + ap.num_tp_at_min_recall_conf + ) + metric_dict[ + f"T4MetricV2_label/{label_name}_medium-recall-num-match_{matching_mode}_{threshold}" + ] = ap.num_tp_at_medium_recall_conf + metric_dict[f"T4MetricV2_label/{label_name}_optimal-num-match_{matching_mode}_{threshold}"] = ( + ap.num_tp_at_optimal_conf + ) + + # TP error metrics (e.g. ATE, AOE, ASE, AVE, AAE) + if ap.tp_error_metrics is not None: + for tp_error_metric in ap.tp_error_metrics: + mode = tp_error_metric.mode + average_mode = tp_error_metric.average_mode + + metric_dict[ + f"T4MetricV2_label/{label_name}_tp-error_{average_mode}_{matching_mode}_{threshold}" + ] = tp_error_metric.avg_metric + metric_dict[ + f"T4MetricV2_label/{label_name}_tp-error-min-recall-conf_{average_mode}_{matching_mode}_{threshold}" + ] = tp_error_metric.min_recall_conf + metric_dict[ + f"T4MetricV2_label/{label_name}_tp-error-optimal-{average_mode}_{matching_mode}_{threshold}" + ] = tp_error_metric.optimal_avg_metric + metric_dict[ + f"T4MetricV2_label/{label_name}_tp-error-medium-{average_mode}_{matching_mode}_{threshold}" + ] = tp_error_metric.medium_avg_metric + metric_dict[ + f"T4MetricV2_label/{label_name}_tp-error-medium-recall-conf-{average_mode}_{matching_mode}_{threshold}" + ] = tp_error_metric.medium_recall_conf + # Label metadata key metric_dict[f"metadata_label/test_{label_name}_num_predictions"] = label_num_preds metric_dict[f"metadata_label/test_{label_name}_num_ground_truths"] = label_num_gts @@ -1054,6 +1108,41 @@ def _process_metrics_for_aggregation(self, metrics_score: MetricsScore, evaluato metric_dict[map_key] = map_instance.map metric_dict[maph_key] = map_instance.maph + # Add mean TP errors (e.g. mATE, mAOE, mASE, mAVE, mAAE) + if map_instance.mean_tp_errors is not None: + for mean_tp_error_name, mean_tp_error_value in map_instance.mean_tp_errors.items(): + metric_dict[f"T4MetricV2/mean-tp-error_{mean_tp_error_name}_{matching_mode}"] = mean_tp_error_value + + optimal_mean_tp_errors = map_instance.optimal_mean_tp_errors.get(mean_tp_error_name, None) + if optimal_mean_tp_errors is not None: + metric_dict[f"T4MetricV2/mean-tp-error-optimal-{mean_tp_error_name}_{matching_mode}"] = ( + optimal_mean_tp_errors + ) + + medium_mean_tp_errors = map_instance.medium_mean_tp_errors.get(mean_tp_error_name, None) + if medium_mean_tp_errors is not None: + metric_dict[f"T4MetricV2/mean-tp-error-medium-{mean_tp_error_name}_{matching_mode}"] = ( + medium_mean_tp_errors + ) + + # Add NuScenes Detection Score (NDS) based on mAP and mAPH + if map_instance.map_based_nds is not None: + metric_dict[f"T4MetricV2/{map_instance.map_based_nds.metric_prefix_name}_nds_{matching_mode}"] = ( + map_instance.map_based_nds.nds + ) + if map_instance.medium_map_based_nds is not None: + metric_dict[ + f"T4MetricV2/{map_instance.medium_map_based_nds.metric_prefix_name}_nds_{matching_mode}" + ] = map_instance.medium_map_based_nds.nds + if map_instance.mapH_based_nds is not None: + metric_dict[f"T4MetricV2/{map_instance.mapH_based_nds.metric_prefix_name}_nds_{matching_mode}"] = ( + map_instance.mapH_based_nds.nds + ) + if map_instance.medium_mapH_based_nds is not None: + metric_dict[ + f"T4MetricV2/{map_instance.medium_mapH_based_nds.metric_prefix_name}_nds_{matching_mode}" + ] = map_instance.medium_mapH_based_nds.nds + total_num_preds = num_preds # Selected evaluator @@ -1295,7 +1384,7 @@ def _parse_scene_id(self, lidar_path: str) -> str: except ValueError: return _UNKNOWN - def _parse_ground_truth_from_sample(self, time: float, data_sample: Dict[str, Any]) -> FrameGroundTruth: + def _parse_ground_truth_from_sample(self, time: float, data_sample: Dict[str, Any], points) -> FrameGroundTruth: """Parses ground truth objects from the given data sample. Args: @@ -1325,6 +1414,15 @@ def _parse_ground_truth_from_sample(self, time: float, data_sample: Dict[str, An # num_lidar_pts: (N,) array of int, number of LiDAR points inside each GT box num_lidar_pts: np.ndarray = eval_info.get("num_lidar_pts", np.array([])) + if self.min_num_points > 0 and len(bboxes): + points_cpu = points.cpu().numpy() + indices = box_np_ops.points_in_rbbox(points_cpu[:, :3], bboxes[:, :7]) + num_points_in_gt = indices.sum(0) + bboxes_mask = num_points_in_gt >= self.min_num_points + bboxes = bboxes[bboxes_mask] + gt_labels_3d = gt_labels_3d[bboxes_mask] + num_lidar_pts = num_lidar_pts[bboxes_mask] + dynamic_objects = [ DynamicObject( unix_time=time, diff --git a/projects/BEVFusion/bevfusion/__init__.py b/projects/BEVFusion/bevfusion/__init__.py index 60a64b532..e84525651 100644 --- a/projects/BEVFusion/bevfusion/__init__.py +++ b/projects/BEVFusion/bevfusion/__init__.py @@ -1,6 +1,7 @@ from .bevfusion import BEVFusion from .bevfusion_head import BEVFusionHead, ConvFuser from .bevfusion_necks import GeneralizedLSSFPN +from .bevfusion_voxel_encoder import HardSimpleVoxelSinCosEncoder from .depth_lss import DepthLSSTransform, LSSTransform from .loading import BEVLoadMultiViewImageFromFiles from .sparse_encoder import BEVFusionSparseEncoder @@ -27,4 +28,5 @@ "BEVFusionRandomFlip3D", "BEVFusionGlobalRotScaleTrans", "TransFusionBBoxCoder", + "HardSimpleVoxelSinCosEncoder", ] diff --git a/projects/BEVFusion/bevfusion/bevfusion.py b/projects/BEVFusion/bevfusion/bevfusion.py index 243b3beb5..709d851a9 100644 --- a/projects/BEVFusion/bevfusion/bevfusion.py +++ b/projects/BEVFusion/bevfusion/bevfusion.py @@ -56,12 +56,10 @@ def __init__( super().__init__(data_preprocessor=data_preprocessor, init_cfg=init_cfg) if voxelize_cfg is not None: - self.voxelize_reduce = voxelize_cfg.pop("voxelize_reduce") self.pts_voxel_layer = Voxelization(**voxelize_cfg) self.pts_voxel_encoder = MODELS.build(pts_voxel_encoder) self.pts_middle_encoder = MODELS.build(pts_middle_encoder) else: - self.voxelize_reduce = False self.pts_voxel_layer = None self.pts_voxel_encoder = None self.pts_middle_encoder = None @@ -181,7 +179,7 @@ def extract_img_feat( if not using_image_features: x = self.get_image_backbone_features(x) - with torch.cuda.amp.autocast(enabled=False): + with torch.amp.autocast("cuda", enabled=False): # with torch.autocast(device_type='cuda', dtype=torch.float32): x = self.view_transform( x, @@ -202,23 +200,18 @@ def extract_img_feat( def extract_pts_feat(self, feats, coords, sizes, points=None) -> torch.Tensor: if points is not None: # NOTE(knzo25): training and normal inference - with torch.cuda.amp.autocast(enabled=False): - # with torch.autocast('cuda', enabled=False): + with torch.amp.autocast("cuda", enabled=False): points = [point.float() for point in points] feats, coords, sizes = self.voxelize(points) batch_size = coords[-1, 0] + 1 else: - # NOTE(knzo25): onnx inference. Voxelization happens outside the graph - with torch.cuda.amp.autocast(enabled=False): - # with torch.autocast('cuda', enabled=False): - + # NOTE: (knzo25): onnx inference. Voxelization happens outside the graph + with torch.amp.autocast("cuda", enabled=False): # NOTE(knzo25): onnx demmands this # batch_size = coords[-1, 0] + 1 batch_size = 1 print("Run onnx point_eSpConvst") - assert self.voxelize_reduce - if self.voxelize_reduce: - feats = feats.sum(dim=1, keepdim=False) / sizes.type_as(feats).view(-1, 1) + feats = self.pts_voxel_encoder(feats, sizes, coords) x = self.pts_middle_encoder(feats, coords, batch_size) return x @@ -241,11 +234,8 @@ def voxelize(self, points): feats = torch.cat(feats, dim=0) coords = torch.cat(coords, dim=0) - if len(sizes) > 0: - sizes = torch.cat(sizes, dim=0) - if self.voxelize_reduce: - feats = feats.sum(dim=1, keepdim=False) / sizes.type_as(feats).view(-1, 1) - feats = feats.contiguous() + assert len(sizes) > 0, "No points in the voxel" + sizes = torch.cat(sizes, dim=0) return feats, coords, sizes diff --git a/projects/BEVFusion/bevfusion/bevfusion_head.py b/projects/BEVFusion/bevfusion/bevfusion_head.py index c37c5a538..2d713b022 100644 --- a/projects/BEVFusion/bevfusion/bevfusion_head.py +++ b/projects/BEVFusion/bevfusion/bevfusion_head.py @@ -63,6 +63,7 @@ def __init__( norm_cfg=dict(type="BN1d"), bias="auto", # loss + loss_iou=None, loss_cls=dict(type="mmdet.GaussianFocalLoss", reduction="mean"), loss_bbox=dict(type="mmdet.L1Loss", reduction="mean"), loss_heatmap=dict(type="mmdet.GaussianFocalLoss", reduction="mean"), @@ -88,8 +89,10 @@ def __init__( if not self.use_sigmoid_cls: self.num_classes += 1 self.loss_cls = MODELS.build(loss_cls) + self.loss_iou = MODELS.build(loss_iou) if loss_iou is not None else None self.loss_bbox = MODELS.build(loss_bbox) self.loss_heatmap = MODELS.build(loss_heatmap) + self.share_conv_out_channels = hidden_channel self.bbox_coder = build_bbox_coder(bbox_coder) self.sampling = False @@ -155,7 +158,11 @@ def __init__( # Position Embedding for Cross-Attention, which is re-used during training # noqa: E501 x_size = self.test_cfg["grid_size"][0] // self.test_cfg["out_size_factor"] y_size = self.test_cfg["grid_size"][1] // self.test_cfg["out_size_factor"] - self.bev_pos = self.create_2D_grid(x_size, y_size) + self.spatial_dim = x_size * y_size + bev_pos = self.create_2D_grid(x_size, y_size) + + # Register the bev_pos as a buffer so it moves to the GPU automatically. + self.register_buffer("bev_pos", bev_pos, persistent=False) # (1, H * W, 2) self.img_feat_pos = None self.img_feat_collapsed_pos = None @@ -174,11 +181,22 @@ def __init__( self.dense_heatmap_exclude_pooling_classes = sorted( list(set(self.class_name_to_indices.values()) - set(self.dense_heatmap_pooling_class_indices)) ) + # Pre-compute the correct order of the classes for the final local_max + heatmap_concat_order = ( + self.dense_heatmap_pooling_class_indices + self.dense_heatmap_exclude_pooling_classes + ) + local_concat_class_remapping = [heatmap_concat_order.index(i) for i in range(self.num_classes)] else: self.dense_heatmap_pooling_class_indices = None self.dense_heatmap_exclude_pooling_classes = None + local_concat_class_remapping = [i for i in range(self.num_classes)] + # Register the remapping as a buffer so it moves to the GPU automatically and gets saved in the state_dict. + self.register_buffer( + "local_concat_class_remapping", torch.tensor(local_concat_class_remapping), persistent=False + ) self.local_heatmap_padding = self.nms_kernel_size // 2 + # NMS clusters self.nms_clusters = self.test_cfg.get("nms_clusters", []) # Add class indices for nms @@ -199,7 +217,8 @@ def __init__( self.partial_ignore_labels = None print_log(f"BEVFusionHead Partial ignore labels: {self.partial_ignore_labels}, dense heatmap pooling classes: \ - {self.dense_heatmap_pooling_classes}, class_names: {self.class_names}", logger="current") + {self.dense_heatmap_pooling_classes}, class_names: {self.class_names}, \ + local_concat_class_remapping: {self.local_concat_class_remapping}", logger="current") def create_2D_grid(self, x_size, y_size): meshgrid = [[0, x_size - 1, x_size], [0, y_size - 1, y_size]] @@ -247,24 +266,20 @@ def forward_single(self, inputs, metas): Returns: list[dict]: Output results for tasks. """ - batch_size = inputs.shape[0] fusion_feat = self.shared_conv(inputs) ################################# # image to BEV ################################# - fusion_feat_flatten = fusion_feat.view(batch_size, fusion_feat.shape[1], -1) # [BS, C, H*W] - bev_pos = self.bev_pos.repeat(batch_size, 1, 1).to(fusion_feat.device) + fusion_feat_flatten = fusion_feat.view(-1, self.share_conv_out_channels, self.spatial_dim) # [BS, C, H*W] ################################# # query initialization ################################# - with torch.cuda.amp.autocast(enabled=False): - # with torch.autocast('cuda', enabled=False): + with torch.amp.autocast("cuda", enabled=False): dense_heatmap = self.heatmap_head(fusion_feat.float()) + heatmap = dense_heatmap.detach().sigmoid() - local_max = torch.zeros_like(heatmap) - # equals to nms radius = voxel_size * out_size_factor * kenel_size if self.dense_heatmap_pooling_class_indices is not None: # Pooling selected_heatmap = heatmap[:, self.dense_heatmap_pooling_class_indices, :, :] @@ -274,29 +289,44 @@ def forward_single(self, inputs, metas): stride=1, padding=0, ) - local_max[ - :, - self.dense_heatmap_pooling_class_indices, - self.local_heatmap_padding : (-self.local_heatmap_padding), - self.local_heatmap_padding : (-self.local_heatmap_padding), - ] = local_max_inner - # Non-pooling classes + + # 2. Restore spatial size using F.pad instead of slice mutation + local_max = F.pad( + local_max_inner, + ( + self.local_heatmap_padding, + self.local_heatmap_padding, + self.local_heatmap_padding, + self.local_heatmap_padding, + ), + mode="constant", + value=0.0, + ) + + # 3. Any non-pooling classes if self.dense_heatmap_exclude_pooling_classes: - local_max[:, self.dense_heatmap_exclude_pooling_classes] = heatmap[ - :, self.dense_heatmap_exclude_pooling_classes - ] + excluded_local_max = heatmap[:, self.dense_heatmap_exclude_pooling_classes, :, :] + local_max = torch.cat([local_max, excluded_local_max], dim=1) + local_max = local_max[:, self.local_concat_class_remapping, :, :] else: local_max = heatmap heatmap = heatmap * (heatmap == local_max) - heatmap = heatmap.view(batch_size, heatmap.shape[1], -1) + # (BS, num_classes, H*W) + heatmap = heatmap.view(-1, self.num_classes, self.spatial_dim) # top num_proposals among all classes - top_proposals = heatmap.view(batch_size, -1).argsort(dim=-1, descending=True)[..., : self.num_proposals] - top_proposals_class = top_proposals // heatmap.shape[-1] - top_proposals_index = top_proposals % heatmap.shape[-1] + flattened_heatmap = heatmap.view(-1, self.num_classes * self.spatial_dim) + + # Use topk instead of argsort to avoid sorting the entire flattened heatmap. + _, top_proposals = torch.topk(flattened_heatmap, k=self.num_proposals, dim=-1, largest=True, sorted=False) + + # 2. Calculate class and spatial indices + # Use shape[-1] dynamically to handle grid sizes safely. + top_proposals_class = top_proposals // self.spatial_dim + top_proposals_index = top_proposals % self.spatial_dim query_feat = fusion_feat_flatten.gather( - index=top_proposals_index[:, None, :].expand(-1, fusion_feat_flatten.shape[1], -1), + index=top_proposals_index[:, None, :].expand(-1, self.share_conv_out_channels, -1), dim=-1, ) self.query_labels = top_proposals_class @@ -306,10 +336,8 @@ def forward_single(self, inputs, metas): query_cat_encoding = self.class_encoding(one_hot.float()) query_feat += query_cat_encoding - query_pos = bev_pos.gather( - index=top_proposals_index[:, None, :].permute(0, 2, 1).expand(-1, -1, bev_pos.shape[-1]), - dim=1, - ) + # (B, N, 2) + query_pos = self.bev_pos.squeeze(0)[top_proposals_index] ################################# # transformer decoder layer (Fusion feature as K,V) ################################# @@ -317,7 +345,9 @@ def forward_single(self, inputs, metas): for i in range(self.num_decoder_layers): # Transformer Decoder Layer # :param query: B C Pq :param query_pos: B Pq 3/6 - query_feat = self.decoder[i](query_feat, key=fusion_feat_flatten, query_pos=query_pos, key_pos=bev_pos) + query_feat = self.decoder[i]( + query_feat, key=fusion_feat_flatten, query_pos=query_pos, key_pos=self.bev_pos + ) # Prediction res_layer = self.prediction_heads[i](query_feat) @@ -384,8 +414,10 @@ def predict_by_feat(self, preds_dicts, metas, img=None, rescale=False, for_roi=F for layer_id, preds_dict in enumerate(preds_dicts): batch_size = preds_dict[0]["heatmap"].shape[0] batch_score = preds_dict[0]["heatmap"][..., -self.num_proposals :].sigmoid() - # if self.loss_iou.loss_weight != 0: - # batch_score = torch.sqrt(batch_score * preds_dict[0]['iou'][..., -self.num_proposals:].sigmoid()) # noqa: E501 + if self.loss_iou is not None: + batch_score = torch.sqrt( + batch_score * preds_dict[0]["iou"][..., -self.num_proposals :].sigmoid() + ) # noqa: E501 one_hot = F.one_hot(self.query_labels, num_classes=self.num_classes).permute(0, 2, 1) batch_score = batch_score * preds_dict[0]["query_heatmap_score"] * one_hot @@ -433,6 +465,7 @@ def predict_by_feat(self, preds_dicts, metas, img=None, rescale=False, for_roi=F circle_nms( boxes_for_nms.detach().cpu().numpy(), nms_cluster["nms_threshold"], + post_max_size=nms_cluster["post_max_size"], ) ) else: @@ -780,8 +813,9 @@ def loss_by_feat( for cls_i, class_name in enumerate(self.class_names): loss_dict[f"loss_heatmap_{class_name}"] = loss_heatmap_cls[cls_i] - # Prevent loss item to avoid computing gradients twice. This is for logging. - loss_dict["total_dense_heatmap"] = loss_heatmap_cls.sum() + # Logging-only aggregate. Detach so it does not retain the autograd graph; + # the per-class `loss_heatmap_{class_name}` entries are what drive gradients. + loss_dict["total_dense_heatmap"] = loss_heatmap_cls.sum().detach() # compute loss for each layer for idx_layer in range(self.num_decoder_layers if self.auxiliary else 1): @@ -855,7 +889,21 @@ def loss_by_feat( loss_dict[f"{prefix}_loss_cls"] = layer_loss_cls loss_dict[f"{prefix}_loss_bbox"] = layer_loss_bbox - # loss_dict[f'{prefix}_loss_iou'] = layer_loss_iou + + # Output iou for iou-aware loss + if self.loss_iou is not None: + layer_ious = preds_dict["iou"][ + ..., + idx_layer * self.num_proposals : (idx_layer + 1) * self.num_proposals, + ].squeeze( + 1 + ) # [BS, num_proposals] + + # [BS, num_proposals] + layer_iou_weights = layer_bbox_weights[:, :, 0] + loss_dict[f"{prefix}_loss_iou"] = self.loss_iou( + layer_ious, ious, layer_iou_weights, avg_factor=max(num_pos, 1) + ) loss_dict["matched_ious"] = layer_loss_cls.new_tensor(matched_ious) diff --git a/projects/BEVFusion/bevfusion/bevfusion_voxel_encoder.py b/projects/BEVFusion/bevfusion/bevfusion_voxel_encoder.py new file mode 100644 index 000000000..02fe76b57 --- /dev/null +++ b/projects/BEVFusion/bevfusion/bevfusion_voxel_encoder.py @@ -0,0 +1,76 @@ +from typing import Optional, Tuple + +import numpy as np +import torch +from mmdet3d.models.voxel_encoders.utils import PFNLayer, get_paddings_indicator +from mmdet3d.registry import MODELS +from torch import Tensor, nn + + +@MODELS.register_module() +class HardSimpleVoxelSinCosEncoder(nn.Module): + def __init__( + self, min_norm_values: Tuple[float], max_norm_values: Tuple[float], in_channels: Optional[int] = 4 + ) -> None: + """ + Simple voxel encoder that only performs mean pooling on the normalize features, and then + performs sin-cos (fourier encoding) on each voxel channels. + + The output shape of each voxel is (N, feature_channels*2). + Args: + min_norm_values (Tuple[float]): Minimum values for the features. + max_norm_values (Tuple[float]): Maximum values for the features. + in_channels (int): Number of input channels. + """ + super().__init__() + + # Create PillarFeatureNet layers + self.in_channels = in_channels + + # Convert the ((x - min) / (max - min)) * pi * exponents to x * scale + bias for folding them into one OP + min_norm_values = torch.tensor(min_norm_values) + max_norm_values = torch.tensor(max_norm_values) + # Let alpha = pi * exponents, beta = max - min + # y = ((x - min) / beta) * alpha + # y = alpha / beta * (x - min) + # y = (alpha / beta) * x - (alpha / beta) * min + # Therefore, scale = alpha / beta, bias = - (alpha * min) / beta + # y = scale * x + bias + exponents = (2 ** torch.arange(0, self.in_channels)).float() + alpha = (torch.pi * exponents).unsqueeze(0) # (1, C) + beta = (max_norm_values - min_norm_values).unsqueeze(1) # (C, 1) + scale = alpha / beta + bias = -(alpha * min_norm_values.unsqueeze(1)) / beta # (C, C) + + self.register_buffer("exponent_scale", scale.unsqueeze(0), persistent=False) # (1, C, C) + self.register_buffer("exponent_bias", bias.unsqueeze(0), persistent=False) # (1, C, C) + + def forward(self, features: Tensor, num_points: Tensor, coors: Tensor, *args, **kwargs) -> Tensor: + """Forward function. + + Args: + features (torch.Tensor): Point features or raw points in shape + (N, M, C) in (x, y, z, intensity, time_lag) if C is 5, (x, y, z, time_lag) if C is 4. + num_points (torch.Tensor): Number of points in each pillar in shape (M). + coors (torch.Tensor): Coordinates of each voxel in (M, [4]), which is (batch_idx, z_idx, y_idx, x_idx). + + Returns: + torch.Tensor: Features of pillars in shape (M, C*C*2). + + """ + # Mean in the voxel + # (N, M, C) -> (N, C) + voxel_mean_features = ( + features.sum(dim=1, keepdim=False) / num_points.type_as(features).view(-1, 1) + ).contiguous() + + # x * scale + bias, (1, C, C) + (1, C, C) * (N, C, 1) -> (N, C, C) + # FMA (fused multiply-add): y = bias + scale * voxel_mean_features + y = torch.addcmul(self.exponent_bias, self.exponent_scale, voxel_mean_features.unsqueeze(-1)) + # SinCos encoding + # (N*C, C) -> (N, C*C) + y = y.reshape(-1, self.in_channels * self.in_channels) + # (N, C*C) -> (N, C*C*2) + voxel_fourier_features = torch.cat([torch.cos(y), torch.sin(y)], dim=1) + + return voxel_fourier_features diff --git a/projects/BEVFusion/bevfusion/custom_sparse_conv_tensor.py b/projects/BEVFusion/bevfusion/custom_sparse_conv_tensor.py new file mode 100644 index 000000000..8671532eb --- /dev/null +++ b/projects/BEVFusion/bevfusion/custom_sparse_conv_tensor.py @@ -0,0 +1,35 @@ +""" +Custom SparseConvTensor for BEVFusion. +This customiztion is used to support cleaner ONNX export of sparse convolutions. +""" + +import torch +from mmdet3d.models.layers.spconv import IS_SPCONV2_AVAILABLE + +if IS_SPCONV2_AVAILABLE: + from spconv.pytorch import SparseConvTensor +else: + from mmcv.ops import SparseConvTensor + + +def sparse_to_dense(sparse_tensor: SparseConvTensor, batch_size: int, spatial_shapes: list[int], out_channels: int): + """ + Convert the sparse tensor to a dense tensor. + """ + H, W, D = spatial_shapes + num_cells = batch_size * H * W * D + idx = sparse_tensor.indices.to(sparse_tensor.features.device).long() # [N, 1+D] + b, h, w, d = idx.unbind(1) + # b * (H * W * D) + h*(W*D) + w*D + d + # Factor out the common terms D and W + # (b*H*W + h*W + w) * D + d -> (b*H + h) * W + w) * D + d + linear_idx = ((b * H + h) * W + w) * D + d # [N] + + out = torch.zeros( + [num_cells, sparse_tensor.features.shape[1]], + device=sparse_tensor.features.device, + dtype=sparse_tensor.features.dtype, + ) + scatter_idx = linear_idx.unsqueeze(1).expand(-1, out_channels) # [N, C] + out = out.scatter(0, scatter_idx, sparse_tensor.features) + return out.view(batch_size, H, W, D, out_channels) diff --git a/projects/BEVFusion/bevfusion/sparse_encoder.py b/projects/BEVFusion/bevfusion/sparse_encoder.py index 019cb630c..4b4fd00f8 100644 --- a/projects/BEVFusion/bevfusion/sparse_encoder.py +++ b/projects/BEVFusion/bevfusion/sparse_encoder.py @@ -1,4 +1,10 @@ # Copyright (c) OpenMMLab. All rights reserved. + +import os +from typing import Dict, Optional + +import numpy as np +import torch from mmdet3d.models.layers import make_sparse_convmodule from mmdet3d.models.layers.spconv import IS_SPCONV2_AVAILABLE from mmdet3d.models.middle_encoders import SparseEncoder @@ -9,8 +15,7 @@ else: from mmcv.ops import SparseConvTensor -import numpy as np -import torch +from .custom_sparse_conv_tensor import sparse_to_dense @MODELS.register_module() @@ -24,6 +29,7 @@ class BEVFusionSparseEncoder(SparseEncoder): Args: in_channels (int): The number of input channels. sparse_shape (list[int]): The sparse shape of input tensor. + dense_output_shape (list[int]): The final shape of the dense output tensor. order (list[str], optional): Order of conv module. Defaults to ('conv', 'norm', 'act'). norm_cfg (dict, optional): Config of normalization layer. Defaults to @@ -47,10 +53,8 @@ class BEVFusionSparseEncoder(SparseEncoder): def __init__( self, in_channels, - aug_features_min_values, - aug_features_max_values, - num_aug_features, sparse_shape, + dense_output_shapes, order=("conv", "norm", "act"), norm_cfg=dict(type="BN1d", eps=1e-3, momentum=0.01), base_channels=16, @@ -63,10 +67,8 @@ def __init__( super(SparseEncoder, self).__init__() assert block_type in ["conv_module", "basicblock"] self.sparse_shape = sparse_shape + self.dense_output_shapes = dense_output_shapes self.in_channels = in_channels - self.register_buffer("aug_features_min_values", torch.tensor(aug_features_min_values)) - self.register_buffer("aug_features_max_values", torch.tensor(aug_features_max_values)) - self.num_aug_features = num_aug_features self.order = order self.base_channels = base_channels self.output_channels = output_channels @@ -77,10 +79,6 @@ def __init__( self.return_middle_feats = return_middle_feats # Spconv init all weight on its own - if num_aug_features: - self.in_channels = in_channels * num_aug_features * 2 - self.register_buffer("exponents", (2 ** torch.arange(0, num_aug_features).float())) - assert isinstance(order, tuple) and len(order) == 3 assert set(order) == {"conv", "norm", "act"} @@ -140,16 +138,6 @@ def forward(self, voxel_features, coors, batch_size): output features. When self.return_middle_feats is True, the module returns middle features. """ - - if self.num_aug_features: - num_points = voxel_features.shape[0] - x = (voxel_features - self.aug_features_min_values.view(1, -1)) / ( - self.aug_features_max_values - self.aug_features_min_values - ).view(1, -1) - y = x.reshape(-1, 1) * np.pi * self.exponents.reshape(1, -1) - y = y.reshape(num_points, -1) - voxel_features = torch.cat([torch.cos(y), torch.sin(y)], dim=1) - coors = coors.int() input_sp_tensor = SparseConvTensor(voxel_features, coors, self.sparse_shape, batch_size) x = self.conv_input(input_sp_tensor) @@ -162,11 +150,15 @@ def forward(self, voxel_features, coors, batch_size): # for detection head # [200, 176, 5] -> [200, 176, 2] out = self.conv_out(encode_features[-1]) - spatial_features = out.dense() - N, C, H, W, D = spatial_features.shape - spatial_features = spatial_features.permute(0, 1, 4, 2, 3).contiguous() - spatial_features = spatial_features.view(N, C * D, H, W) + spatial_features = sparse_to_dense(out, batch_size, self.dense_output_shapes, self.output_channels) + spatial_features = spatial_features.permute(0, 4, 3, 1, 2).contiguous() + spatial_features = spatial_features.view( + batch_size, + self.output_channels * self.dense_output_shapes[2], + self.dense_output_shapes[0], + self.dense_output_shapes[1], + ) if self.return_middle_feats: return spatial_features, encode_features diff --git a/projects/BEVFusion/bevfusion/utils.py b/projects/BEVFusion/bevfusion/utils.py index c47604dbd..39c6a0ded 100644 --- a/projects/BEVFusion/bevfusion/utils.py +++ b/projects/BEVFusion/bevfusion/utils.py @@ -85,26 +85,31 @@ def decode(self, heatmap, rot, dim, center, height, vel, filter=False): final_box_preds = torch.cat([center, height, dim, rot, vel], dim=1).permute(0, 2, 1) predictions_dicts = [] - for i in range(heatmap.shape[0]): - boxes3d = final_box_preds[i] - scores = final_scores[i] - labels = final_preds[i] - predictions_dict = {"bboxes": boxes3d, "scores": scores, "labels": labels} - predictions_dicts.append(predictions_dict) - - if filter is False: + if not filter: + for i in range(heatmap.shape[0]): + boxes3d = final_box_preds[i] + scores = final_scores[i] + labels = final_preds[i] + predictions_dict = {"bboxes": boxes3d, "scores": scores, "labels": labels} + predictions_dicts.append(predictions_dict) return predictions_dicts # use score threshold if self.score_threshold is not None: - thresh_mask = final_scores > self.score_threshold + if isinstance(self.score_threshold, float): + thresh_mask = final_scores > self.score_threshold + elif isinstance(self.score_threshold, (list, tuple)): + score_threshold = final_scores.new_tensor(self.score_threshold) + thresh_mask = final_scores > score_threshold[final_preds] + else: + raise ValueError("score_threshold must be a float or list") + predictions_dicts = [] if self.post_center_range is not None: self.post_center_range = torch.tensor(self.post_center_range, device=heatmap.device) mask = (final_box_preds[..., :3] >= self.post_center_range[:3]).all(2) mask &= (final_box_preds[..., :3] <= self.post_center_range[3:]).all(2) - predictions_dicts = [] for i in range(heatmap.shape[0]): cmask = mask[i, :] if self.score_threshold: @@ -114,7 +119,6 @@ def decode(self, heatmap, rot, dim, center, height, vel, filter=False): scores = final_scores[i, cmask] labels = final_preds[i, cmask] predictions_dict = {"bboxes": boxes3d, "scores": scores, "labels": labels} - predictions_dicts.append(predictions_dict) else: raise NotImplementedError( diff --git a/projects/BEVFusion/configs/deploy/bevfusion_main_body_lidar_only_intensity_tensorrt_dynamic.py b/projects/BEVFusion/configs/deploy/bevfusion_main_body_lidar_only_intensity_tensorrt_dynamic.py index e22e0f41b..96ccbf583 100644 --- a/projects/BEVFusion/configs/deploy/bevfusion_main_body_lidar_only_intensity_tensorrt_dynamic.py +++ b/projects/BEVFusion/configs/deploy/bevfusion_main_body_lidar_only_intensity_tensorrt_dynamic.py @@ -1,11 +1,7 @@ codebase_config = dict(type="mmdet3d", task="VoxelDetection", model_type="end2end") custom_imports = dict( - imports=[ - "projects.BEVFusion.deploy", - "projects.BEVFusion.bevfusion", - "projects.SparseConvolution", - ], + imports=["projects.BEVFusion.deploy", "projects.BEVFusion.bevfusion", "projects.SparseConvolution"], allow_failed_imports=False, ) @@ -16,7 +12,7 @@ dict( input_shapes=dict( voxels=dict( - min_shape=[1, 10, 5], opt_shape=[64000, 10, 5], max_shape=[256000, 10, 5] + min_shape=[1, 32, 5], opt_shape=[64000, 32, 5], max_shape=[256000, 32, 5] ), # [M, maximum number of points, features] features=5 when using intensity coors=dict(min_shape=[1, 3], opt_shape=[64000, 3], max_shape=[256000, 3]), num_points_per_voxel=dict(min_shape=[1], opt_shape=[64000], max_shape=[256000]), @@ -29,7 +25,7 @@ type="onnx", export_params=True, keep_initializers_as_inputs=False, - opset_version=17, + opset_version=18, save_file="bevfusion_lidar_intensity.onnx", input_names=["voxels", "coors", "num_points_per_voxel"], output_names=["bbox_pred", "score", "label_pred"], @@ -45,5 +41,5 @@ }, }, input_shape=None, - verbose=True, + verbose=False, ) diff --git a/projects/BEVFusion/configs/deploy/bevfusion_main_body_lidar_only_tensorrt_dynamic.py b/projects/BEVFusion/configs/deploy/bevfusion_main_body_lidar_only_tensorrt_dynamic.py index 7c7d9355d..d6ec91051 100644 --- a/projects/BEVFusion/configs/deploy/bevfusion_main_body_lidar_only_tensorrt_dynamic.py +++ b/projects/BEVFusion/configs/deploy/bevfusion_main_body_lidar_only_tensorrt_dynamic.py @@ -16,7 +16,7 @@ dict( input_shapes=dict( voxels=dict( - min_shape=[1, 10, 4], opt_shape=[64000, 10, 4], max_shape=[256000, 10, 4] + min_shape=[1, 32, 4], opt_shape=[64000, 32, 4], max_shape=[256000, 32, 4] ), # [M, maximum number of points, features] features=5 when using intensity coors=dict(min_shape=[1, 3], opt_shape=[64000, 3], max_shape=[256000, 3]), num_points_per_voxel=dict(min_shape=[1], opt_shape=[64000], max_shape=[256000]), @@ -29,7 +29,7 @@ type="onnx", export_params=True, keep_initializers_as_inputs=False, - opset_version=17, + opset_version=18, save_file="bevfusion_lidar.onnx", input_names=["voxels", "coors", "num_points_per_voxel"], output_names=["bbox_pred", "score", "label_pred"], @@ -45,5 +45,5 @@ }, }, input_shape=None, - verbose=True, + verbose=False, ) diff --git a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m.py b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m.py similarity index 93% rename from projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m.py rename to projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m.py index d32dc9c70..3edd06c92 100644 --- a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m.py +++ b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m.py @@ -3,7 +3,7 @@ "../../../../../autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2_base.py", "../default/pipelines/default_lidar_intensity_120m.py", "../default/models/default_lidar_second_secfpn_120m.py", - "../default/schedulers/default_30e_8xb8_adamw_cosine.py", + "../default/schedulers/default_30e_8xb16_adamw_cosine.py", "../default/default_misc.py", ] @@ -16,7 +16,7 @@ info_directory_path = "info/user_name/" experiment_group_name = "bevfusion_lidar_intensity/j6gen2_base/" + _base_.dataset_type -experiment_name = "lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m" +experiment_name = "lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m" work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name # model parameter @@ -25,22 +25,18 @@ voxelize_cfg=dict( point_cloud_range=_base_.point_cloud_range, voxel_size=_base_.voxel_size, - voxelize_reduce=True, ), - pts_voxel_encoder=dict(num_features=_base_.point_use_dim), - pts_middle_encoder=dict( - in_channels=_base_.point_use_dim, - sparse_shape=_base_.grid_size, - num_aug_features=5, + pts_voxel_encoder=dict( + in_channels=len(_base_.lidar_sweep_dims), # min-max normalization for x, y, z, intensity, time_lag, where the max of time lag technically is two seeps (200 ms) here - aug_features_min_values=[ + min_norm_values=[ _base_.point_cloud_range[0], _base_.point_cloud_range[1], _base_.point_cloud_range[2], 0.0, 0.0, ], - aug_features_max_values=[ + max_norm_values=[ _base_.point_cloud_range[3], _base_.point_cloud_range[4], _base_.point_cloud_range[5], @@ -48,6 +44,11 @@ 0.2, ], ), + pts_middle_encoder=dict( + in_channels=50, + sparse_shape=_base_.grid_size, + dense_output_shapes=_base_.sparse_dense_output_shapes, + ), bbox_head=dict( class_names=_base_.class_names, # Use class names to identify the correct class indices train_cfg=dict( @@ -163,5 +164,4 @@ checkpoint=dict(type="CheckpointHook", interval=1, max_keep_ckpts=3, save_best="NuScenes metric/T4Metric/mAP"), ) log_processor = dict(window_size=50) - load_from = None diff --git a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m_t4metric_v2.py b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m_t4metric_v2.py similarity index 93% rename from projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m_t4metric_v2.py rename to projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m_t4metric_v2.py index 260080bc6..8931d3008 100644 --- a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m_t4metric_v2.py +++ b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m_t4metric_v2.py @@ -1,10 +1,10 @@ _base_ = [ - "./bevfusion_lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m.py", + "./bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m.py", ] # user setting experiment_group_name = "bevfusion_lidar_intensity/j6gen2_base/" + _base_.dataset_type -experiment_name = "lidar_voxel_second_secfpn_30e_8xb8_j6gen2_base_120m_t4metric_v2" +experiment_name = "lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m_t4metric_v2" work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name # Add evaluator configs @@ -50,6 +50,7 @@ name_mapping={{_base_.name_mapping}}, experiment_name=experiment_name, experiment_group_name=_base_.experiment_group_name, + min_num_points=2, ) test_evaluator = dict( @@ -72,4 +73,5 @@ name_mapping={{_base_.name_mapping}}, experiment_name=experiment_name, experiment_group_name=_base_.experiment_group_name, + min_num_points=2, ) diff --git a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m.py b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m.py similarity index 93% rename from projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m.py rename to projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m.py index 8fa1509b9..05947c2fd 100644 --- a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m.py +++ b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m.py @@ -3,7 +3,7 @@ "../../../../../autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_base.py", "../default/pipelines/default_lidar_intensity_120m.py", "../default/models/default_lidar_second_secfpn_120m.py", - "../default/schedulers/default_30e_8xb8_adamw_cosine.py", + "../default/schedulers/default_30e_8xb16_adamw_cosine.py", "../default/default_misc.py", ] @@ -16,7 +16,7 @@ info_directory_path = "info/user_name/" experiment_group_name = "bevfusion_lidar_intensity/jpntaxi_base/" + _base_.dataset_type -experiment_name = "lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m" +experiment_name = "lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m" work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name # model parameter @@ -25,22 +25,18 @@ voxelize_cfg=dict( point_cloud_range=_base_.point_cloud_range, voxel_size=_base_.voxel_size, - voxelize_reduce=True, ), - pts_voxel_encoder=dict(num_features=_base_.point_use_dim), - pts_middle_encoder=dict( - in_channels=_base_.point_use_dim, - sparse_shape=_base_.grid_size, - num_aug_features=5, + pts_voxel_encoder=dict( + in_channels=len(_base_.lidar_sweep_dims), # min-max normalization for x, y, z, intensity, time_lag, where the max of time lag technically is two seeps (200 ms) here - aug_features_min_values=[ + min_norm_values=[ _base_.point_cloud_range[0], _base_.point_cloud_range[1], _base_.point_cloud_range[2], 0.0, 0.0, ], - aug_features_max_values=[ + max_norm_values=[ _base_.point_cloud_range[3], _base_.point_cloud_range[4], _base_.point_cloud_range[5], @@ -48,6 +44,11 @@ 0.2, ], ), + pts_middle_encoder=dict( + in_channels=50, + sparse_shape=_base_.grid_size, + dense_output_shapes=_base_.sparse_dense_output_shapes, + ), bbox_head=dict( class_names=_base_.class_names, # Use class names to identify the correct class indices train_cfg=dict( diff --git a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m_t4metric_v2.py b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m_t4metric_v2.py similarity index 93% rename from projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m_t4metric_v2.py rename to projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m_t4metric_v2.py index 3d543e709..b6c23eeb8 100644 --- a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m_t4metric_v2.py +++ b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m_t4metric_v2.py @@ -1,10 +1,10 @@ _base_ = [ - "./bevfusion_lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m.py", + "./bevfusion_lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m.py", ] # user setting experiment_group_name = "bevfusion_lidar_intensity/jpntaxi_base/" + _base_.dataset_type -experiment_name = "lidar_voxel_second_secfpn_30e_8xb8_jpntaxi_base_120m_t4metric_v2" +experiment_name = "lidar_voxel_second_secfpn_30e_8xb16_jpntaxi_base_120m_t4metric_v2" work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name # Add evaluator configs @@ -50,6 +50,7 @@ name_mapping={{_base_.name_mapping}}, experiment_name=experiment_name, experiment_group_name=_base_.experiment_group_name, + min_num_points=2, ) test_evaluator = dict( @@ -72,4 +73,5 @@ name_mapping={{_base_.name_mapping}}, experiment_name=experiment_name, experiment_group_name=_base_.experiment_group_name, + min_num_points=2, ) diff --git a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb8_base_120m.py b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb16_base_120m.py similarity index 87% rename from projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb8_base_120m.py rename to projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb16_base_120m.py index e8068332a..7716a1508 100644 --- a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb8_base_120m.py +++ b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb16_base_120m.py @@ -3,7 +3,7 @@ "../../../../../autoware_ml/configs/detection3d/dataset/t4dataset/base.py", "../default/pipelines/default_lidar_120m.py", "../default/models/default_lidar_second_secfpn_120m.py", - "../default/schedulers/default_50e_8xb8_adamw_cosine.py", + "../default/schedulers/default_50e_8xb16_adamw_cosine.py", "../default/default_misc.py", ] @@ -16,7 +16,7 @@ info_directory_path = "info/user_name/" experiment_group_name = "bevfusion_lidar/base/" + _base_.dataset_type -experiment_name = "lidar_voxel_second_secfpn_50e_8xb8_base_120m" +experiment_name = "lidar_voxel_second_secfpn_50e_8xb16_base_120m" work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name # model parameter @@ -25,26 +25,17 @@ voxelize_cfg=dict( point_cloud_range=_base_.point_cloud_range, voxel_size=_base_.voxel_size, - voxelize_reduce=True, ), - pts_voxel_encoder=dict(num_features=_base_.point_use_dim), + pts_voxel_encoder=dict( + in_channels=len(_base_.lidar_sweep_dims), + # min-max normalization for x, y, z, time_lag, where the max of time lag technically is two seeps (200 ms) here + min_norm_values=[_base_.point_cloud_range[0], _base_.point_cloud_range[1], _base_.point_cloud_range[2], 0.0], + max_norm_values=[_base_.point_cloud_range[3], _base_.point_cloud_range[4], _base_.point_cloud_range[5], 0.2], + ), pts_middle_encoder=dict( - in_channels=_base_.point_use_dim, + in_channels=32, sparse_shape=_base_.grid_size, - num_aug_features=4, - # min-max normalization for x, y, z, time_lag, where the max of time lag technically is two seeps (200 ms) here - aug_features_min_values=[ - _base_.point_cloud_range[0], - _base_.point_cloud_range[1], - _base_.point_cloud_range[2], - 0.0, - ], - aug_features_max_values=[ - _base_.point_cloud_range[3], - _base_.point_cloud_range[4], - _base_.point_cloud_range[5], - 0.2, - ], + dense_output_shapes=_base_.sparse_dense_output_shapes, ), bbox_head=dict( class_names=_base_.class_names, # Use class names to identify the correct class indices diff --git a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb8_base_120m_t4metric_v2.py b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb16_base_120m_t4metric_v2.py similarity index 91% rename from projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb8_base_120m_t4metric_v2.py rename to projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb16_base_120m_t4metric_v2.py index efcd091f5..6f1c88ec4 100644 --- a/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb8_base_120m_t4metric_v2.py +++ b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb16_base_120m_t4metric_v2.py @@ -1,10 +1,10 @@ _base_ = [ - "./bevfusion_lidar_voxel_second_secfpn_50e_8xb8_base_120m.py", + "./bevfusion_lidar_voxel_second_secfpn_50e_8xb16_base_120m.py", ] # user setting experiment_group_name = "bevfusion_lidar/base/" + _base_.dataset_type -experiment_name = "lidar_voxel_second_secfpn_50e_8xb8_base_120m_t4metric_v2" +experiment_name = "lidar_voxel_second_secfpn_50e_8xb16_base_120m_t4metric_v2" work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name # Add evaluator configs @@ -18,7 +18,7 @@ frame_pass_fail_config = dict( target_labels=_base_.class_names, # Matching thresholds per class (must align with `plane_distance_thresholds` used in evaluation) - matching_threshold_list=[2.0, 2.0, 2.0, 2.0, 2.0], + matching_threshold_list=[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0], confidence_threshold_list=None, ) @@ -50,6 +50,7 @@ name_mapping={{_base_.name_mapping}}, experiment_name=experiment_name, experiment_group_name=_base_.experiment_group_name, + min_num_points=2, ) test_evaluator = dict( @@ -72,4 +73,5 @@ name_mapping={{_base_.name_mapping}}, experiment_name=experiment_name, experiment_group_name=_base_.experiment_group_name, + min_num_points=2, ) diff --git a/projects/BEVFusion/configs/t4dataset/default/models/default_lidar_second_secfpn_120m.py b/projects/BEVFusion/configs/t4dataset/default/models/default_lidar_second_secfpn_120m.py index 809179b20..11b57c1cd 100644 --- a/projects/BEVFusion/configs/t4dataset/default/models/default_lidar_second_secfpn_120m.py +++ b/projects/BEVFusion/configs/t4dataset/default/models/default_lidar_second_secfpn_120m.py @@ -1,25 +1,25 @@ num_proposals = 500 -max_num_points = 10 +max_num_points = 32 max_voxels = [120000, 160000] +out_size_factor = 8 model = dict( type="BEVFusion", voxelize_cfg=dict( max_num_points=max_num_points, max_voxels=max_voxels, - voxelize_reduce=True, ), data_preprocessor=dict( type="Det3DDataPreprocessor", pad_size_divisor=32, ), - pts_voxel_encoder=dict(type="HardSimpleVFE"), + pts_voxel_encoder=dict( + type="HardSimpleVoxelSinCosEncoder", + in_channels=4, + ), pts_middle_encoder=dict( type="BEVFusionSparseEncoder", in_channels=5, - aug_features_min_values=[], - aug_features_max_values=[], - num_aug_features=0, order=("conv", "norm", "act"), norm_cfg=dict(type="BN1d", eps=0.001, momentum=0.01), encoder_channels=((16, 16, 32), (32, 32, 64), (64, 64, 128), (128, 128)), @@ -69,7 +69,7 @@ ), train_cfg=dict( dataset="t4datasets", - out_size_factor=8, + out_size_factor=out_size_factor, gaussian_overlap=0.1, min_radius=2, pos_weight=-1, @@ -84,23 +84,28 @@ ), test_cfg=dict( dataset="t4datasets", - out_size_factor=8, - nms_type=None, # Set to "circle" for circle_nms + out_size_factor=out_size_factor, + nms_type="circle", # Set to "circle" for circle_nms # Set NMS for different clusters nms_clusters=[ - dict(class_names=["car", "truck", "bus"], nms_threshold=0.5), # It's radius if using circle_nms - dict(class_names=["bicycle"], nms_threshold=0.5), - dict(class_names=["pedestrian"], nms_threshold=0.175), - dict(class_names=["barrier"], nms_threshold=0.5), - dict(class_names=["traffic_cone"], nms_threshold=0.175), + # Sqrt(0.25) = 0.50 + dict( + class_names=["car", "truck", "bus"], class_indices=[0, 1, 2], nms_threshold=0.25, post_max_size=300 + ), # It's radius if using circle_nms + dict(class_names=["bicycle"], class_indices=[3], nms_threshold=0.0, post_max_size=50), + dict(class_names=["pedestrian"], class_indices=[4], nms_threshold=0.0, post_max_size=100), + dict(class_names=["traffic_cone"], class_indices=[5], nms_threshold=0.0, post_max_size=100), + dict(class_names=["barrier"], class_indices=[6], nms_threshold=0.0, post_max_size=50), ], ), - dense_heatmap_pooling_classes=["car", "truck", "bus", "bicycle", "barrier"], # Use class indices for pooling + dense_heatmap_pooling_classes=["car", "truck", "bus", "barrier"], # Use class indices for pooling common_heads=dict(center=[2, 2], height=[1, 2], dim=[3, 2], rot=[2, 2], vel=[2, 2]), bbox_coder=dict( type="TransFusionBBoxCoder", post_center_range=[-200.0, -200.0, -10.0, 200.0, 200.0, 10.0], - score_threshold=0.0, + # score_threshold=0.03, + # CAR, TRUCK, BUS, BICYCLE, PEDESTRIAN, TRAFFIC_CONE, BARRIER + score_threshold=[0.015, 0.010, 0.010, 0.020, 0.030, 0.040, 0.020], out_size_factor=8, code_size=10, ), @@ -112,8 +117,10 @@ reduction="mean", loss_weight=1.0, ), - loss_heatmap=dict(type="mmdet.GaussianFocalLoss", reduction="mean", loss_weight=1.0), + loss_iou=None, + loss_heatmap=dict(type="mmdet.GaussianFocalLoss", reduction="none", loss_weight=1.0), loss_bbox=dict(type="mmdet.L1Loss", reduction="mean", loss_weight=0.25), - partial_ignore_labels=None, + # partial_ + partial_ignore_labels=["traffic_cone", "barrier"], ), ) diff --git a/projects/BEVFusion/configs/t4dataset/default/pipelines/default_camera_lidar_intensity_120m.py b/projects/BEVFusion/configs/t4dataset/default/pipelines/default_camera_lidar_intensity_120m.py index 963a218e1..4d9a5aa12 100644 --- a/projects/BEVFusion/configs/t4dataset/default/pipelines/default_camera_lidar_intensity_120m.py +++ b/projects/BEVFusion/configs/t4dataset/default/pipelines/default_camera_lidar_intensity_120m.py @@ -1,6 +1,6 @@ # Dataset parameters backend_args = None -num_workers = 32 +num_workers = 16 input_modality = dict(use_lidar=True, use_camera=True) # range setting @@ -13,6 +13,8 @@ "bus": 120, "bicycle": 120, "pedestrian": 120, + "traffic_cone": 120, + "barrier": 120, } # LiDAR parameters @@ -74,16 +76,15 @@ classes=[ "car", "truck", - "construction_vehicle", "bus", - "trailer", - "barrier", - "motorcycle", "bicycle", "pedestrian", "traffic_cone", + "barrier", ], ), + dict(type="ObjectRangeMinPointsFilter", range_radius=[0, 60], min_num_points=3), + dict(type="ObjectRangeMinPointsFilter", range_radius=[60, 130], min_num_points=2), dict(type="PointShuffle"), dict( type="Pack3DDetInputs", @@ -107,6 +108,9 @@ "img_aug_matrix", "lidar_aug_matrix", "timestamp", + "vehicle_type", + "city", + "traffic_cone_barrier_status", ], ), ] @@ -164,6 +168,9 @@ "num_pts_feats", "num_views", "timestamp", + "vehicle_type", + "city", + "traffic_cone_barrier_status", ], ), ] diff --git a/projects/BEVFusion/configs/t4dataset/default/pipelines/default_lidar_120m.py b/projects/BEVFusion/configs/t4dataset/default/pipelines/default_lidar_120m.py index 09b9f7b26..b37108873 100644 --- a/projects/BEVFusion/configs/t4dataset/default/pipelines/default_lidar_120m.py +++ b/projects/BEVFusion/configs/t4dataset/default/pipelines/default_lidar_120m.py @@ -7,6 +7,9 @@ point_cloud_range = [-122.4, -122.4, -3.0, 122.4, 122.4, 5.0] voxel_size = [0.17, 0.17, 0.2] grid_size = [1440, 1440, 41] +# Sparse dense output shapes +sparse_dense_output_shapes = [180, 180, 2] + eval_class_range = { "car": 120, "truck": 120, @@ -63,6 +66,8 @@ "barrier", ], ), + dict(type="ObjectRangeMinPointsFilter", range_radius=[0, 60], min_num_points=3), + dict(type="ObjectRangeMinPointsFilter", range_radius=[60, 130], min_num_points=2), dict(type="PointShuffle"), dict( type="Pack3DDetInputs", diff --git a/projects/BEVFusion/configs/t4dataset/default/pipelines/default_lidar_intensity_120m.py b/projects/BEVFusion/configs/t4dataset/default/pipelines/default_lidar_intensity_120m.py index e2de195e9..7d6b8e506 100644 --- a/projects/BEVFusion/configs/t4dataset/default/pipelines/default_lidar_intensity_120m.py +++ b/projects/BEVFusion/configs/t4dataset/default/pipelines/default_lidar_intensity_120m.py @@ -1,12 +1,15 @@ # Dataset parameters backend_args = None -num_workers = 32 +num_workers = 16 input_modality = dict(use_lidar=True, use_camera=False) # range setting point_cloud_range = [-122.4, -122.4, -3.0, 122.4, 122.4, 5.0] voxel_size = [0.17, 0.17, 0.2] grid_size = [1440, 1440, 41] +# Sparse dense output shapes +sparse_dense_output_shapes = [180, 180, 2] + eval_class_range = { "car": 120, "truck": 120, @@ -63,6 +66,8 @@ "barrier", ], ), + dict(type="ObjectRangeMinPointsFilter", range_radius=[0, 60], min_num_points=3), + dict(type="ObjectRangeMinPointsFilter", range_radius=[60, 130], min_num_points=2), dict(type="PointShuffle"), dict( type="Pack3DDetInputs", diff --git a/projects/BEVFusion/configs/t4dataset/default/schedulers/default_30e_8xb8_adamw_cosine.py b/projects/BEVFusion/configs/t4dataset/default/schedulers/default_30e_8xb16_adamw_cosine.py similarity index 86% rename from projects/BEVFusion/configs/t4dataset/default/schedulers/default_30e_8xb8_adamw_cosine.py rename to projects/BEVFusion/configs/t4dataset/default/schedulers/default_30e_8xb16_adamw_cosine.py index a2cd2d2e9..cc5c780bd 100644 --- a/projects/BEVFusion/configs/t4dataset/default/schedulers/default_30e_8xb8_adamw_cosine.py +++ b/projects/BEVFusion/configs/t4dataset/default/schedulers/default_30e_8xb16_adamw_cosine.py @@ -1,17 +1,16 @@ # learning rate -# 1e-4 * sqrt(2) = 0.0001414 -lr = 1.4141e-4 -t_max = 8 +lr = 2.0e-4 +t_max = 3 max_epochs = 30 val_interval = 5 train_gpu_size = 8 -test_batch_size = 2 -train_batch_size = 8 +test_batch_size = 4 +train_batch_size = 16 param_scheduler = [ # learning rate scheduler - # During the first (max_epochs * 0.4) epochs, learning rate increases from 0 to lr * 10 + # During the first (max_epochs * 0.10) epochs, learning rate increases from 0 to lr * 5 # during the next epochs, learning rate decreases from lr * 10 to # lr * 1e-4 dict( @@ -33,7 +32,7 @@ convert_to_iter_based=True, ), # momentum scheduler - # During the first (0.4 * max_epochs) epochs, momentum increases from 0 to 0.85 / 0.95 + # During the first (max_epochs * 0.10) epochs, momentum increases from 0 to 0.85 / 0.95 # during the next epochs, momentum increases from 0.85 / 0.95 to 1 dict( type="CosineAnnealingMomentum", diff --git a/projects/BEVFusion/configs/t4dataset/default/schedulers/default_50e_8xb8_adamw_cosine.py b/projects/BEVFusion/configs/t4dataset/default/schedulers/default_50e_8xb16_adamw_cosine.py similarity index 86% rename from projects/BEVFusion/configs/t4dataset/default/schedulers/default_50e_8xb8_adamw_cosine.py rename to projects/BEVFusion/configs/t4dataset/default/schedulers/default_50e_8xb16_adamw_cosine.py index 87571d0b3..cf8b0e4f2 100644 --- a/projects/BEVFusion/configs/t4dataset/default/schedulers/default_50e_8xb8_adamw_cosine.py +++ b/projects/BEVFusion/configs/t4dataset/default/schedulers/default_50e_8xb16_adamw_cosine.py @@ -1,17 +1,16 @@ # learning rate -# 1e-4 * sqrt(2) = 0.0001414 -lr = 1.4141e-4 -t_max = 15 +lr = 2.0e-4 +t_max = 3 max_epochs = 50 val_interval = 5 train_gpu_size = 8 -test_batch_size = 2 -train_batch_size = 8 +test_batch_size = 4 +train_batch_size = 16 param_scheduler = [ # learning rate scheduler - # During the first (max_epochs * 0.4) epochs, learning rate increases from 0 to lr * 10 + # During the first (max_epochs * 0.10) epochs, learning rate increases from 0 to lr * 10 # during the next epochs, learning rate decreases from lr * 10 to # lr * 1e-4 dict( @@ -33,7 +32,7 @@ convert_to_iter_based=True, ), # momentum scheduler - # During the first (0.4 * max_epochs) epochs, momentum increases from 0 to 0.85 / 0.95 + # During the first (0.10 * max_epochs) epochs, momentum increases from 0 to 0.85 / 0.95 # during the next epochs, momentum increases from 0.85 / 0.95 to 1 dict( type="CosineAnnealingMomentum", diff --git a/projects/BEVFusion/deploy/containers.py b/projects/BEVFusion/deploy/containers.py index 51f2316df..bab40d87f 100644 --- a/projects/BEVFusion/deploy/containers.py +++ b/projects/BEVFusion/deploy/containers.py @@ -1,8 +1,8 @@ +# Wrapper Classes for onnx conversion +import numpy as np import torch import torch.nn.functional as F -# Wrapper Classes for onnx conversion - class TrtBevFusionImageBackboneContainer(torch.nn.Module): def __init__(self, mod, mean, std) -> None: @@ -46,7 +46,6 @@ def forward( coors = coors.flip(dims=[-1]).contiguous() # [x, y, z] batch_coors = torch.zeros(num_points, 1).to(coors.device) coors = torch.cat([batch_coors, coors], dim=1).contiguous() - batch_inputs_dict = { "voxels": {"voxels": voxels, "coors": coors, "num_points_per_voxel": num_points_per_voxel}, } diff --git a/projects/BEVFusion/deploy/exporter.py b/projects/BEVFusion/deploy/exporter.py index b1a430eb9..c7cd9e6b0 100644 --- a/projects/BEVFusion/deploy/exporter.py +++ b/projects/BEVFusion/deploy/exporter.py @@ -2,7 +2,7 @@ import logging import os.path as osp -from typing import Optional +from typing import Any, Optional import numpy as np import onnx @@ -11,12 +11,32 @@ from builder import ExportBuilder from containers import TrtBevFusionCameraOnlyContainer, TrtBevFusionImageBackboneContainer, TrtBevFusionMainContainer from data_classes import ModelData, SetupConfigs -from mmdeploy.core import RewriterContext +from mmdeploy.core import SYMBOLIC_REWRITER, RewriterContext from mmdeploy.utils import ( get_root_logger, ) +def purge_mmdeploy_symbolics(op_names: list[str]) -> dict: + """Delete mmdeploy's symbolic records for the given op names. + Both the op-name key (e.g. `"layer_norm"`) and the function-path + bookkeeping key (e.g. `"mmdeploy.pytorch.symbolics.layer_norm.layer_norm__default"`) + are removed. Returns a snapshot of what was deleted for optional restore. + """ + records = SYMBOLIC_REWRITER._registry._rewrite_records + removed: dict = {} + for key in list(records.keys()): + # Primary key: the aten op name itself. + if key in op_names: + removed[key] = records.pop(key) + continue + # Bookkeeping key: full Python path of an implementer function. + # Match by "...symbolics.." or "...symbolics.__" + if any(f".symbolics.{op}." in key or f".symbolics.{op}__" in key for op in op_names): + removed[key] = records.pop(key) + return removed + + class Torch2OnnxExporter: def __init__(self, setup_configs: SetupConfigs, log_level: str): @@ -62,6 +82,10 @@ def _export_model( patched_model (torch.nn.Module): Patched Pytorch model. ir_configs (dict): Configs for intermediate representations in ONNX. """ + # Purge the mmdeploy symbolic records for the layer_norm op, remove this if LayerNorm OP is not supported + # in the tensorrt version + removed = purge_mmdeploy_symbolics(["layer_norm"]) + self.logger.info(f"Purged {len(removed)} mmdeploy symbolic records: {list(removed.keys())}") with RewriterContext(**context_info), torch.no_grad(): image_feats = None if "img_backbone" in self.setup_configs.model_cfg.model: diff --git a/projects/BEVFusion/deploy/utils.py b/projects/BEVFusion/deploy/utils.py index fde859523..d3716d02d 100644 --- a/projects/BEVFusion/deploy/utils.py +++ b/projects/BEVFusion/deploy/utils.py @@ -49,7 +49,7 @@ def setup_configs( # TODO(KokSeang): Remove this # Default voxelize_layer voxelize_cfg = dict( - max_num_points=10, + max_num_points=32, voxel_size=[0.17, 0.17, 0.2], point_cloud_range=[-122.4, -122.4, -3.0, 122.4, 122.4, 5.0], max_voxels=[120000, 160000], diff --git a/projects/BEVFusion/deploy/voxel_detection.py b/projects/BEVFusion/deploy/voxel_detection.py index d9c5b3aa1..8b4b740a9 100644 --- a/projects/BEVFusion/deploy/voxel_detection.py +++ b/projects/BEVFusion/deploy/voxel_detection.py @@ -128,7 +128,6 @@ def create_input( camera_mask, img, lidar2image, cam2image, camera2lidar, geom_feats, kept, ranks, indices = ( self.extract_img_inputs(batch=batch, model=model, collate_data=collate_data) ) - inputs = [ feats, coors, diff --git a/projects/BEVFusion/docs/BEVFusion-L/v2/base.md b/projects/BEVFusion/docs/BEVFusion-L/v2/base.md index ecdd1e9a8..c814f93a6 100644 --- a/projects/BEVFusion/docs/BEVFusion-L/v2/base.md +++ b/projects/BEVFusion/docs/BEVFusion-L/v2/base.md @@ -9,10 +9,10 @@ ### Testing Datasets -- **Total Frames: 15,154** +- **Total Frames: 16,597**
- j6gen2 (3,951 frames) + j6gen2 (4,682 frames) - `db_j6gen2_v1` - `db_j6gen2_v2` @@ -23,6 +23,9 @@ - `db_j6gen2_v7` - `db_j6gen2_v8` - `db_j6gen2_v9` + - `db_j6gen2_v10` + - `db_j6gen2_v11` + - `db_j6gen2_v12`
@@ -36,16 +39,183 @@
- jpntaxi_gen2 (9,975 frames) + jpntaxi_gen2 (10,687 frames) - `db_jpntaxigen2_v1` - `db_jpntaxigen2_v2`
+### mAP - Base +- Note that the metrics reported in `traffic_cone/barrier` might not be accurate since some of the evaluation dataset doesn't have annotations for the two classes. + +- **Class mAP for BEV Center Distance: 0.5m, 1.0m, 2.0m, 4.0m** + +
+ Eval Range: 0.0 - 50.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(126,168) | truck
(26,897) | bus
(6,559) | bicycle
(5,865) | pedestrian
(93,520) | traffic_cone
(20,835) | barrier
(3,359) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.6763 | 0.6381 | 0.6507 | 0.6062 | 0.6316 | 0.5871 | 0.9065 | 0.8566 | 0.8705 | 0.8157 | 0.8913 | 0.3417 | 0.0521 | + +
+ +
+ Eval Range: 50.0 - 90.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(105,914) | truck
(28,864) | bus
(5,290) | bicycle
(3,608) | pedestrian
(48,637) | traffic_cone
(9,819) | barrier
(2,469) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.5447 | 0.5067 | 0.5590 | 0.5236 | 0.5400 | 0.5046 | 0.8132 | 0.6652 | 0.6404 | 0.6241 | 0.7502 | 0.3184 | 0.0017 | + +
+ +
+ Eval Range: 90.0 - 121.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(39,577) | truck
(18,213) | bus
(3,541) | bicycle
(942) | pedestrian
(20,134) | traffic_cone
(1,231) | barrier
(711) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.3779 | 0.3496 | 0.4428 | 0.3903 | 0.4287 | 0.3762 | 0.6979 | 0.5143 | 0.3860 | 0.3610 | 0.6588 | 0.0272 | 0.0002 | + +
+ +
+ Eval Range: 0.0 - 121.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(271,659) | truck
(73,974) | bus
(15,390) | bicycle
(10,415) | pedestrian
(162,291) | traffic_cone
(31,885) | barrier
(6,539) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.5936 | 0.5554 | 0.6017 | 0.5555 | 0.5826 | 0.5365 | 0.8534 | 0.7110 | 0.6992 | 0.7185 | 0.8315 | 0.3204 | 0.0209 | + +
+ +### Mean TPError - Base +- Recalls: `0.10`, `0.40`, `optimal` + +
+ Eval Range: 0.0 - 50.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.1796 | 0.1993 | 0.2024 | 0.2937 | 1.0000 | 0.2857 | 0.2916 | 0.2957 | 0.4466 | 1.0000 | 0.2149 | 0.2196 | 0.2175 | 0.3260 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 126,168) | truck
0.5/1.0/2.0/4.0
(GTs: 26,897) | bus
0.5/1.0/2.0/4.0
(GTs: 6,559) | bicycle
0.5/1.0/2.0/4.0
(GTs: 5,865) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 93,520) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,835) | barrier
0.5/1.0/2.0/4.0
(GTs: 3,359) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 13,878 / 13,878 / 13,878 / 13,878 | 2,958 / 2,958 / 2,958 / 2,958 | 721 / 721 / 721 / 721 | 645 / 645 / 645 / 645 | 10,287 / 10,287 / 10,287 / 10,287 | 2,291 / 2,291 / 2,291 / 2,291 | 369 / 369 / 369 / 369 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 126,168) | truck
0.5/1.0/2.0/4.0
(GTs: 26,897) | bus
0.5/1.0/2.0/4.0
(GTs: 6,559) | bicycle
0.5/1.0/2.0/4.0
(GTs: 5,865) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 93,520) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,835) | barrier
0.5/1.0/2.0/4.0
(GTs: 3,359) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 51,728 / 51,728 / 51,728 / 51,728 | 11,027 / 11,027 / 11,027 / 11,027 | 2,689 / 2,689 / 2,689 / 2,689 | 2,404 / 2,404 / 2,404 / 2,404 | 38,343 / 38,343 / 38,343 / 38,343 | 8,542 / 8,542 / 8,542 / 8,542 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 126,168) | truck
0.5/1.0/2.0/4.0
(GTs: 26,897) | bus
0.5/1.0/2.0/4.0
(GTs: 6,559) | bicycle
0.5/1.0/2.0/4.0
(GTs: 5,865) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 93,520) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,835) | barrier
0.5/1.0/2.0/4.0
(GTs: 3,359) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 109,035 / 113,613 / 115,502 / 116,446 | 20,578 / 23,058 / 24,234 / 24,631 | 5,398 / 5,809 / 5,952 / 5,967 | 4,573 / 4,739 / 4,611 / 4,616 | 78,245 / 79,717 / 80,219 / 80,761 | 10,168 / 11,012 / 11,411 / 11,947 | 452 / 629 / 667 / 715 | + +
+ +
+ + Eval Range: 50.0 - 90.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.2878 | 0.2286 | 0.2487 | 0.3686 | 1.0000 | 0.3596 | 0.3138 | 0.3151 | 0.4991 | 1.0000 | 0.2897 | 0.2224 | 0.2274 | 0.3779 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 105,914) | truck
0.5/1.0/2.0/4.0
(GTs: 28,864) | bus
0.5/1.0/2.0/4.0
(GTs: 5,290) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,608) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 48,637) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 9,819) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,469) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 11,650 / 11,650 / 11,650 / 11,650 | 3,175 / 3,175 / 3,175 / 3,175 | 581 / 581 / 581 / 581 | 396 / 396 / 396 / 396 | 5,350 / 5,350 / 5,350 / 5,350 | 1,080 / 1,080 / 1,080 / 1,080 | 0 / 271 / 271 / 271 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 105,914) | truck
0.5/1.0/2.0/4.0
(GTs: 28,864) | bus
0.5/1.0/2.0/4.0
(GTs: 5,290) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,608) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 48,637) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 9,819) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,469) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 43,424 / 43,424 / 43,424 / 43,424 | 11,834 / 11,834 / 11,834 / 11,834 | 2,168 / 2,168 / 2,168 / 2,168 | 1,479 / 1,479 / 1,479 / 1,479 | 19,941 / 19,941 / 19,941 / 19,941 | 4,025 / 4,025 / 4,025 / 4,025 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 105,914) | truck
0.5/1.0/2.0/4.0
(GTs: 28,864) | bus
0.5/1.0/2.0/4.0
(GTs: 5,290) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,608) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 48,637) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 9,819) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,469) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 76,795 / 84,883 / 88,518 / 89,420 | 15,896 / 19,085 / 21,454 / 22,235 | 2,428 / 3,407 / 3,812 / 3,890 | 2,186 / 2,306 / 2,311 / 2,327 | 34,885 / 35,834 / 35,898 / 36,174 | 4,370 / 4,522 / 4,871 / 4,902 | 140 / 222 / 238 / 244 | + +
+
- base (15,154 frames) + Eval Range: 90.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.3878 | 0.2914 | 0.3071 | 0.4752 | 1.0000 | 0.5015 | 0.3966 | 0.4158 | 0.6724 | 1.0000 | 0.3567 | 0.2630 | 0.2646 | 0.4419 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 39,577) | truck
0.5/1.0/2.0/4.0
(GTs: 18,213) | bus
0.5/1.0/2.0/4.0
(GTs: 3,541) | bicycle
0.5/1.0/2.0/4.0
(GTs: 942) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 20,134) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 1,231) | barrier
0.5/1.0/2.0/4.0
(GTs: 711) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 4,353 / 4,353 / 4,353 / 4,353 | 2,003 / 2,003 / 2,003 / 2,003 | 389 / 389 / 389 / 389 | 103 / 103 / 103 / 103 | 2,214 / 2,214 / 2,214 / 2,214 | 135 / 135 / 135 / 135 | 0 / 0 / 78 / 78 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 39,577) | truck
0.5/1.0/2.0/4.0
(GTs: 18,213) | bus
0.5/1.0/2.0/4.0
(GTs: 3,541) | bicycle
0.5/1.0/2.0/4.0
(GTs: 942) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 20,134) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 1,231) | barrier
0.5/1.0/2.0/4.0
(GTs: 711) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 16,226 / 16,226 / 16,226 / 16,226 | 7,467 / 7,467 / 7,467 / 7,467 | 1,451 / 1,451 / 1,451 / 1,451 | 386 / 386 / 386 / 386 | 8,254 / 8,254 / 8,254 / 8,254 | 0 / 0 / 0 / 504 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 39,577) | truck
0.5/1.0/2.0/4.0
(GTs: 18,213) | bus
0.5/1.0/2.0/4.0
(GTs: 3,541) | bicycle
0.5/1.0/2.0/4.0
(GTs: 942) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 20,134) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 1,231) | barrier
0.5/1.0/2.0/4.0
(GTs: 711) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 23,637 / 27,796 / 30,068 / 31,051 | 6,996 / 9,963 / 12,146 / 13,138 | 1,277 / 1,704 / 1,857 / 1,963 | 379 / 421 / 434 / 439 | 13,121 / 13,270 / 13,350 / 13,493 | 247 / 251 / 262 / 280 | 28 / 54 / 67 / 70 | + +
+ +
+ + Eval Range: 0.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.2162 | 0.2040 | 0.2147 | 0.3160 | 1.0000 | 0.3255 | 0.3034 | 0.3079 | 0.4756 | 1.0000 | 0.2567 | 0.2269 | 0.2265 | 0.3571 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 271,659) | truck
0.5/1.0/2.0/4.0
(GTs: 73,974) | bus
0.5/1.0/2.0/4.0
(GTs: 15,390) | bicycle
0.5/1.0/2.0/4.0
(GTs: 10,415) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 162,291) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 31,885) | barrier
0.5/1.0/2.0/4.0
(GTs: 6,539) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 29,883 / 29,882 / 29,882 / 29,882 | 8,137 / 8,137 / 8,137 / 8,137 | 1,692 / 1,692 / 1,692 / 1,692 | 1,145 / 1,145 / 1,145 / 1,145 | 17,852 / 17,852 / 17,852 / 17,852 | 3,507 / 3,507 / 3,507 / 3,507 | 719 / 719 / 719 / 719 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 271,659) | truck
0.5/1.0/2.0/4.0
(GTs: 73,974) | bus
0.5/1.0/2.0/4.0
(GTs: 15,390) | bicycle
0.5/1.0/2.0/4.0
(GTs: 10,415) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 162,291) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 31,885) | barrier
0.5/1.0/2.0/4.0
(GTs: 6,539) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 111,380 / 111,380 / 111,380 / 111,380 | 30,329 / 30,329 / 30,329 / 30,329 | 6,309 / 6,309 / 6,309 / 6,309 | 4,270 / 4,270 / 4,270 / 4,270 | 66,539 / 66,539 / 66,539 / 66,539 | 13,072 / 13,072 / 13,072 / 13,072 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 271,659) | truck
0.5/1.0/2.0/4.0
(GTs: 73,974) | bus
0.5/1.0/2.0/4.0
(GTs: 15,390) | bicycle
0.5/1.0/2.0/4.0
(GTs: 10,415) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 162,291) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 31,885) | barrier
0.5/1.0/2.0/4.0
(GTs: 6,539) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 209,064 / 225,972 / 233,684 / 235,583 | 43,131 / 52,358 / 57,620 / 60,504 | 9,224 / 10,854 / 11,562 / 11,707 | 7,044 / 7,224 / 7,242 / 7,260 | 125,762 / 128,271 / 129,072 / 130,130 | 14,442 / 15,921 / 16,500 / 17,198 | 624 / 886 / 950 / 1,028 | + +
+ + +## Datasets + +
+ J6Gen2 + +- Datasets (4,682 Testing Frames): - `db_j6gen2_v1` - `db_j6gen2_v2` - `db_j6gen2_v3` @@ -55,109 +225,165 @@ - `db_j6gen2_v7` - `db_j6gen2_v8` - `db_j6gen2_v9` - - `db_largebus_v1` - - `db_largebus_v2` - - `db_largebus_v3` - - `db_jpntaxigen2_v1` - - `db_jpntaxigen2_v2` - -
- -### mAP - Base + - `db_j6gen2_v10` + - `db_j6gen2_v11` + - `db_j6gen2_v12` - **Class mAP for BEV Center Distance: 0.5m, 1.0m, 2.0m, 4.0m**
Eval Range: 0.0 - 50.0m - | Model version | mAP | mAPH | car
(107,309) | truck
(24,206) | bus
(5,712) | bicycle
(4,060) | pedestrian
(77,369) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.8817 | 0.8496 | 0.9131 | 0.8552 | 0.9081 | 0.8357 | 0.8966 | - | BEVFusion-LiDAR base/2.6.0 | 0.8774 | 0.8443 | 0.9049 | 0.8514 | 0.8824 | 0.8543 | 0.8941 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(60,938) | truck
(7,081) | bus
(2,370) | bicycle
(1,357) | pedestrian
(18,202) | traffic_cone
(8,250) | barrier
(1,350) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.7246 | 0.6765 | 0.6874 | 0.6712 | 0.6633 | 0.6471 | 0.8849 | 0.8325 | 0.9034 | 0.9004 | 0.8381 | 0.4459 | 0.2671 | -
+
Eval Range: 50.0 - 90.0m - | Model version | mAP | mAPH | car
(94,080) | truck
(27,651) | bus
(4,761) | bicycle
(2,365) | pedestrian
(37,523) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.7002 | 0.6621 | 0.8174 | 0.6660 | 0.6414 | 0.6430 | 0.7331 | - | BEVFusion-LiDAR base/2.6.0 | 0.6824 | 0.6437 | 0.8005 | 0.6567 | 0.5783 | 0.6322 | 0.7445 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(54,217) | truck
(4,913) | bus
(2,116) | bicycle
(838) | pedestrian
(8,336) | traffic_cone
(2,632) | barrier
(622) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.5590 | 0.5053 | 0.5849 | 0.5656 | 0.5581 | 0.5387 | 0.7864 | 0.6212 | 0.7611 | 0.6674 | 0.6253 | 0.2711 | 0.1807 |
Eval Range: 90.0 - 121.0m - | Model version | mAP | mAPH | car
(36,895) | truck
(17,759) | bus
(2,852) | bicycle
(519) | pedestrian
(17,091) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.5600 | 0.5254 | 0.6578 | 0.5131 | 0.5178 | 0.4296 | 0.6815 | - | BEVFusion-LiDAR base/2.6.0 | 0.5136 | 0.4788 | 0.6552 | 0.5023 | 0.2849 | 0.4369 | 0.6887 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(19,301) | truck
(2,906) | bus
(484) | bicycle
(291) | pedestrian
(2,564) | traffic_cone
(462) | barrier
(145) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.4021 | 0.3638 | 0.4870 | 0.4675 | 0.4679 | 0.4484 | 0.6848 | 0.4894 | 0.4972 | 0.4913 | 0.4232 | 0.1266 | 0.1024 |
Eval Range: 0.0 - 121.0m - | Model version | mAP | mAPH | car
(238,284) | truck
(69,616) | bus
(13,325) | bicycle
(6,944) | pedestrian
(131,983) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.7777 | 0.7420 | 0.8504 | 0.7065 | 0.7443 | 0.7538 | 0.8332 | - | BEVFusion-LiDAR base/2.6.0 | 0.7592 | 0.7227 | 0.8398 | 0.6994 | 0.6621 | 0.7595 | 0.8351 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(134,456) | truck
(14,900) | bus
(4,970) | bicycle
(2,486) | pedestrian
(29,102) | traffic_cone
(11,344) | barrier
(2,117) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.6463 | 0.5953 | 0.6403 | 0.6221 | 0.6148 | 0.5966 | 0.8310 | 0.7078 | 0.8174 | 0.7884 | 0.7558 | 0.3971 | 0.2263 |
-## Datasets +- **Mean TPError** -
- JPNTaxi Gen2 +
+ Eval Range: 0.0 - 50.0m -- Datasets (9,975 Testing Frames): - - `db_jpntaxigen2_v1` - - `db_jpntaxigen2_v2` + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.1731 | 0.1809 | 0.1966 | 0.1987 | 1.0000 | 0.2178 | 0.2153 | 0.2319 | 0.2464 | 1.0000 | 0.2080 | 0.2074 | 0.2153 | 0.2185 | 1.0000 | -- **Class mAP for BEV Center Distance: 0.5m, 1.0m, 2.0m, 4.0m** + Num match summary -
- Eval Range: 0.0 - 50.0m + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 60,938) | truck
0.5/1.0/2.0/4.0
(GTs: 7,081) | bus
0.5/1.0/2.0/4.0
(GTs: 2,370) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,357) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 18,202) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,250) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 6,703 / 6,703 / 6,703 / 6,703 | 778 / 778 / 778 / 778 | 261 / 261 / 260 / 260 | 149 / 149 / 149 / 149 | 2,002 / 2,002 / 2,002 / 2,002 | 907 / 907 / 907 / 907 | 148 / 148 / 148 / 148 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 60,938) | truck
0.5/1.0/2.0/4.0
(GTs: 7,081) | bus
0.5/1.0/2.0/4.0
(GTs: 2,370) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,357) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 18,202) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,250) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 24,984 / 24,984 / 24,984 / 24,984 | 2,903 / 2,903 / 2,903 / 2,903 | 971 / 971 / 971 / 971 | 556 / 556 / 556 / 556 | 7,462 / 7,462 / 7,462 / 7,462 | 3,382 / 3,382 / 3,382 / 3,382 | 0 / 553 / 553 / 553 | - | Model version | mAP | mAPH | car
(42,789) | truck
(17,259) | bus
(3,437) | bicycle
(2,681) | pedestrian
(57,948) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.8837 | 0.8562 | 0.9393 | 0.8587 | 0.8802 | 0.8268 | 0.9135 | - | BEVFusion-LiDAR base/2.6.0 | 0.8784 | 0.8487 | 0.9436 | 0.8531 | 0.8284 | 0.8546 | 0.9123 | + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 60,938) | truck
0.5/1.0/2.0/4.0
(GTs: 7,081) | bus
0.5/1.0/2.0/4.0
(GTs: 2,370) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,357) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 18,202) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,250) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 51,545 / 53,474 / 54,439 / 55,024 | 5,252 / 5,935 / 6,142 / 6,261 | 1,963 / 2,101 / 2,217 / 2,227 | 1,141 / 1,146 / 1,146 / 1,146 | 14,108 / 14,410 / 14,530 / 14,796 | 4,463 / 4,943 / 5,097 / 5,196 | 452 / 587 / 622 / 638 |
Eval Range: 50.0 - 90.0m - | Model version | mAP | mAPH | car
(35,518) | truck
(22,550) | bus
(2,683) | bicycle
(1,607) | pedestrian
(27,240) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.6901 | 0.6630 | 0.8382 | 0.6676 | 0.5007 | 0.6794 | 0.7645 | - | BEVFusion-LiDAR base/2.6.0 | 0.6692 | 0.6414 | 0.8323 | 0.6571 | 0.4033 | 0.6721 | 0.7812 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.2509 | 0.2223 | 0.2170 | 0.2557 | 1.0000 | 0.3081 | 0.2757 | 0.2529 | 0.3025 | 1.0000 | 0.2876 | 0.2507 | 0.2251 | 0.2699 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 54,217) | truck
0.5/1.0/2.0/4.0
(GTs: 4,913) | bus
0.5/1.0/2.0/4.0
(GTs: 2,116) | bicycle
0.5/1.0/2.0/4.0
(GTs: 838) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 8,336) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,632) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 5,963 / 5,963 / 5,963 / 5,963 | 540 / 540 / 540 / 540 | 232 / 232 / 232 / 232 | 92 / 92 / 92 / 92 | 916 / 916 / 916 / 916 | 289 / 289 / 289 / 289 | 68 / 68 / 68 / 68 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 54,217) | truck
0.5/1.0/2.0/4.0
(GTs: 4,913) | bus
0.5/1.0/2.0/4.0
(GTs: 2,116) | bicycle
0.5/1.0/2.0/4.0
(GTs: 838) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 8,336) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,632) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 22,228 / 22,228 / 22,228 / 22,228 | 2,014 / 2,014 / 2,014 / 2,014 | 867 / 867 / 867 / 867 | 343 / 343 / 343 / 343 | 3,417 / 3,417 / 3,417 / 3,417 | 1,079 / 1,079 / 1,079 / 1,079 | 0 / 255 / 255 / 255 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 54,217) | truck
0.5/1.0/2.0/4.0
(GTs: 4,913) | bus
0.5/1.0/2.0/4.0
(GTs: 2,116) | bicycle
0.5/1.0/2.0/4.0
(GTs: 838) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 8,336) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,632) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 37,866 / 42,472 / 44,630 / 45,417 | 2,598 / 3,145 / 3,407 / 3,593 | 1,175 / 1,604 / 1,740 / 1,815 | 524 / 551 / 552 / 557 | 5,196 / 5,378 / 5,448 / 5,510 | 1,014 / 1,077 / 1,237 / 1,290 | 136 / 209 / 227 / 233 |
Eval Range: 90.0 - 121.0m - | Model version | mAP | mAPH | car
(16,524) | truck
(14,587) | bus
(2,476) | bicycle
(364) | pedestrian
(14,297) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.5750 | 0.5466 | 0.6601 | 0.5131 | 0.5145 | 0.4541 | 0.7331 | - | BEVFusion-LiDAR base/2.6.0 | 0.5300 | 0.5010 | 0.6692 | 0.5020 | 0.2822 | 0.4586 | 0.7380 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.3294 | 0.2250 | 0.2534 | 0.3325 | 1.0000 | 0.3858 | 0.2797 | 0.2836 | 0.3859 | 1.0000 | 0.3505 | 0.2499 | 0.2570 | 0.3449 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 19,301) | truck
0.5/1.0/2.0/4.0
(GTs: 2,906) | bus
0.5/1.0/2.0/4.0
(GTs: 484) | bicycle
0.5/1.0/2.0/4.0
(GTs: 291) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 2,564) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 2,123 / 2,123 / 2,123 / 2,123 | 319 / 319 / 319 / 319 | 53 / 53 / 53 / 53 | 32 / 32 / 32 / 32 | 282 / 282 / 282 / 282 | 50 / 50 / 50 / 50 | 15 / 15 / 15 / 15 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 19,301) | truck
0.5/1.0/2.0/4.0
(GTs: 2,906) | bus
0.5/1.0/2.0/4.0
(GTs: 484) | bicycle
0.5/1.0/2.0/4.0
(GTs: 291) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 2,564) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 7,913 / 7,913 / 7,913 / 7,913 | 1,191 / 1,191 / 1,191 / 1,191 | 198 / 198 / 198 / 198 | 119 / 119 / 119 / 119 | 1,051 / 1,051 / 1,051 / 1,051 | 189 / 189 / 189 / 189 | 0 / 59 / 59 / 59 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 19,301) | truck
0.5/1.0/2.0/4.0
(GTs: 2,906) | bus
0.5/1.0/2.0/4.0
(GTs: 484) | bicycle
0.5/1.0/2.0/4.0
(GTs: 291) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 2,564) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 11,411 / 13,677 / 14,775 / 15,007 | 1,065 / 1,541 / 1,834 / 2,047 | 164 / 233 / 291 / 296 | 147 / 164 / 164 / 164 | 1,358 / 1,329 / 1,371 / 1,342 | 135 / 135 / 133 / 148 | 26 / 40 / 50 / 53 |
-
+
Eval Range: 0.0 - 121.0m - | Model version | mAP | mAPH | car
(94,831) | truck
(54,396) | bus
(8,596) | bicycle
(4,652) | pedestrian
(99,485) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.7715 | 0.7432 | 0.8661 | 0.7010 | 0.6721 | 0.7611 | 0.8573 | - | BEVFusion-LiDAR base/2.6.0 | 0.7471 | 0.7176 | 0.8667 | 0.6928 | 0.5446 | 0.7710 | 0.8606 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.2028 | 0.1964 | 0.2072 | 0.2220 | 1.0000 | 0.2571 | 0.2377 | 0.2438 | 0.2713 | 1.0000 | 0.2468 | 0.2298 | 0.2238 | 0.2435 | 1.0000 | -
+ Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 134,456) | truck
0.5/1.0/2.0/4.0
(GTs: 14,900) | bus
0.5/1.0/2.0/4.0
(GTs: 4,970) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,486) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 29,102) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,344) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 14,790 / 14,790 / 14,790 / 14,790 | 1,639 / 1,639 / 1,639 / 1,639 | 546 / 546 / 546 / 546 | 273 / 273 / 273 / 273 | 3,201 / 3,201 / 3,201 / 3,201 | 1,247 / 1,247 / 1,247 / 1,247 | 232 / 232 / 232 / 232 | + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 134,456) | truck
0.5/1.0/2.0/4.0
(GTs: 14,900) | bus
0.5/1.0/2.0/4.0
(GTs: 4,970) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,486) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 29,102) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,344) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 55,126 / 55,126 / 55,126 / 55,126 | 6,109 / 6,109 / 6,109 / 6,109 | 2,037 / 2,037 / 2,037 / 2,037 | 1,019 / 1,019 / 1,019 / 1,019 | 11,931 / 11,931 / 11,931 / 11,931 | 4,651 / 4,651 / 4,651 / 4,651 | 0 / 867 / 867 / 867 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 134,456) | truck
0.5/1.0/2.0/4.0
(GTs: 14,900) | bus
0.5/1.0/2.0/4.0
(GTs: 4,970) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,486) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 29,102) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,344) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 100,294 / 109,159 / 113,989 / 115,141 | 8,931 / 10,558 / 11,357 / 11,896 | 3,256 / 3,944 / 4,259 / 4,307 | 1,785 / 1,879 / 1,880 / 1,887 | 20,949 / 21,293 / 21,452 / 21,637 | 5,511 / 6,135 / 6,340 / 6,540 | 605 / 817 / 935 / 960 | + +
@@ -173,98 +399,317 @@
Eval Range: 0.0 - 50.0m - | Model version | mAP | mAPH | car
(14,883) | truck
(1,193) | bus
(336) | bicycle
(740) | pedestrian
(5,059) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.8876 | 0.8447 | 0.9176 | 0.8727 | 0.9443 | 0.8396 | 0.8639 | - | BEVFusion-LiDAR base/2.6.0 | 0.8882 | 0.8475 | 0.9045 | 0.8793 | 0.9482 | 0.8489 | 0.8598 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(14,872) | truck
(1,192) | bus
(336) | bicycle
(740) | pedestrian
(5,055) | traffic_cone
(60) | barrier
(0) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.6292 | 0.5987 | 0.5796 | 0.5491 | 0.5644 | 0.5339 | 0.9088 | 0.8625 | 0.9253 | 0.8660 | 0.8414 | 0.0000 | 0.0000 | + +
+ +
+ Eval Range: 50.0 - 90.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(10,929) | truck
(1,009) | bus
(141) | bicycle
(460) | pedestrian
(3,721) | traffic_cone
(4) | barrier
(0) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.5080 | 0.4699 | 0.4842 | 0.4820 | 0.4652 | 0.4630 | 0.8284 | 0.6953 | 0.8101 | 0.5551 | 0.6672 | 0.0000 | 0.0000 | + +
+ +
+ Eval Range: 90.0 - 121.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(2,883) | truck
(600) | bus
(60) | bicycle
(85) | pedestrian
(1,092) | traffic_cone
(0) | barrier
(0) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.3869 | 0.3535 | 0.4036 | 0.3922 | 0.3870 | 0.3755 | 0.7338 | 0.6045 | 0.5314 | 0.3490 | 0.4896 | 0.0000 | 0.0000 | + +
+ +
+ Eval Range: 0.0 - 121.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(28,684) | truck
(2,801) | bus
(537) | bicycle
(1,285) | pedestrian
(9,868) | traffic_cone
(64) | barrier
(0) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.5663 | 0.5318 | 0.5398 | 0.5103 | 0.5226 | 0.4931 | 0.8718 | 0.7543 | 0.8572 | 0.7306 | 0.7502 | 0.0000 | 0.0000 | + +
+ +- **Mean TPError** + +
+ Eval Range: 0.0 - 50.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.2738 | 0.4304 | 0.3040 | 0.3416 | 1.0000 | 0.3927 | 0.3902 | 0.3987 | 0.4730 | 1.0000 | 0.1903 | 0.3709 | 0.2019 | 0.2298 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 14,872) | truck
0.5/1.0/2.0/4.0
(GTs: 1,192) | bus
0.5/1.0/2.0/4.0
(GTs: 336) | bicycle
0.5/1.0/2.0/4.0
(GTs: 740) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 5,055) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 60) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 1,635 / 1,635 / 1,635 / 1,635 | 131 / 131 / 131 / 131 | 36 / 36 / 36 / 36 | 81 / 81 / 81 / 81 | 556 / 556 / 556 / 556 | 6 / 6 / 6 / 6 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 14,872) | truck
0.5/1.0/2.0/4.0
(GTs: 1,192) | bus
0.5/1.0/2.0/4.0
(GTs: 336) | bicycle
0.5/1.0/2.0/4.0
(GTs: 740) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 5,055) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 60) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 6,097 / 6,097 / 6,097 / 6,097 | 488 / 488 / 488 / 488 | 137 / 137 / 137 / 137 | 303 / 303 / 303 / 303 | 2,072 / 2,072 / 2,072 / 2,072 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 14,872) | truck
0.5/1.0/2.0/4.0
(GTs: 1,192) | bus
0.5/1.0/2.0/4.0
(GTs: 336) | bicycle
0.5/1.0/2.0/4.0
(GTs: 740) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 5,055) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 60) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 13,062 / 13,479 / 13,554 / 13,743 | 932 / 1,043 / 1,066 / 1,074 | 275 / 321 / 324 / 324 | 602 / 607 / 608 / 613 | 4,140 / 4,178 / 4,198 / 4,214 | 23 / 13 / 13 / 13 | 0 / 0 / 0 / 0 |
Eval Range: 50.0 - 90.0m - | Model version | mAP | mAPH | car
(10,994) | truck
(1,011) | bus
(143) | bicycle
(463) | pedestrian
(3,754) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.7392 | 0.6842 | 0.8425 | 0.7288 | 0.8580 | 0.5826 | 0.6839 | - | BEVFusion-LiDAR base/2.6.0 | 0.7132 | 0.6586 | 0.8237 | 0.7245 | 0.7811 | 0.5497 | 0.6871 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.3215 | 0.6553 | 0.3435 | 0.3779 | 1.0000 | 0.3296 | 0.6554 | 0.3494 | 0.3854 | 1.0000 | 0.2287 | 0.6009 | 0.2438 | 0.2891 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 10,929) | truck
0.5/1.0/2.0/4.0
(GTs: 1,009) | bus
0.5/1.0/2.0/4.0
(GTs: 141) | bicycle
0.5/1.0/2.0/4.0
(GTs: 460) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,721) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 4) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 1,202 / 1,202 / 1,202 / 1,202 | 110 / 110 / 110 / 110 | 15 / 15 / 15 / 15 | 50 / 50 / 50 / 50 | 409 / 409 / 409 / 409 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 10,929) | truck
0.5/1.0/2.0/4.0
(GTs: 1,009) | bus
0.5/1.0/2.0/4.0
(GTs: 141) | bicycle
0.5/1.0/2.0/4.0
(GTs: 460) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,721) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 4) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 4,480 / 4,480 / 4,480 / 4,480 | 413 / 413 / 413 / 413 | 57 / 57 / 57 / 57 | 188 / 188 / 188 / 188 | 1,525 / 1,525 / 1,525 / 1,525 | 1 / 1 / 1 / 1 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 10,929) | truck
0.5/1.0/2.0/4.0
(GTs: 1,009) | bus
0.5/1.0/2.0/4.0
(GTs: 141) | bicycle
0.5/1.0/2.0/4.0
(GTs: 460) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,721) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 4) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 8,159 / 8,918 / 9,145 / 9,193 | 584 / 735 / 782 / 787 | 97 / 115 / 115 / 115 | 243 / 263 / 265 / 265 | 2,464 / 2,492 / 2,508 / 2,524 | 2 / 2 / 2 / 2 | 0 / 0 / 0 / 0 |
Eval Range: 90.0 - 121.0m - | Model version | mAP | mAPH | car
(3,018) | truck
(602) | bus
(60) | bicycle
(85) | pedestrian
(1,121) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.5572 | 0.5118 | 0.7091 | 0.6393 | 0.6121 | 0.3386 | 0.4870 | - | BEVFusion-LiDAR base/2.6.0 | 0.5202 | 0.4736 | 0.6989 | 0.6297 | 0.4058 | 0.3609 | 0.5056 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.4740 | 0.4126 | 0.4223 | 0.5892 | 1.0000 | 0.4917 | 0.4497 | 0.4298 | 0.6415 | 1.0000 | 0.2930 | 0.2332 | 0.2012 | 0.4842 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 2,883) | truck
0.5/1.0/2.0/4.0
(GTs: 600) | bus
0.5/1.0/2.0/4.0
(GTs: 60) | bicycle
0.5/1.0/2.0/4.0
(GTs: 85) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 1,092) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 0) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 317 / 317 / 317 / 317 | 66 / 66 / 66 / 66 | 6 / 6 / 6 / 6 | 9 / 9 / 9 / 9 | 120 / 120 / 120 / 120 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **recall 0.40** + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 2,883) | truck
0.5/1.0/2.0/4.0
(GTs: 600) | bus
0.5/1.0/2.0/4.0
(GTs: 60) | bicycle
0.5/1.0/2.0/4.0
(GTs: 85) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 1,092) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 0) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 1,182 / 1,182 / 1,182 / 1,182 | 246 / 246 / 246 / 246 | 24 / 24 / 24 / 24 | 34 / 34 / 34 / 34 | 447 / 447 / 447 / 447 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 2,883) | truck
0.5/1.0/2.0/4.0
(GTs: 600) | bus
0.5/1.0/2.0/4.0
(GTs: 60) | bicycle
0.5/1.0/2.0/4.0
(GTs: 85) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 1,092) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 0) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 1,763 / 2,103 / 2,227 / 2,243 | 254 / 358 / 448 / 456 | 24 / 38 / 31 / 31 | 38 / 44 / 46 / 46 | 613 / 619 / 623 / 626 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 |
Eval Range: 0.0 - 121.0m - | Model version | mAP | mAPH | car
(28,895) | truck
(2,806) | bus
(539) | bicycle
(1,288) | pedestrian
(9,934) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.8086 | 0.7594 | 0.8789 | 0.7783 | 0.8898 | 0.7288 | 0.7670 | - | BEVFusion-LiDAR base/2.6.0 | 0.7995 | 0.7514 | 0.8640 | 0.7788 | 0.8608 | 0.7272 | 0.7669 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.2926 | 0.4708 | 0.3148 | 0.3550 | 1.0000 | 0.4159 | 0.4150 | 0.4074 | 0.4899 | 1.0000 | 0.2138 | 0.4027 | 0.2131 | 0.2586 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 28,684) | truck
0.5/1.0/2.0/4.0
(GTs: 2,801) | bus
0.5/1.0/2.0/4.0
(GTs: 537) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,285) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 9,868) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 64) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 3,155 / 3,155 / 3,155 / 3,155 | 308 / 308 / 308 / 308 | 59 / 59 / 59 / 59 | 141 / 141 / 141 / 141 | 1,085 / 1,085 / 1,085 / 1,085 | 7 / 7 / 7 / 7 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 28,684) | truck
0.5/1.0/2.0/4.0
(GTs: 2,801) | bus
0.5/1.0/2.0/4.0
(GTs: 537) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,285) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 9,868) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 64) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 11,760 / 11,760 / 11,760 / 11,760 | 1,148 / 1,148 / 1,148 / 1,148 | 220 / 220 / 220 / 220 | 526 / 526 / 526 / 526 | 4,045 / 4,045 / 4,045 / 4,045 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **optimal** + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 28,684) | truck
0.5/1.0/2.0/4.0
(GTs: 2,801) | bus
0.5/1.0/2.0/4.0
(GTs: 537) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,285) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 9,868) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 64) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 23,001 / 24,465 / 24,972 / 25,071 | 1,757 / 2,148 / 2,295 / 2,313 | 391 / 442 / 445 / 445 | 839 / 875 / 938 / 944 | 7,078 / 7,166 / 7,204 / 7,247 | 13 / 15 / 15 / 15 | 0 / 0 / 0 / 0 |
- J6Gen2 + JPNTaxi Gen2 -- Datasets (3,951 Testing Frames): - - `db_j6gen2_v1` - - `db_j6gen2_v2` - - `db_j6gen2_v3` - - `db_j6gen2_v4` - - `db_j6gen2_v5` - - `db_j6gen2_v6` - - `db_j6gen2_v7` - - `db_j6gen2_v8` - - `db_j6gen2_v9` +- Datasets (10,687 Testing Frames): + - `db_jpntaxigen2_v1` + - `db_jpntaxigen2_v2` - **Class mAP for BEV Center Distance: 0.5m, 1.0m, 2.0m, 4.0m**
Eval Range: 0.0 - 50.0m - | Model version | mAP | mAPH | car
(49,637) | truck
(5,754) | bus
(1,939) | bicycle
(639) | pedestrian
(14,362) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.8776 | 0.8370 | 0.8907 | 0.8438 | 0.9473 | 0.8665 | 0.8397 | - | BEVFusion-LiDAR base/2.6.0 | 0.8702 | 0.8284 | 0.8758 | 0.8410 | 0.9408 | 0.8590 | 0.8344 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(50,954) | truck
(18,624) | bus
(3,853) | bicycle
(3,768) | pedestrian
(70,699) | traffic_cone
(12,525) | barrier
(2,009) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.6595 | 0.6258 | 0.5925 | 0.5885 | 0.5757 | 0.5717 | 0.9193 | 0.8663 | 0.8424 | 0.7784 | 0.9038 | 0.3064 | 0.0000 | + +
+ +
+ Eval Range: 50.0 - 90.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(41,196) | truck
(22,942) | bus
(3,033) | bicycle
(2,310) | pedestrian
(36,881) | traffic_cone
(7,183) | barrier
(1,847) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.5440 | 0.5111 | 0.5263 | 0.5188 | 0.5099 | 0.5024 | 0.8350 | 0.6741 | 0.5382 | 0.6234 | 0.7829 | 0.3548 | 0.0000 | + +
+ +
+ Eval Range: 90.0 - 121.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(17,510) | truck
(14,707) | bus
(2,997) | bicycle
(566) | pedestrian
(16,580) | traffic_cone
(769) | barrier
(566) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.3700 | 0.3469 | 0.4109 | 0.3757 | 0.3994 | 0.3641 | 0.7043 | 0.5157 | 0.3679 | 0.2959 | 0.7000 | 0.0063 | 0.0000 | + +
+ +
+ Eval Range: 0.0 - 121.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(109,660) | truck
(56,273) | bus
(9,883) | bicycle
(6,644) | pedestrian
(124,160) | traffic_cone
(20,477) | barrier
(4,422) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.5785 | 0.5444 | 0.5467 | 0.5405 | 0.5296 | 0.5234 | 0.8675 | 0.7091 | 0.6251 | 0.6924 | 0.8516 | 0.3040 | 0.0000 | + +
+ +- **Mean TPError** + +
+ Eval Range: 0.0 - 50.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.2759 | 0.3051 | 0.2927 | 0.4984 | 1.0000 | 0.2896 | 0.3123 | 0.2968 | 0.5135 | 1.0000 | 0.1903 | 0.2037 | 0.1876 | 0.4654 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 50,954) | truck
0.5/1.0/2.0/4.0
(GTs: 18,624) | bus
0.5/1.0/2.0/4.0
(GTs: 3,853) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,768) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 70,699) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 12,525) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,009) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 5,604 / 5,604 / 5,604 / 5,604 | 2,048 / 2,048 / 2,048 / 2,048 | 423 / 423 / 423 / 423 | 414 / 414 / 414 / 414 | 7,776 / 7,776 / 7,776 / 7,776 | 1,377 / 1,377 / 1,377 / 1,377 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 50,954) | truck
0.5/1.0/2.0/4.0
(GTs: 18,624) | bus
0.5/1.0/2.0/4.0
(GTs: 3,853) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,768) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 70,699) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 12,525) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,009) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 20,891 / 20,891 / 20,891 / 20,891 | 7,635 / 7,635 / 7,635 / 7,635 | 1,579 / 1,579 / 1,579 / 1,579 | 1,544 / 1,544 / 1,544 / 1,544 | 28,986 / 28,986 / 28,986 / 28,986 | 5,135 / 5,135 / 5,135 / 5,135 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 50,954) | truck
0.5/1.0/2.0/4.0
(GTs: 18,624) | bus
0.5/1.0/2.0/4.0
(GTs: 3,853) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,768) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 70,699) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 12,525) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,009) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 44,376 / 46,850 / 47,216 / 47,606 | 14,516 / 16,104 / 17,017 / 17,294 | 3,080 / 3,354 / 3,439 / 3,443 | 2,874 / 2,897 / 2,900 / 2,900 | 59,982 / 60,714 / 61,271 / 61,531 | 5,720 / 6,079 / 6,376 / 6,515 | 0 / 0 / 0 / 0 |
Eval Range: 50.0 - 90.0m - | Model version | mAP | mAPH | car
(47,568) | truck
(4,090) | bus
(1,935) | bicycle
(295) | pedestrian
(6,529) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.6805 | 0.6279 | 0.7957 | 0.6451 | 0.7955 | 0.5394 | 0.6266 | - | BEVFusion-LiDAR base/2.6.0 | 0.6708 | 0.6165 | 0.7721 | 0.6421 | 0.7731 | 0.5472 | 0.6192 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.3424 | 0.2851 | 0.3099 | 0.5197 | 1.0000 | 0.3636 | 0.2926 | 0.3147 | 0.5610 | 1.0000 | 0.2700 | 0.1826 | 0.2042 | 0.5180 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 41,196) | truck
0.5/1.0/2.0/4.0
(GTs: 22,942) | bus
0.5/1.0/2.0/4.0
(GTs: 3,033) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,310) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 36,881) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 7,183) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,847) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 4,531 / 4,531 / 4,531 / 4,531 | 2,523 / 2,523 / 2,523 / 2,523 | 333 / 333 / 333 / 333 | 254 / 254 / 254 / 254 | 4,056 / 4,056 / 4,056 / 4,056 | 790 / 790 / 790 / 790 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 41,196) | truck
0.5/1.0/2.0/4.0
(GTs: 22,942) | bus
0.5/1.0/2.0/4.0
(GTs: 3,033) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,310) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 36,881) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 7,183) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,847) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 16,890 / 16,890 / 16,890 / 16,890 | 9,406 / 9,406 / 9,406 / 9,406 | 1,243 / 1,243 / 1,243 / 1,243 | 947 / 947 / 947 / 947 | 15,121 / 15,121 / 15,121 / 15,121 | 2,945 / 2,945 / 2,945 / 2,945 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 41,196) | truck
0.5/1.0/2.0/4.0
(GTs: 22,942) | bus
0.5/1.0/2.0/4.0
(GTs: 3,033) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,310) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 36,881) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 7,183) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,847) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 30,777 / 33,945 / 34,775 / 34,956 | 12,711 / 15,140 / 17,099 / 18,052 | 1,191 / 1,652 / 1,886 / 1,924 | 1,384 / 1,483 / 1,484 / 1,496 | 27,185 / 28,060 / 28,214 / 28,437 | 3,298 / 3,377 / 3,633 / 3,756 | 0 / 0 / 0 / 0 |
Eval Range: 90.0 - 121.0m - | Model version | mAP | mAPH | car
(17,353) | truck
(2,570) | bus
(316) | bicycle
(70) | pedestrian
(1,673) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.4902 | 0.4491 | 0.6483 | 0.4871 | 0.5172 | 0.4406 | 0.3578 | - | BEVFusion-LiDAR base/2.6.0 | 0.4462 | 0.4042 | 0.6346 | 0.4758 | 0.3215 | 0.4303 | 0.3688 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.4063 | 0.3864 | 0.3484 | 0.5995 | 1.0000 | 0.5077 | 0.3923 | 0.4395 | 0.7535 | 1.0000 | 0.3267 | 0.2998 | 0.2415 | 0.5890 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 17,510) | truck
0.5/1.0/2.0/4.0
(GTs: 14,707) | bus
0.5/1.0/2.0/4.0
(GTs: 2,997) | bicycle
0.5/1.0/2.0/4.0
(GTs: 566) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 16,580) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 769) | barrier
0.5/1.0/2.0/4.0
(GTs: 566) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 1,926 / 1,926 / 1,926 / 1,926 | 1,617 / 1,617 / 1,617 / 1,617 | 329 / 329 / 329 / 329 | 62 / 62 / 62 / 62 | 1,823 / 1,823 / 1,823 / 1,823 | 84 / 84 / 84 / 84 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 17,510) | truck
0.5/1.0/2.0/4.0
(GTs: 14,707) | bus
0.5/1.0/2.0/4.0
(GTs: 2,997) | bicycle
0.5/1.0/2.0/4.0
(GTs: 566) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 16,580) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 769) | barrier
0.5/1.0/2.0/4.0
(GTs: 566) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 7,179 / 7,179 / 7,179 / 7,179 | 6,029 / 6,029 / 6,029 / 6,029 | 1,228 / 1,228 / 1,228 / 1,228 | 232 / 232 / 232 / 232 | 6,797 / 6,797 / 6,797 / 6,797 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 17,510) | truck
0.5/1.0/2.0/4.0
(GTs: 14,707) | bus
0.5/1.0/2.0/4.0
(GTs: 2,997) | bicycle
0.5/1.0/2.0/4.0
(GTs: 566) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 16,580) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 769) | barrier
0.5/1.0/2.0/4.0
(GTs: 566) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 10,800 / 12,644 / 13,151 / 13,260 | 5,665 / 8,075 / 9,757 / 10,776 | 1,062 / 1,422 / 1,542 / 1,587 | 221 / 245 / 257 / 261 | 11,283 / 11,409 / 11,480 / 11,627 | 167 / 177 / 127 / 133 | 0 / 0 / 0 / 0 |
Eval Range: 0.0 - 121.0m - | Model version | mAP | mAPH | car
(114,558) | truck
(12,414) | bus
(4,190) | bicycle
(1,004) | pedestrian
(22,564) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR base/2.7.0 | 0.7822 | 0.7349 | 0.8292 | 0.7169 | 0.8590 | 0.7505 | 0.7556 | - | BEVFusion-LiDAR base/2.6.0 | 0.7712 | 0.7223 | 0.8110 | 0.7129 | 0.8348 | 0.7458 | 0.7515 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR base/2.8.0 | 0.3080 | 0.2997 | 0.3015 | 0.5162 | 1.0000 | 0.3316 | 0.3074 | 0.3083 | 0.5404 | 1.0000 | 0.2360 | 0.2001 | 0.1988 | 0.4973 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 109,660) | truck
0.5/1.0/2.0/4.0
(GTs: 56,273) | bus
0.5/1.0/2.0/4.0
(GTs: 9,883) | bicycle
0.5/1.0/2.0/4.0
(GTs: 6,644) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 124,160) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,477) | barrier
0.5/1.0/2.0/4.0
(GTs: 4,422) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 12,062 / 12,062 / 12,062 / 12,062 | 6,190 / 6,190 / 6,190 / 6,190 | 1,087 / 1,087 / 1,087 / 1,087 | 730 / 730 / 730 / 730 | 13,657 / 13,657 / 13,657 / 13,657 | 2,252 / 2,252 / 2,252 / 2,252 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 109,660) | truck
0.5/1.0/2.0/4.0
(GTs: 56,273) | bus
0.5/1.0/2.0/4.0
(GTs: 9,883) | bicycle
0.5/1.0/2.0/4.0
(GTs: 6,644) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 124,160) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,477) | barrier
0.5/1.0/2.0/4.0
(GTs: 4,422) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 44,960 / 44,960 / 44,960 / 44,960 | 23,071 / 23,071 / 23,071 / 23,071 | 4,052 / 4,052 / 4,052 / 4,052 | 2,724 / 2,724 / 2,724 / 2,724 | 50,905 / 50,905 / 50,905 / 50,905 | 8,395 / 8,395 / 8,395 / 8,395 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 109,660) | truck
0.5/1.0/2.0/4.0
(GTs: 56,273) | bus
0.5/1.0/2.0/4.0
(GTs: 9,883) | bicycle
0.5/1.0/2.0/4.0
(GTs: 6,644) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 124,160) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,477) | barrier
0.5/1.0/2.0/4.0
(GTs: 4,422) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR base/2.8.0 | 85,327 / 93,303 / 94,826 / 95,316 | 32,549 / 39,457 / 43,979 / 46,340 | 5,296 / 6,439 / 6,895 / 6,991 | 4,446 / 4,536 / 4,537 / 4,547 | 98,679 / 99,889 / 100,492 / 101,325 | 9,207 / 9,578 / 10,010 / 10,328 | 0 / 0 / 0 / 0 |
@@ -272,6 +717,1015 @@ ## Release +### BEVFusion-LiDAR base/2.8.0 + +
+ Changes +- Add datasets: `db_j6gen2_v10`, `db_j6gen2_v11`, and `db_j6gen2_v12`. +- Update training batch size from `8` to `16` per gpu. +- Update number of max points per voxel from `10` to `32`. +- Implement 1D-flatten sparse to dense to reduce ONNX ops (projects/BEVFusion/bevfusion/custom_sparse_conv_tensor.py). +- Purse custom LayerNorm in mmdeploy to support ONNX LayerNorm ops (purge_mmdeploy_symbolics). +- Add two classes: `traffic_cone` and `barrier` to the model. +- Do not max-pooling of `bicycle`. +- Filter 3D bboxes in the train set by distance (< 60m, >= 3) and (60m <= x < 130m, >= 2). +- Filter 3D bboxes in the test set by number of points (>= 2). + + +
+ +
+ Artifacts + +- Deployed onnx and ROS parameter files (for internal) + - [WebAuto](https://evaluation.ci.tier4.jp/evaluation/mlpackages/46f8188d-e3be-4f2f-b989-fd27002610d7/releases/6c8bc393-9cff-413c-bfc7-52c1fcd8ba8a?project_id=zWhWRzei) + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/t4base/v2.8.0/deployment.zip) + - [Google drive](https://drive.google.com/file/d/16dh2UQg4w46WQu0Dbmai9BtD43nz7hLv/view?usp=drive_link) +- Logs (for internal) + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/t4base/v2.8.0/logs.zip) + - [Google drive](https://drive.google.com/file/d/1kQVufXiB_K9JYTL3DSUhGUIGW5fCbbwc/view?usp=drive_link) +- Pytorch Best checkpoints: + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/t4base/v2.8.0/best_epoch_47.zip) + - [Google drive](https://drive.google.com/file/d/1wJjdG1dCbOjfmTCaOFWw9-2xNvlfYDza/view?usp=drive_link) + +
+ +
+ Training configs + +- [Config file path](https://github.com/KSeangTan/AWML/blob/179ca256a165fd483801bec0a2a95c24866edf70/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_50e_8xb16_base_120m.py) +- Train time: NVIDIA H200 140GB * 8 * 50 epochs ~= 4 days +- Batch size: 8*16 = 128 +- Training Dataset (frames: 151,478): + - jpntaxi: db_jpntaxi_v1 + db_jpntaxi_v2 + db_jpntaxi_v4 (28,161 frames) + - j6: db_gsm8_v1 + db_j6_v1 + db_j6_v2 + db_j6_v3 + db_j6_v5 (29,336 frames) + - j6gen2: db_j6gen2_v1 + db_j6gen2_v2 + db_j6gen2_v3 + db_j6gen2_v4 + db_j6gen2_v5 + db_j6gen2_v6 + db_j6gen2_v7 + db_j6gen2_v8 + db_j6gen2_v9 + db_j6gen2_v10 + db_j6gen2_v11 + db_j6gen2_v12 (51,208 frames) + - largebus: db_largebus_v1 + db_largebus_v2 + db_largebus_v3 (12,605 frames) + - jpntaxi_gen2: db_jpntaxigen2_v1 + db_jpntaxigen2_v2 (30,168 frames) + +
+ +
+ Evaluation + +**Base Datasets (16,597 frames)**: + + - j6gen2 (4,682 frames): db_j6gen2_v1 + db_j6gen2_v2 + db_j6gen2_v3 + db_j6gen2_v4 + db_j6gen2_v5 + db_j6gen2_v6 + db_j6gen2_v7 + db_j6gen2_v8 + db_j6gen2_v9 + db_j6gen2_v10 + db_j6gen2_v11 + db_j6gen2_v12 + - largebus (1,228 frames): db_largebus_v1 + db_largebus_v2 + db_largebus_v3 + - jpntaxi_gen2 (10,687 frames): db_jpntaxigen2_v1 + db_jpntaxigen2_v2 + +**Total BEV Center Distance mAP (eval range = 0.0 - 50.0m): 0.6763** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 126,168 | 112,018 / 117,488 / 119,486 / 120,557 | 0.852 / 0.913 / 0.925 / 0.936 | 0.900 / 0.932 / 0.939 / 0.942 | 0.249 / 0.213 / 0.168 / 0.148 | +| truck | 26,897 | 21,675 / 24,088 / 25,410 / 25,929 | 0.719 / 0.849 / 0.916 / 0.942 | 0.810 / 0.884 / 0.922 / 0.937 | 0.293 / 0.202 / 0.176 / 0.176 | +| bus | 6,559 | 5,520 / 5,973 / 6,143 / 6,163 | 0.789 / 0.876 / 0.908 / 0.909 | 0.828 / 0.887 / 0.909 / 0.911 | 0.047 / 0.044 / 0.044 / 0.044 | +| bicycle | 5,865 | 5,319 / 5,410 / 5,419 / 5,428 | 0.799 / 0.820 / 0.821 / 0.823 | 0.819 / 0.826 / 0.827 / 0.828 | 0.234 / 0.202 / 0.236 / 0.236 | +| pedestrian | 93,520 | 87,053 / 88,369 / 88,873 / 89,317 | 0.873 / 0.889 / 0.899 / 0.905 | 0.861 / 0.872 / 0.878 / 0.883 | 0.154 / 0.148 / 0.148 / 0.148 | +| traffic_cone | 20,835 | 13,926 / 14,948 / 15,514 / 15,991 | 0.293 / 0.330 / 0.358 / 0.386 | 0.484 / 0.509 / 0.527 / 0.548 | 0.153 / 0.143 / 0.143 / 0.140 | +| barrier | 3,359 | 518 / 711 / 772 / 804 | 0.017 / 0.055 / 0.066 / 0.071 | 0.199 / 0.263 / 0.279 / 0.285 | 0.151 / 0.107 / 0.107 / 0.083 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 13,878 / 13,878 / 13,878 / 13,878 | 0.106 / 0.112 / 0.114 / 0.119 | 0.029 / 0.031 / 0.032 / 0.033 | 0.108 / 0.110 / 0.110 / 0.110 | 0.210 / 0.214 / 0.215 / 0.216 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,958 / 2,958 / 2,958 / 2,958 | 0.137 / 0.163 / 0.183 / 0.198 | 0.019 / 0.019 / 0.020 / 0.020 | 0.117 / 0.123 / 0.127 / 0.129 | 0.381 / 0.390 / 0.398 / 0.401 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 721 / 721 / 721 / 721 | 0.137 / 0.148 / 0.159 / 0.161 | 0.045 / 0.046 / 0.046 / 0.047 | 0.096 / 0.098 / 0.101 / 0.101 | 0.220 / 0.226 / 0.226 / 0.226 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 645 / 645 / 645 / 645 | 0.127 / 0.131 / 0.132 / 0.134 | 0.089 / 0.090 / 0.090 / 0.091 | 0.187 / 0.188 / 0.188 / 0.188 | 0.758 / 0.757 / 0.758 / 0.758 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 10,287 / 10,287 / 10,287 / 10,287 | 0.106 / 0.109 / 0.114 / 0.126 | 0.282 / 0.284 / 0.286 / 0.288 | 0.228 / 0.228 / 0.228 / 0.228 | 0.380 / 0.380 / 0.380 / 0.382 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 2,291 / 2,291 / 2,291 / 2,291 | 0.147 / 0.173 / 0.236 / 0.364 | 0.505 / 0.505 / 0.509 / 0.503 | 0.291 / 0.292 / 0.292 / 0.292 | 0.063 / 0.063 / 0.063 / 0.062 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 369 / 369 / 369 / 369 | 0.243 / 0.334 / 0.381 / 0.436 | 0.448 / 0.413 / 0.404 / 0.405 | 0.356 / 0.378 / 0.381 / 0.390 | 0.023 / 0.024 / 0.025 / 0.025 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 51,728 / 51,728 / 51,728 / 51,728 | 0.113 / 0.122 / 0.124 / 0.130 | 0.032 / 0.036 / 0.037 / 0.038 | 0.113 / 0.115 / 0.115 / 0.115 | 0.236 / 0.239 / 0.241 / 0.241 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 11,027 / 11,027 / 11,027 / 11,027 | 0.145 / 0.177 / 0.203 / 0.224 | 0.020 / 0.021 / 0.022 / 0.023 | 0.121 / 0.128 / 0.133 / 0.135 | 0.439 / 0.445 / 0.455 / 0.457 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,689 / 2,689 / 2,689 / 2,689 | 0.143 / 0.159 / 0.171 / 0.173 | 0.044 / 0.046 / 0.046 / 0.047 | 0.099 / 0.102 / 0.105 / 0.105 | 0.257 / 0.262 / 0.261 / 0.261 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 2,404 / 2,404 / 2,404 / 2,404 | 0.131 / 0.136 / 0.136 / 0.140 | 0.092 / 0.093 / 0.093 / 0.094 | 0.194 / 0.195 / 0.195 / 0.196 | 0.719 / 0.721 / 0.721 / 0.722 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 38,343 / 38,343 / 38,343 / 38,343 | 0.110 / 0.115 / 0.123 / 0.139 | 0.291 / 0.292 / 0.295 / 0.299 | 0.231 / 0.231 / 0.231 / 0.232 | 0.392 / 0.391 / 0.392 / 0.394 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 8,542 / 8,542 / 8,542 / 8,542 | 0.159 / 0.196 / 0.282 / 0.450 | 0.553 / 0.551 / 0.555 / 0.546 | 0.297 / 0.297 / 0.298 / 0.297 | 0.065 / 0.064 / 0.064 / 0.064 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 109,035 / 113,613 / 115,502 / 116,446 | 0.128 / 0.147 / 0.159 / 0.175 | 0.046 / 0.055 / 0.061 / 0.064 | 0.121 / 0.125 / 0.127 / 0.127 | 0.267 / 0.274 / 0.279 / 0.281 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 20,578 / 23,058 / 24,234 / 24,631 | 0.157 / 0.207 / 0.257 / 0.300 | 0.024 / 0.028 / 0.031 / 0.033 | 0.129 / 0.141 / 0.150 / 0.154 | 0.469 / 0.490 / 0.515 / 0.523 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 5,398 / 5,809 / 5,952 / 5,967 | 0.157 / 0.193 / 0.219 / 0.227 | 0.045 / 0.051 / 0.052 / 0.055 | 0.107 / 0.117 / 0.123 / 0.123 | 0.307 / 0.306 / 0.306 / 0.308 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 4,573 / 4,739 / 4,611 / 4,616 | 0.134 / 0.140 / 0.140 / 0.144 | 0.098 / 0.102 / 0.099 / 0.099 | 0.198 / 0.200 / 0.199 / 0.200 | 0.710 / 0.712 / 0.710 / 0.710 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 78,245 / 79,717 / 80,219 / 80,761 | 0.117 / 0.127 / 0.141 / 0.174 | 0.312 / 0.316 / 0.321 / 0.328 | 0.236 / 0.237 / 0.238 / 0.238 | 0.401 / 0.401 / 0.402 / 0.405 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 10,168 / 11,012 / 11,411 / 11,947 | 0.156 / 0.190 / 0.271 / 0.440 | 0.543 / 0.550 / 0.552 / 0.546 | 0.295 / 0.297 / 0.297 / 0.297 | 0.064 / 0.064 / 0.064 / 0.064 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 452 / 629 / 667 / 715 | 0.247 / 0.353 / 0.409 / 0.509 | 0.452 / 0.430 / 0.423 / 0.430 | 0.363 / 0.405 / 0.412 / 0.433 | 0.023 / 0.025 / 0.025 / 0.025 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 50.0 - 90.0m): 0.5447** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 105,914 | 83,239 / 92,799 / 97,175 / 98,872 | 0.700 / 0.813 / 0.860 / 0.880 | 0.780 / 0.843 / 0.865 / 0.872 | 0.227 / 0.188 / 0.164 / 0.161 | +| truck | 28,864 | 17,414 / 21,372 / 24,178 / 25,356 | 0.467 / 0.634 / 0.757 / 0.803 | 0.624 / 0.729 / 0.800 / 0.823 | 0.223 / 0.182 / 0.149 / 0.140 | +| bus | 5,290 | 3,038 / 4,048 / 4,566 / 4,671 | 0.393 / 0.623 / 0.760 / 0.785 | 0.537 / 0.695 / 0.778 / 0.793 | 0.154 / 0.077 / 0.077 / 0.077 | +| bicycle | 3,608 | 2,849 / 3,030 / 3,047 / 3,063 | 0.575 / 0.635 / 0.640 / 0.647 | 0.662 / 0.691 / 0.693 / 0.698 | 0.147 / 0.142 / 0.142 / 0.142 | +| pedestrian | 48,637 | 43,259 / 43,978 / 44,223 / 44,525 | 0.729 / 0.748 / 0.756 / 0.768 | 0.750 / 0.759 / 0.764 / 0.770 | 0.153 / 0.146 / 0.148 / 0.148 | +| traffic_cone | 9,819 | 6,361 / 6,625 / 6,770 / 7,010 | 0.276 / 0.301 / 0.327 / 0.369 | 0.462 / 0.478 / 0.496 / 0.518 | 0.135 / 0.135 / 0.125 / 0.135 | +| barrier | 2,469 | 180 / 276 / 305 / 321 | 0.000 / 0.001 / 0.002 / 0.004 | 0.093 / 0.143 / 0.154 / 0.157 | 0.116 / 0.097 / 0.097 / 0.097 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 11,650 / 11,650 / 11,650 / 11,650 | 0.151 / 0.171 / 0.184 / 0.197 | 0.078 / 0.099 / 0.113 / 0.116 | 0.148 / 0.151 / 0.152 / 0.153 | 0.231 / 0.241 / 0.246 / 0.249 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 3,175 / 3,175 / 3,175 / 3,175 | 0.195 / 0.237 / 0.297 / 0.330 | 0.026 / 0.030 / 0.034 / 0.037 | 0.148 / 0.156 / 0.165 / 0.169 | 0.492 / 0.538 / 0.572 / 0.589 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 581 / 581 / 581 / 581 | 0.196 / 0.267 / 0.331 / 0.356 | 0.088 / 0.084 / 0.088 / 0.094 | 0.124 / 0.134 / 0.142 / 0.144 | 0.190 / 0.199 / 0.202 / 0.202 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 396 / 396 / 396 / 396 | 0.183 / 0.200 / 0.203 / 0.235 | 0.107 / 0.110 / 0.111 / 0.110 | 0.223 / 0.226 / 0.226 / 0.225 | 0.786 / 0.801 / 0.801 / 0.801 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 5,350 / 5,350 / 5,350 / 5,350 | 0.116 / 0.122 / 0.134 / 0.166 | 0.339 / 0.343 / 0.346 / 0.352 | 0.253 / 0.254 / 0.254 / 0.254 | 0.429 / 0.429 / 0.430 / 0.434 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,080 / 1,080 / 1,080 / 1,080 | 0.164 / 0.184 / 0.300 / 0.551 | 0.429 / 0.435 / 0.436 / 0.439 | 0.249 / 0.250 / 0.250 / 0.250 | 0.089 / 0.088 / 0.088 / 0.087 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 271 / 271 / 271 | 1.000 / 0.398 / 0.488 / 0.703 | 1.000 / 0.327 / 0.314 / 0.313 | 1.000 / 0.414 / 0.417 / 0.433 | 1.000 / 0.036 / 0.036 / 0.035 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 43,424 / 43,424 / 43,424 / 43,424 | 0.163 / 0.188 / 0.207 / 0.224 | 0.095 / 0.121 / 0.139 / 0.144 | 0.154 / 0.157 / 0.159 / 0.159 | 0.260 / 0.271 / 0.277 / 0.280 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 11,834 / 11,834 / 11,834 / 11,834 | 0.201 / 0.263 / 0.343 / 0.390 | 0.034 / 0.039 / 0.044 / 0.047 | 0.153 / 0.165 / 0.176 / 0.182 | 0.581 / 0.629 / 0.666 / 0.685 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,168 / 2,168 / 2,168 / 2,168 | 0.219 / 0.317 / 0.401 / 0.439 | 0.077 / 0.076 / 0.085 / 0.094 | 0.129 / 0.142 / 0.153 / 0.154 | 0.225 / 0.222 / 0.223 / 0.223 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,479 / 1,479 / 1,479 / 1,479 | 0.187 / 0.208 / 0.211 / 0.253 | 0.132 / 0.135 / 0.136 / 0.133 | 0.229 / 0.232 / 0.232 / 0.231 | 0.812 / 0.825 / 0.825 / 0.828 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 19,941 / 19,941 / 19,941 / 19,941 | 0.121 / 0.129 / 0.144 / 0.186 | 0.363 / 0.368 / 0.371 / 0.379 | 0.254 / 0.254 / 0.255 / 0.255 | 0.449 / 0.449 / 0.450 / 0.455 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 4,025 / 4,025 / 4,025 / 4,025 | 0.172 / 0.195 / 0.320 / 0.588 | 0.437 / 0.444 / 0.444 / 0.448 | 0.248 / 0.249 / 0.249 / 0.250 | 0.086 / 0.086 / 0.085 / 0.085 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 76,795 / 84,883 / 88,518 / 89,420 | 0.174 / 0.215 / 0.247 / 0.276 | 0.122 / 0.158 / 0.183 / 0.189 | 0.160 / 0.165 / 0.167 / 0.168 | 0.288 / 0.310 / 0.324 / 0.327 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 15,896 / 19,085 / 21,454 / 22,235 | 0.205 / 0.280 / 0.387 / 0.461 | 0.036 / 0.048 / 0.060 / 0.064 | 0.156 / 0.171 / 0.190 / 0.199 | 0.611 / 0.683 / 0.745 / 0.776 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,428 / 3,407 / 3,812 / 3,890 | 0.217 / 0.335 / 0.437 / 0.496 | 0.072 / 0.077 / 0.091 / 0.109 | 0.128 / 0.146 / 0.158 / 0.160 | 0.192 / 0.236 / 0.245 / 0.244 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 2,186 / 2,306 / 2,311 / 2,327 | 0.186 / 0.206 / 0.209 / 0.255 | 0.129 / 0.133 / 0.133 / 0.133 | 0.230 / 0.233 / 0.234 / 0.233 | 0.807 / 0.817 / 0.817 / 0.821 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 34,885 / 35,834 / 35,898 / 36,174 | 0.124 / 0.134 / 0.151 / 0.200 | 0.374 / 0.381 / 0.385 / 0.393 | 0.254 / 0.255 / 0.255 / 0.255 | 0.460 / 0.464 / 0.463 / 0.468 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 4,370 / 4,522 / 4,871 / 4,902 | 0.167 / 0.187 / 0.315 / 0.563 | 0.434 / 0.441 / 0.443 / 0.447 | 0.249 / 0.250 / 0.250 / 0.251 | 0.087 / 0.087 / 0.086 / 0.086 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 140 / 222 / 238 / 244 | 0.243 / 0.402 / 0.475 / 0.566 | 0.329 / 0.297 / 0.288 / 0.282 | 0.319 / 0.369 / 0.379 / 0.384 | 0.035 / 0.034 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 90.0 - 121.0m): 0.3779** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 39,577 | 28,566 / 33,503 / 35,851 / 36,643 | 0.545 / 0.697 / 0.765 / 0.785 | 0.653 / 0.737 / 0.769 / 0.778 | 0.197 / 0.165 / 0.142 / 0.129 | +| truck | 18,213 | 7,767 / 11,422 / 14,150 / 15,707 | 0.210 / 0.458 / 0.641 / 0.748 | 0.437 / 0.617 / 0.728 / 0.779 | 0.186 / 0.178 / 0.139 / 0.130 | +| bus | 3,541 | 1,576 / 2,234 / 2,516 / 2,627 | 0.174 / 0.385 / 0.475 / 0.511 | 0.408 / 0.544 / 0.593 / 0.611 | 0.050 / 0.050 / 0.050 / 0.046 | +| bicycle | 942 | 651 / 733 / 753 / 757 | 0.279 / 0.363 / 0.397 / 0.405 | 0.459 / 0.509 / 0.525 / 0.531 | 0.159 / 0.159 / 0.159 / 0.159 | +| pedestrian | 20,134 | 17,375 / 17,647 / 17,776 / 17,948 | 0.639 / 0.654 / 0.663 / 0.678 | 0.685 / 0.693 / 0.697 / 0.704 | 0.136 / 0.136 / 0.136 / 0.136 | +| traffic_cone | 1,231 | 454 / 474 / 484 / 524 | 0.022 / 0.024 / 0.028 / 0.036 | 0.194 / 0.197 / 0.206 / 0.220 | 0.143 / 0.143 / 0.144 / 0.143 | +| barrier | 711 | 38 / 65 / 83 / 88 | 0.000 / 0.000 / 0.000 / 0.001 | 0.061 / 0.106 / 0.132 / 0.137 | 0.082 / 0.059 / 0.059 / 0.059 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 4,353 / 4,353 / 4,353 / 4,353 | 0.196 / 0.233 / 0.264 / 0.291 | 0.138 / 0.178 / 0.201 / 0.209 | 0.181 / 0.185 / 0.187 / 0.188 | 0.432 / 0.437 / 0.441 / 0.442 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,003 / 2,003 / 2,003 / 2,003 | 0.236 / 0.343 / 0.443 / 0.588 | 0.032 / 0.035 / 0.040 / 0.047 | 0.178 / 0.193 / 0.203 / 0.222 | 0.578 / 0.584 / 0.604 / 0.616 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 389 / 389 / 389 / 389 | 0.255 / 0.363 / 0.444 / 0.496 | 0.034 / 0.039 / 0.041 / 0.046 | 0.138 / 0.147 / 0.160 / 0.167 | 0.215 / 0.244 / 0.272 / 0.278 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 103 / 103 / 103 / 103 | 0.210 / 0.255 / 0.293 / 0.338 | 0.192 / 0.184 / 0.180 / 0.190 | 0.238 / 0.250 / 0.262 / 0.260 | 0.831 / 0.866 / 0.881 / 0.878 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 2,214 / 2,214 / 2,214 / 2,214 | 0.132 / 0.139 / 0.151 / 0.205 | 0.274 / 0.275 / 0.280 / 0.289 | 0.276 / 0.276 / 0.276 / 0.276 | 0.538 / 0.538 / 0.539 / 0.544 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 135 / 135 / 135 / 135 | 0.180 / 0.203 / 0.304 / 0.671 | 0.686 / 0.693 / 0.690 / 0.687 | 0.343 / 0.343 / 0.343 / 0.339 | 0.110 / 0.110 / 0.112 / 0.114 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 78 / 78 | 1.000 / 1.000 / 0.652 / 0.972 | 1.000 / 1.000 / 0.257 / 0.243 | 1.000 / 1.000 / 0.479 / 0.489 | 1.000 / 1.000 / 0.050 / 0.051 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 16,226 / 16,226 / 16,226 / 16,226 | 0.206 / 0.253 / 0.292 / 0.326 | 0.175 / 0.216 / 0.241 / 0.250 | 0.186 / 0.191 / 0.192 / 0.193 | 0.460 / 0.466 / 0.472 / 0.473 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 7,467 / 7,467 / 7,467 / 7,467 | 0.243 / 0.374 / 0.510 / 0.666 | 0.070 / 0.050 / 0.054 / 0.063 | 0.190 / 0.202 / 0.215 / 0.234 | 0.696 / 0.686 / 0.693 / 0.706 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,451 / 1,451 / 1,451 / 1,451 | 0.271 / 0.376 / 0.474 / 0.535 | 0.039 / 0.044 / 0.048 / 0.055 | 0.144 / 0.154 / 0.170 / 0.177 | 0.229 / 0.283 / 0.316 / 0.322 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 386 / 386 / 386 / 386 | 0.206 / 0.257 / 0.290 / 0.341 | 0.225 / 0.206 / 0.202 / 0.212 | 0.232 / 0.243 / 0.253 / 0.251 | 0.866 / 0.932 / 0.946 / 0.944 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 8,254 / 8,254 / 8,254 / 8,254 | 0.138 / 0.148 / 0.163 / 0.231 | 0.317 / 0.319 / 0.324 / 0.336 | 0.272 / 0.272 / 0.273 / 0.273 | 0.551 / 0.551 / 0.552 / 0.559 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 504 | 1.000 / 1.000 / 1.000 / 0.742 | 1.000 / 1.000 / 1.000 / 0.658 | 1.000 / 1.000 / 1.000 / 0.328 | 1.000 / 1.000 / 1.000 / 0.124 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 23,637 / 27,796 / 30,068 / 31,051 | 0.208 / 0.265 / 0.316 / 0.362 | 0.187 / 0.242 / 0.276 / 0.289 | 0.187 / 0.193 / 0.195 / 0.196 | 0.468 / 0.484 / 0.494 / 0.497 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,996 / 9,963 / 12,146 / 13,138 | 0.240 / 0.374 / 0.533 / 0.705 | 0.046 / 0.049 / 0.062 / 0.073 | 0.184 / 0.204 / 0.222 / 0.242 | 0.662 / 0.697 / 0.744 / 0.767 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,277 / 1,704 / 1,857 / 1,963 | 0.257 / 0.364 / 0.451 / 0.512 | 0.039 / 0.043 / 0.046 / 0.050 | 0.136 / 0.148 / 0.162 / 0.170 | 0.221 / 0.269 / 0.302 / 0.305 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 379 / 421 / 434 / 439 | 0.202 / 0.248 / 0.279 / 0.329 | 0.175 / 0.169 / 0.165 / 0.180 | 0.237 / 0.248 / 0.257 / 0.256 | 0.864 / 0.904 / 0.922 / 0.916 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 13,121 / 13,270 / 13,350 / 13,493 | 0.139 / 0.149 / 0.163 / 0.233 | 0.313 / 0.316 / 0.320 / 0.333 | 0.272 / 0.272 / 0.272 / 0.273 | 0.551 / 0.551 / 0.552 / 0.560 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 247 / 251 / 262 / 280 | 0.180 / 0.195 / 0.301 / 0.651 | 0.723 / 0.731 / 0.722 / 0.717 | 0.347 / 0.346 / 0.345 / 0.341 | 0.110 / 0.111 / 0.115 / 0.117 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 28 / 54 / 67 / 70 | 0.268 / 0.452 / 0.633 / 0.979 | 0.376 / 0.247 / 0.242 / 0.235 | 0.374 / 0.427 / 0.450 / 0.454 | 0.048 / 0.046 / 0.048 / 0.048 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 0.0 - 121.0m): 0.5936** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 271,659 | 224,244 / 244,411 / 253,295 / 256,954 | 0.763 / 0.852 / 0.893 / 0.905 | 0.821 / 0.872 / 0.888 / 0.894 | 0.229 / 0.192 / 0.165 / 0.161 | +| truck | 73,974 | 46,967 / 57,070 / 64,081 / 67,424 | 0.504 / 0.682 / 0.801 / 0.856 | 0.652 / 0.765 / 0.833 / 0.860 | 0.246 / 0.182 / 0.166 / 0.139 | +| bus | 15,390 | 10,156 / 12,295 / 13,277 / 13,514 | 0.528 / 0.694 / 0.779 / 0.796 | 0.634 / 0.745 / 0.794 / 0.804 | 0.058 / 0.057 / 0.057 / 0.057 | +| bicycle | 10,415 | 8,835 / 9,191 / 9,237 / 9,265 | 0.685 / 0.726 / 0.730 / 0.733 | 0.738 / 0.755 / 0.757 / 0.759 | 0.202 / 0.198 / 0.198 / 0.198 | +| pedestrian | 162,291 | 147,838 / 150,126 / 151,000 / 151,937 | 0.812 / 0.828 / 0.839 / 0.846 | 0.808 / 0.818 / 0.823 / 0.829 | 0.153 / 0.148 / 0.148 / 0.148 | +| traffic_cone | 31,885 | 20,767 / 22,074 / 22,797 / 23,566 | 0.276 / 0.308 / 0.333 / 0.365 | 0.465 / 0.487 / 0.505 / 0.527 | 0.154 / 0.138 / 0.138 / 0.138 | +| barrier | 6,539 | 737 / 1,055 / 1,164 / 1,216 | 0.001 / 0.021 / 0.029 / 0.033 | 0.147 / 0.204 / 0.218 / 0.224 | 0.125 / 0.107 / 0.107 / 0.082 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 29,883 / 29,882 / 29,882 / 29,882 | 0.126 / 0.140 / 0.149 / 0.157 | 0.049 / 0.060 / 0.068 / 0.070 | 0.125 / 0.128 / 0.129 / 0.130 | 0.236 / 0.242 / 0.247 / 0.249 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 8,137 / 8,137 / 8,137 / 8,137 | 0.169 / 0.214 / 0.262 / 0.307 | 0.022 / 0.025 / 0.028 / 0.031 | 0.134 / 0.144 / 0.152 / 0.158 | 0.446 / 0.472 / 0.496 / 0.509 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,692 / 1,692 / 1,692 / 1,692 | 0.159 / 0.199 / 0.236 / 0.250 | 0.055 / 0.057 / 0.059 / 0.061 | 0.106 / 0.113 / 0.119 / 0.120 | 0.227 / 0.235 / 0.240 / 0.241 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,145 / 1,145 / 1,145 / 1,145 | 0.143 / 0.152 / 0.155 / 0.165 | 0.096 / 0.098 / 0.098 / 0.099 | 0.197 / 0.200 / 0.201 / 0.201 | 0.747 / 0.753 / 0.753 / 0.753 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 17,852 / 17,852 / 17,852 / 17,852 | 0.111 / 0.115 / 0.123 / 0.143 | 0.297 / 0.298 / 0.301 / 0.305 | 0.237 / 0.237 / 0.237 / 0.238 | 0.402 / 0.402 / 0.403 / 0.405 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 3,507 / 3,507 / 3,507 / 3,507 | 0.153 / 0.177 / 0.255 / 0.423 | 0.488 / 0.491 / 0.493 / 0.489 | 0.281 / 0.282 / 0.282 / 0.282 | 0.070 / 0.070 / 0.070 / 0.069 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 719 / 719 / 719 / 719 | 0.248 / 0.365 / 0.428 / 0.532 | 0.434 / 0.387 / 0.377 / 0.375 | 0.385 / 0.392 / 0.395 / 0.406 | 0.028 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 111,380 / 111,380 / 111,380 / 111,380 | 0.138 / 0.157 / 0.169 / 0.180 | 0.061 / 0.077 / 0.088 / 0.090 | 0.133 / 0.136 / 0.138 / 0.138 | 0.264 / 0.271 / 0.276 / 0.278 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 30,329 / 30,329 / 30,329 / 30,329 | 0.181 / 0.241 / 0.307 / 0.367 | 0.027 / 0.031 / 0.034 / 0.038 | 0.142 / 0.154 / 0.164 / 0.171 | 0.519 / 0.545 / 0.570 / 0.584 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 6,309 / 6,309 / 6,309 / 6,309 | 0.179 / 0.238 / 0.288 / 0.309 | 0.053 / 0.056 / 0.060 / 0.065 | 0.113 / 0.123 / 0.130 / 0.132 | 0.259 / 0.261 / 0.265 / 0.266 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 4,270 / 4,270 / 4,270 / 4,270 | 0.151 / 0.163 / 0.166 / 0.182 | 0.108 / 0.110 / 0.110 / 0.111 | 0.207 / 0.210 / 0.211 / 0.211 | 0.741 / 0.749 / 0.750 / 0.750 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 66,539 / 66,539 / 66,539 / 66,539 | 0.116 / 0.122 / 0.133 / 0.162 | 0.312 / 0.314 / 0.318 / 0.323 | 0.241 / 0.242 / 0.242 / 0.242 | 0.420 / 0.420 / 0.420 / 0.424 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 13,072 / 13,072 / 13,072 / 13,072 | 0.164 / 0.198 / 0.296 / 0.504 | 0.526 / 0.528 / 0.530 / 0.525 | 0.284 / 0.285 / 0.286 / 0.285 | 0.072 / 0.072 / 0.071 / 0.071 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 209,064 / 225,972 / 233,684 / 235,583 | 0.154 / 0.187 / 0.212 / 0.234 | 0.088 / 0.115 / 0.133 / 0.138 | 0.143 / 0.148 / 0.150 / 0.151 | 0.297 / 0.313 / 0.324 / 0.326 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 43,131 / 52,358 / 57,620 / 60,504 | 0.187 / 0.266 / 0.362 / 0.453 | 0.030 / 0.040 / 0.047 / 0.054 | 0.147 / 0.164 / 0.179 / 0.191 | 0.550 / 0.602 / 0.646 / 0.675 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 9,224 / 10,854 / 11,562 / 11,707 | 0.189 / 0.266 / 0.329 / 0.363 | 0.054 / 0.060 / 0.066 / 0.074 | 0.117 / 0.130 / 0.140 / 0.142 | 0.272 / 0.282 / 0.289 / 0.291 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 7,044 / 7,224 / 7,242 / 7,260 | 0.152 / 0.163 / 0.167 / 0.183 | 0.109 / 0.111 / 0.111 / 0.111 | 0.209 / 0.211 / 0.212 / 0.212 | 0.743 / 0.750 / 0.752 / 0.752 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 125,762 / 128,271 / 129,072 / 130,130 | 0.121 / 0.131 / 0.146 / 0.188 | 0.328 / 0.333 / 0.338 / 0.347 | 0.245 / 0.246 / 0.246 / 0.247 | 0.432 / 0.433 / 0.434 / 0.438 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 14,442 / 15,921 / 16,500 / 17,198 | 0.158 / 0.190 / 0.285 / 0.485 | 0.515 / 0.523 / 0.525 / 0.522 | 0.283 / 0.285 / 0.284 / 0.285 | 0.072 / 0.071 / 0.071 / 0.071 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 624 / 886 / 950 / 1,028 | 0.246 / 0.367 / 0.435 / 0.567 | 0.424 / 0.390 / 0.381 / 0.385 | 0.355 / 0.393 / 0.403 / 0.423 | 0.026 / 0.028 / 0.028 / 0.029 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +--- + +**LargeBus**: db_largebus_v1 + db_largebus_v2 + db_largebus_v3 (1,228 frames) + +**Total BEV Center Distance mAP (eval range = 0.0 - 50.0m): 0.6292** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 14,872 | 13,343 / 13,829 / 14,017 / 14,145 | 0.867 / 0.906 / 0.926 / 0.937 | 0.914 / 0.938 / 0.943 / 0.946 | 0.228 / 0.194 / 0.193 / 0.145 | +| truck | 1,192 | 960 / 1,091 / 1,118 / 1,135 | 0.726 / 0.886 / 0.910 / 0.928 | 0.823 / 0.910 / 0.919 / 0.923 | 0.247 / 0.208 / 0.157 / 0.152 | +| bus | 336 | 283 / 329 / 332 / 332 | 0.793 / 0.962 / 0.973 / 0.973 | 0.859 / 0.950 / 0.959 / 0.959 | 0.286 / 0.065 / 0.065 / 0.065 | +| bicycle | 740 | 694 / 713 / 717 / 724 | 0.825 / 0.870 / 0.877 / 0.891 | 0.837 / 0.855 / 0.856 / 0.863 | 0.201 / 0.211 / 0.211 / 0.211 | +| pedestrian | 5,055 | 4,641 / 4,701 / 4,718 / 4,731 | 0.825 / 0.839 / 0.848 / 0.854 | 0.840 / 0.847 / 0.851 / 0.855 | 0.160 / 0.160 / 0.160 / 0.160 | +| traffic_cone | 60 | 24 / 24 / 24 / 24 | 0.000 / 0.000 / 0.000 / 0.000 | 0.028 / 0.029 / 0.029 / 0.029 | 0.077 / 0.153 / 0.153 / 0.153 | +| barrier | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,635 / 1,635 / 1,635 / 1,635 | 0.110 / 0.114 / 0.116 / 0.119 | 0.040 / 0.042 / 0.044 / 0.045 | 0.112 / 0.113 / 0.113 / 0.113 | 0.141 / 0.142 / 0.143 / 0.143 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 131 / 131 / 131 / 131 | 0.140 / 0.171 / 0.175 / 0.180 | 0.035 / 0.039 / 0.040 / 0.043 | 0.128 / 0.132 / 0.134 / 0.134 | 0.229 / 0.223 / 0.223 / 0.222 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 36 / 36 / 36 / 36 | 0.144 / 0.164 / 0.166 / 0.166 | 0.176 / 0.173 / 0.173 / 0.173 | 0.097 / 0.099 / 0.099 / 0.099 | 0.188 / 0.183 / 0.182 / 0.182 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 81 / 81 / 81 / 81 | 0.131 / 0.142 / 0.145 / 0.156 | 0.092 / 0.092 / 0.093 / 0.093 | 0.206 / 0.211 / 0.211 / 0.214 | 0.517 / 0.511 / 0.511 / 0.510 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 556 / 556 / 556 / 556 | 0.096 / 0.099 / 0.105 / 0.123 | 0.290 / 0.291 / 0.294 / 0.299 | 0.204 / 0.204 / 0.205 / 0.205 | 0.240 / 0.240 / 0.240 / 0.244 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 6 / 6 / 6 / 6 | 0.154 / 0.251 / 0.251 / 0.251 | 1.295 / 1.395 / 1.395 / 1.395 | 0.369 / 0.370 / 0.370 / 0.370 | 0.087 / 0.088 / 0.088 / 0.088 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 6,097 / 6,097 / 6,097 / 6,097 | 0.118 / 0.124 / 0.126 / 0.130 | 0.045 / 0.048 / 0.050 / 0.051 | 0.117 / 0.118 / 0.119 / 0.119 | 0.155 / 0.156 / 0.157 / 0.157 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 488 / 488 / 488 / 488 | 0.151 / 0.188 / 0.195 / 0.201 | 0.041 / 0.047 / 0.048 / 0.052 | 0.140 / 0.144 / 0.146 / 0.147 | 0.231 / 0.224 / 0.224 / 0.223 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 137 / 137 / 137 / 137 | 0.157 / 0.184 / 0.186 / 0.186 | 0.206 / 0.196 / 0.195 / 0.195 | 0.096 / 0.099 / 0.099 / 0.099 | 0.192 / 0.184 / 0.183 / 0.183 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 303 / 303 / 303 / 303 | 0.135 / 0.146 / 0.151 / 0.167 | 0.107 / 0.107 / 0.108 / 0.108 | 0.214 / 0.218 / 0.219 / 0.222 | 0.499 / 0.496 / 0.495 / 0.494 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 2,072 / 2,072 / 2,072 / 2,072 | 0.100 / 0.104 / 0.112 / 0.134 | 0.326 / 0.327 / 0.330 / 0.336 | 0.211 / 0.211 / 0.212 / 0.212 | 0.246 / 0.246 / 0.247 / 0.252 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 13,062 / 13,479 / 13,554 / 13,743 | 0.134 / 0.148 / 0.155 / 0.171 | 0.065 / 0.081 / 0.083 / 0.090 | 0.128 / 0.130 / 0.131 / 0.132 | 0.156 / 0.158 / 0.159 / 0.160 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 932 / 1,043 / 1,066 / 1,074 | 0.169 / 0.218 / 0.232 / 0.248 | 0.057 / 0.063 / 0.067 / 0.081 | 0.151 / 0.158 / 0.161 / 0.162 | 0.223 / 0.215 / 0.218 / 0.217 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 275 / 321 / 324 / 324 | 0.176 / 0.238 / 0.247 / 0.247 | 0.192 / 0.169 / 0.167 / 0.167 | 0.097 / 0.105 / 0.105 / 0.105 | 0.184 / 0.184 / 0.182 / 0.182 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 602 / 607 / 608 / 613 | 0.141 / 0.152 / 0.154 / 0.174 | 0.122 / 0.120 / 0.120 / 0.120 | 0.222 / 0.226 / 0.227 / 0.231 | 0.485 / 0.483 / 0.483 / 0.482 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 4,140 / 4,178 / 4,198 / 4,214 | 0.108 / 0.116 / 0.127 / 0.158 | 0.362 / 0.364 / 0.367 / 0.377 | 0.217 / 0.218 / 0.219 / 0.219 | 0.248 / 0.248 / 0.248 / 0.254 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 23 / 13 / 13 / 13 | 0.147 / 0.302 / 0.302 / 0.302 | 1.599 / 1.355 / 1.355 / 1.355 | 0.378 / 0.374 / 0.374 / 0.374 | 0.085 / 0.087 / 0.087 / 0.087 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 50.0 - 90.0m): 0.5080** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 10,929 | 8,721 / 9,616 / 10,038 / 10,208 | 0.724 / 0.828 / 0.872 / 0.891 | 0.802 / 0.862 / 0.880 / 0.884 | 0.216 / 0.185 / 0.177 / 0.175 | +| truck | 1,009 | 649 / 789 / 850 / 868 | 0.527 / 0.699 / 0.770 / 0.785 | 0.661 / 0.785 / 0.824 / 0.829 | 0.229 / 0.154 / 0.134 / 0.134 | +| bus | 141 | 112 / 130 / 131 / 134 | 0.632 / 0.861 / 0.864 / 0.883 | 0.752 / 0.881 / 0.881 / 0.881 | 0.483 / 0.431 / 0.431 / 0.431 | +| bicycle | 460 | 321 / 363 / 373 / 375 | 0.461 / 0.573 / 0.593 / 0.593 | 0.602 / 0.652 / 0.657 / 0.657 | 0.143 / 0.143 / 0.143 / 0.143 | +| pedestrian | 3,721 | 3,077 / 3,133 / 3,157 / 3,180 | 0.646 / 0.667 / 0.673 / 0.683 | 0.696 / 0.707 / 0.712 / 0.717 | 0.119 / 0.122 / 0.122 / 0.122 | +| traffic_cone | 4 | 2 / 2 / 2 / 2 | 0.000 / 0.000 / 0.000 / 0.000 | 0.043 / 0.043 / 0.043 / 0.043 | 0.227 / 0.227 / 0.227 / 0.227 | +| barrier | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,202 / 1,202 / 1,202 / 1,202 | 0.157 / 0.173 / 0.183 / 0.193 | 0.091 / 0.111 / 0.122 / 0.126 | 0.147 / 0.150 / 0.152 / 0.153 | 0.224 / 0.228 / 0.232 / 0.233 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 110 / 110 / 110 / 110 | 0.187 / 0.225 / 0.251 / 0.259 | 0.038 / 0.045 / 0.048 / 0.051 | 0.164 / 0.175 / 0.180 / 0.181 | 0.241 / 0.245 / 0.259 / 0.263 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 15 / 15 / 15 / 15 | 0.225 / 0.269 / 0.271 / 0.278 | 0.493 / 0.444 / 0.443 / 0.439 | 0.100 / 0.114 / 0.115 / 0.115 | 0.155 / 0.185 / 0.188 / 0.191 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 50 / 50 / 50 / 50 | 0.191 / 0.235 / 0.247 / 0.251 | 0.178 / 0.184 / 0.189 / 0.188 | 0.237 / 0.254 / 0.258 / 0.258 | 0.611 / 0.693 / 0.689 / 0.689 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 409 / 409 / 409 / 409 | 0.114 / 0.124 / 0.132 / 0.161 | 0.415 / 0.417 / 0.418 / 0.426 | 0.203 / 0.204 / 0.205 / 0.205 | 0.280 / 0.279 / 0.279 / 0.282 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 0.219 / 0.219 / 0.219 / 0.219 | 2.371 / 2.371 / 2.371 / 2.371 | 0.513 / 0.513 / 0.513 / 0.513 | 0.034 / 0.034 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 4,480 / 4,480 / 4,480 / 4,480 | 0.167 / 0.188 / 0.202 / 0.215 | 0.114 / 0.138 / 0.153 / 0.157 | 0.154 / 0.158 / 0.160 / 0.161 | 0.240 / 0.245 / 0.249 / 0.250 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 413 / 413 / 413 / 413 | 0.197 / 0.253 / 0.289 / 0.300 | 0.045 / 0.054 / 0.058 / 0.064 | 0.173 / 0.187 / 0.193 / 0.194 | 0.248 / 0.252 / 0.266 / 0.272 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 57 / 57 / 57 / 57 | 0.230 / 0.274 / 0.276 / 0.288 | 0.405 / 0.359 / 0.358 / 0.356 | 0.104 / 0.117 / 0.118 / 0.118 | 0.172 / 0.201 / 0.207 / 0.210 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 188 / 188 / 188 / 188 | 0.192 / 0.236 / 0.251 / 0.257 | 0.219 / 0.227 / 0.232 / 0.232 | 0.246 / 0.261 / 0.265 / 0.265 | 0.610 / 0.685 / 0.681 / 0.681 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,525 / 1,525 / 1,525 / 1,525 | 0.123 / 0.136 / 0.149 / 0.189 | 0.463 / 0.464 / 0.465 / 0.475 | 0.209 / 0.210 / 0.211 / 0.212 | 0.297 / 0.295 / 0.295 / 0.298 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1 / 1 / 1 / 1 | 0.205 / 0.205 / 0.205 / 0.205 | 2.328 / 2.328 / 2.328 / 2.328 | 0.517 / 0.517 / 0.517 / 0.517 | 0.034 / 0.034 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 8,159 / 8,918 / 9,145 / 9,193 | 0.179 / 0.214 / 0.238 / 0.253 | 0.166 / 0.202 / 0.219 / 0.223 | 0.162 / 0.168 / 0.169 / 0.170 | 0.252 / 0.272 / 0.279 / 0.280 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 584 / 735 / 782 / 787 | 0.203 / 0.288 / 0.342 / 0.356 | 0.043 / 0.076 / 0.081 / 0.086 | 0.176 / 0.196 / 0.204 / 0.206 | 0.252 / 0.242 / 0.257 / 0.271 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 97 / 115 / 115 / 115 | 0.232 / 0.284 / 0.284 / 0.284 | 0.381 / 0.326 / 0.326 / 0.326 | 0.105 / 0.118 / 0.118 / 0.118 | 0.224 / 0.244 / 0.244 / 0.244 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 243 / 263 / 265 / 265 | 0.192 / 0.233 / 0.240 / 0.247 | 0.198 / 0.205 / 0.204 / 0.203 | 0.244 / 0.257 / 0.259 / 0.259 | 0.588 / 0.651 / 0.648 / 0.648 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 2,464 / 2,492 / 2,508 / 2,524 | 0.127 / 0.141 / 0.156 / 0.198 | 0.474 / 0.473 / 0.475 / 0.484 | 0.211 / 0.212 / 0.213 / 0.214 | 0.302 / 0.300 / 0.300 / 0.302 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 2 / 2 / 2 / 2 | 0.199 / 0.199 / 0.199 / 0.199 | 2.313 / 2.313 / 2.313 / 2.313 | 0.518 / 0.518 / 0.518 / 0.518 | 0.034 / 0.034 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 90.0 - 121.0m): 0.3869** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 2,883 | 2,115 / 2,486 / 2,667 / 2,711 | 0.586 / 0.739 / 0.796 / 0.814 | 0.688 / 0.769 / 0.794 / 0.801 | 0.231 / 0.180 / 0.162 / 0.163 | +| truck | 600 | 314 / 447 / 515 / 531 | 0.307 / 0.600 / 0.741 / 0.769 | 0.497 / 0.688 / 0.776 / 0.790 | 0.257 / 0.234 / 0.120 / 0.120 | +| bus | 60 | 30 / 45 / 47 / 48 | 0.337 / 0.572 / 0.607 / 0.610 | 0.516 / 0.650 / 0.667 / 0.667 | 0.098 / 0.044 / 0.098 / 0.098 | +| bicycle | 85 | 54 / 62 / 65 / 65 | 0.246 / 0.365 / 0.392 / 0.392 | 0.452 / 0.537 / 0.561 / 0.561 | 0.120 / 0.122 / 0.122 / 0.122 | +| pedestrian | 1,092 | 881 / 895 / 900 / 918 | 0.474 / 0.485 / 0.492 / 0.507 | 0.584 / 0.590 / 0.594 / 0.597 | 0.125 / 0.125 / 0.125 / 0.125 | +| traffic_cone | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | +| barrier | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 317 / 317 / 317 / 317 | 0.195 / 0.229 / 0.248 / 0.272 | 0.108 / 0.142 / 0.165 / 0.172 | 0.179 / 0.186 / 0.187 / 0.187 | 0.521 / 0.529 / 0.535 / 0.536 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 66 / 66 / 66 / 66 | 0.222 / 0.321 / 0.382 / 0.402 | 0.045 / 0.050 / 0.057 / 0.062 | 0.180 / 0.191 / 0.200 / 0.203 | 0.177 / 0.168 / 0.166 / 0.173 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 6 / 6 / 6 / 6 | 0.273 / 0.344 / 0.364 / 0.371 | 0.168 / 0.213 / 0.210 / 0.211 | 0.140 / 0.162 / 0.165 / 0.166 | 0.184 / 0.307 / 0.440 / 0.450 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 9 / 9 / 9 / 9 | 0.183 / 0.260 / 0.290 / 0.290 | 0.091 / 0.084 / 0.086 / 0.086 | 0.236 / 0.248 / 0.253 / 0.253 | 0.721 / 0.705 / 0.729 / 0.729 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 120 / 120 / 120 / 120 | 0.131 / 0.137 / 0.152 / 0.207 | 0.398 / 0.398 / 0.400 / 0.407 | 0.172 / 0.172 / 0.172 / 0.173 | 0.354 / 0.355 / 0.355 / 0.364 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,182 / 1,182 / 1,182 / 1,182 | 0.206 / 0.249 / 0.276 / 0.309 | 0.146 / 0.189 / 0.219 / 0.227 | 0.186 / 0.193 / 0.194 / 0.195 | 0.579 / 0.582 / 0.587 / 0.585 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 246 / 246 / 246 / 246 | 0.231 / 0.345 / 0.430 / 0.459 | 0.072 / 0.066 / 0.075 / 0.081 | 0.185 / 0.196 / 0.209 / 0.212 | 0.215 / 0.199 / 0.190 / 0.199 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 24 / 24 / 24 / 24 | 0.291 / 0.384 / 0.416 / 0.425 | 0.269 / 0.312 / 0.301 / 0.298 | 0.146 / 0.175 / 0.178 / 0.179 | 0.315 / 0.475 / 0.689 / 0.695 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 34 / 34 / 34 / 34 | 0.183 / 0.261 / 0.315 / 0.315 | 0.110 / 0.109 / 0.110 / 0.110 | 0.257 / 0.269 / 0.275 / 0.275 | 0.757 / 0.750 / 0.789 / 0.789 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 447 / 447 / 447 / 447 | 0.136 / 0.145 / 0.162 / 0.231 | 0.472 / 0.470 / 0.472 / 0.483 | 0.176 / 0.176 / 0.176 / 0.177 | 0.387 / 0.389 / 0.389 / 0.401 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,763 / 2,103 / 2,227 / 2,243 | 0.207 / 0.261 / 0.299 / 0.338 | 0.155 / 0.220 / 0.266 / 0.271 | 0.188 / 0.196 / 0.197 / 0.198 | 0.571 / 0.603 / 0.613 / 0.613 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 254 / 358 / 448 / 456 | 0.225 / 0.356 / 0.481 / 0.514 | 0.051 / 0.061 / 0.095 / 0.097 | 0.182 / 0.199 / 0.221 / 0.224 | 0.192 / 0.187 / 0.206 / 0.217 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 24 / 38 / 31 / 31 | 0.304 / 0.380 / 0.407 / 0.407 | 0.305 / 0.282 / 0.341 / 0.341 | 0.155 / 0.172 / 0.176 / 0.176 | 0.329 / 0.487 / 0.633 / 0.633 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 38 / 44 / 46 / 46 | 0.178 / 0.253 / 0.304 / 0.304 | 0.113 / 0.102 / 0.102 / 0.102 | 0.252 / 0.263 / 0.267 / 0.267 | 0.725 / 0.699 / 0.717 / 0.717 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 613 / 619 / 623 / 626 | 0.134 / 0.141 / 0.157 / 0.208 | 0.439 / 0.441 / 0.439 / 0.442 | 0.173 / 0.172 / 0.173 / 0.174 | 0.383 / 0.385 / 0.385 / 0.389 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 0.0 - 121.0m): 0.5663** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 28,684 | 24,224 / 25,996 / 26,796 / 27,149 | 0.797 / 0.872 / 0.903 / 0.914 | 0.852 / 0.894 / 0.906 / 0.910 | 0.225 / 0.192 / 0.177 / 0.177 | +| truck | 2,801 | 1,924 / 2,330 / 2,488 / 2,543 | 0.570 / 0.767 / 0.831 / 0.850 | 0.700 / 0.818 / 0.856 / 0.863 | 0.250 / 0.177 / 0.138 / 0.138 | +| bus | 537 | 425 / 504 / 510 / 514 | 0.707 / 0.897 / 0.909 / 0.916 | 0.795 / 0.889 / 0.895 / 0.895 | 0.337 / 0.284 / 0.284 / 0.284 | +| bicycle | 1,285 | 1,072 / 1,141 / 1,158 / 1,167 | 0.666 / 0.740 / 0.755 / 0.762 | 0.727 / 0.759 / 0.764 / 0.769 | 0.200 / 0.200 / 0.150 / 0.150 | +| pedestrian | 9,868 | 8,613 / 8,743 / 8,786 / 8,842 | 0.733 / 0.748 / 0.757 / 0.764 | 0.759 / 0.769 / 0.773 / 0.777 | 0.146 / 0.146 / 0.146 / 0.146 | +| traffic_cone | 64 | 26 / 26 / 26 / 26 | 0.000 / 0.000 / 0.000 / 0.000 | 0.024 / 0.027 / 0.027 / 0.027 | 0.157 / 0.153 / 0.153 / 0.153 | +| barrier | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 3,155 / 3,155 / 3,155 / 3,155 | 0.127 / 0.137 / 0.143 / 0.148 | 0.057 / 0.067 / 0.072 / 0.074 | 0.125 / 0.127 / 0.128 / 0.129 | 0.177 / 0.182 / 0.185 / 0.186 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 308 / 308 / 308 / 308 | 0.165 / 0.209 / 0.229 / 0.237 | 0.038 / 0.043 / 0.046 / 0.050 | 0.147 / 0.155 / 0.159 / 0.160 | 0.223 / 0.219 / 0.223 / 0.225 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 59 / 59 / 59 / 59 | 0.165 / 0.195 / 0.197 / 0.199 | 0.250 / 0.239 / 0.238 / 0.238 | 0.098 / 0.103 / 0.103 / 0.104 | 0.186 / 0.190 / 0.193 / 0.193 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 141 / 141 / 141 / 141 | 0.146 / 0.167 / 0.175 / 0.186 | 0.114 / 0.115 / 0.118 / 0.117 | 0.215 / 0.223 / 0.226 / 0.227 | 0.538 / 0.554 / 0.553 / 0.553 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,085 / 1,085 / 1,085 / 1,085 | 0.103 / 0.109 / 0.116 / 0.140 | 0.338 / 0.339 / 0.342 / 0.348 | 0.205 / 0.206 / 0.206 / 0.207 | 0.257 / 0.257 / 0.257 / 0.262 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 7 / 7 / 7 / 7 | 0.160 / 0.246 / 0.246 / 0.246 | 1.421 / 1.507 / 1.507 / 1.507 | 0.389 / 0.391 / 0.391 / 0.391 | 0.081 / 0.082 / 0.082 / 0.082 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 11,760 / 11,760 / 11,760 / 11,760 | 0.139 / 0.152 / 0.160 / 0.167 | 0.069 / 0.082 / 0.090 / 0.092 | 0.133 / 0.136 / 0.137 / 0.137 | 0.197 / 0.202 / 0.205 / 0.206 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,148 / 1,148 / 1,148 / 1,148 | 0.179 / 0.235 / 0.263 / 0.275 | 0.046 / 0.052 / 0.055 / 0.061 | 0.159 / 0.168 / 0.173 / 0.174 | 0.229 / 0.225 / 0.229 / 0.232 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 220 / 220 / 220 / 220 | 0.181 / 0.217 / 0.220 / 0.223 | 0.254 / 0.238 / 0.237 / 0.237 | 0.099 / 0.106 / 0.106 / 0.106 | 0.184 / 0.190 / 0.194 / 0.195 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 526 / 526 / 526 / 526 | 0.154 / 0.176 / 0.187 / 0.200 | 0.137 / 0.138 / 0.143 / 0.141 | 0.226 / 0.234 / 0.236 / 0.239 | 0.525 / 0.543 / 0.542 / 0.542 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 4,045 / 4,045 / 4,045 / 4,045 | 0.111 / 0.118 / 0.129 / 0.159 | 0.383 / 0.383 / 0.386 / 0.393 | 0.209 / 0.210 / 0.210 / 0.211 | 0.268 / 0.268 / 0.268 / 0.273 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 23,001 / 24,465 / 24,972 / 25,071 | 0.155 / 0.182 / 0.199 / 0.212 | 0.107 / 0.136 / 0.149 / 0.152 | 0.145 / 0.149 / 0.151 / 0.151 | 0.222 / 0.237 / 0.243 / 0.243 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,757 / 2,148 / 2,295 / 2,313 | 0.188 / 0.266 / 0.319 / 0.340 | 0.051 / 0.067 / 0.078 / 0.087 | 0.163 / 0.178 / 0.188 / 0.190 | 0.228 / 0.223 / 0.227 / 0.235 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 391 / 442 / 445 / 445 | 0.196 / 0.242 / 0.248 / 0.248 | 0.248 / 0.230 / 0.228 / 0.228 | 0.102 / 0.110 / 0.110 / 0.110 | 0.191 / 0.192 / 0.209 / 0.209 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 839 / 875 / 938 / 944 | 0.154 / 0.176 / 0.199 / 0.216 | 0.135 / 0.135 / 0.157 / 0.157 | 0.226 / 0.234 / 0.243 / 0.246 | 0.519 / 0.542 / 0.536 / 0.535 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 7,078 / 7,166 / 7,204 / 7,247 | 0.115 / 0.125 / 0.138 / 0.175 | 0.398 / 0.400 / 0.402 / 0.413 | 0.210 / 0.211 / 0.212 / 0.213 | 0.272 / 0.272 / 0.272 / 0.278 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 13 / 15 / 15 / 15 | 0.174 / 0.289 / 0.289 / 0.289 | 1.258 / 1.483 / 1.483 / 1.483 | 0.389 / 0.393 / 0.393 / 0.393 | 0.079 / 0.080 / 0.080 / 0.080 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +--- + +**J6Gen2**: db_j6gen2_v1 + db_j6gen2_v2 + db_j6gen2_v3 + db_j6gen2_v4 + db_j6gen2_v5 + db_j6gen2_v6 + db_j6gen2_v7 + db_j6gen2_v8 + db_j6gen2_v9 + db_j6gen2_v10 + db_j6gen2_v11 + db_j6gen2_v12 (4,682 frames) + + +**Total BEV Center Distance mAP (eval range = 0.0 - 50.0m): 0.7246** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 134,456 | 107,307 / 117,848 / 123,019 / 125,254 | 0.730 / 0.828 / 0.873 / 0.893 | 0.804 / 0.858 / 0.879 / 0.887 | 0.240 / 0.200 / 0.165 / 0.162 | +| truck | 14,900 | 9,814 / 11,683 / 12,829 / 13,364 | 0.527 / 0.690 / 0.786 / 0.828 | 0.667 / 0.766 / 0.817 / 0.841 | 0.225 / 0.182 / 0.170 / 0.145 | +| bus | 4,970 | 3,654 / 4,321 / 4,639 / 4,711 | 0.643 / 0.809 / 0.902 / 0.916 | 0.740 / 0.842 / 0.892 / 0.901 | 0.318 / 0.140 / 0.103 / 0.102 | +| bicycle | 2,486 | 2,153 / 2,228 / 2,239 / 2,239 | 0.755 / 0.796 / 0.801 / 0.802 | 0.789 / 0.809 / 0.809 / 0.812 | 0.186 / 0.164 / 0.164 / 0.164 | +| pedestrian | 29,102 | 25,516 / 26,078 / 26,291 / 26,512 | 0.728 / 0.752 / 0.765 / 0.778 | 0.748 / 0.761 / 0.766 / 0.773 | 0.153 / 0.153 / 0.153 / 0.153 | +| traffic_cone | 11,344 | 7,980 / 8,516 / 8,749 / 9,099 | 0.342 / 0.388 / 0.410 / 0.448 | 0.509 / 0.538 / 0.551 / 0.575 | 0.177 / 0.156 / 0.153 / 0.157 | +| barrier | 2,117 | 737 / 1,055 / 1,164 / 1,216 | 0.104 / 0.236 / 0.275 / 0.290 | 0.323 / 0.439 / 0.467 / 0.479 | 0.151 / 0.155 / 0.115 / 0.115 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 14,790 / 14,790 / 14,790 / 14,790 | 0.129 / 0.145 / 0.156 / 0.167 | 0.064 / 0.082 / 0.095 / 0.099 | 0.129 / 0.133 / 0.134 / 0.135 | 0.151 / 0.153 / 0.154 / 0.155 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,639 / 1,639 / 1,639 / 1,639 | 0.161 / 0.202 / 0.244 / 0.276 | 0.029 / 0.033 / 0.037 / 0.039 | 0.134 / 0.144 / 0.150 / 0.154 | 0.360 / 0.375 / 0.382 / 0.385 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 546 / 546 / 546 / 546 | 0.132 / 0.163 / 0.196 / 0.203 | 0.057 / 0.059 / 0.061 / 0.064 | 0.094 / 0.100 / 0.107 / 0.107 | 0.126 / 0.130 / 0.131 / 0.131 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 273 / 273 / 273 / 273 | 0.146 / 0.154 / 0.155 / 0.158 | 0.079 / 0.081 / 0.082 / 0.083 | 0.195 / 0.197 / 0.197 / 0.197 | 0.593 / 0.594 / 0.595 / 0.596 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 3,201 / 3,201 / 3,201 / 3,201 | 0.110 / 0.118 / 0.133 / 0.160 | 0.455 / 0.459 / 0.464 / 0.469 | 0.238 / 0.239 / 0.239 / 0.240 | 0.245 / 0.245 / 0.246 / 0.248 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,247 / 1,247 / 1,247 / 1,247 | 0.152 / 0.181 / 0.225 / 0.389 | 0.292 / 0.291 / 0.290 / 0.293 | 0.283 / 0.283 / 0.283 / 0.284 | 0.027 / 0.027 / 0.027 / 0.027 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 232 / 232 / 232 / 232 | 0.235 / 0.339 / 0.389 / 0.459 | 0.390 / 0.353 / 0.349 / 0.349 | 0.319 / 0.353 / 0.362 / 0.370 | 0.026 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 55,126 / 55,126 / 55,126 / 55,126 | 0.143 / 0.165 / 0.181 / 0.196 | 0.084 / 0.109 / 0.126 / 0.131 | 0.139 / 0.143 / 0.144 / 0.145 | 0.164 / 0.165 / 0.166 / 0.166 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,109 / 6,109 / 6,109 / 6,109 | 0.177 / 0.231 / 0.287 / 0.332 | 0.037 / 0.042 / 0.047 / 0.049 | 0.144 / 0.155 / 0.164 / 0.169 | 0.386 / 0.403 / 0.410 / 0.414 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,037 / 2,037 / 2,037 / 2,037 | 0.149 / 0.192 / 0.229 / 0.239 | 0.056 / 0.060 / 0.064 / 0.069 | 0.102 / 0.108 / 0.115 / 0.115 | 0.149 / 0.150 / 0.149 / 0.150 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,019 / 1,019 / 1,019 / 1,019 | 0.151 / 0.161 / 0.163 / 0.168 | 0.090 / 0.093 / 0.094 / 0.096 | 0.201 / 0.204 / 0.204 / 0.204 | 0.618 / 0.619 / 0.621 / 0.622 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 11,931 / 11,931 / 11,931 / 11,931 | 0.115 / 0.126 / 0.145 / 0.183 | 0.487 / 0.491 / 0.496 / 0.503 | 0.243 / 0.244 / 0.245 / 0.245 | 0.261 / 0.260 / 0.261 / 0.264 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 4,651 / 4,651 / 4,651 / 4,651 | 0.161 / 0.197 / 0.257 / 0.463 | 0.318 / 0.316 / 0.315 / 0.318 | 0.288 / 0.287 / 0.287 / 0.288 | 0.028 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 867 / 867 / 867 | 1.000 / 0.374 / 0.445 / 0.567 | 1.000 / 0.395 / 0.385 / 0.383 | 1.000 / 0.407 / 0.414 / 0.425 | 1.000 / 0.028 / 0.029 / 0.029 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 100,294 / 109,159 / 113,989 / 115,141 | 0.159 / 0.194 / 0.227 / 0.255 | 0.120 / 0.160 / 0.190 / 0.196 | 0.148 / 0.154 / 0.156 / 0.157 | 0.172 / 0.175 / 0.178 / 0.178 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 8,931 / 10,558 / 11,357 / 11,896 | 0.184 / 0.254 / 0.327 / 0.403 | 0.044 / 0.054 / 0.063 / 0.072 | 0.150 / 0.167 / 0.177 / 0.186 | 0.393 / 0.411 / 0.422 / 0.428 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 3,256 / 3,944 / 4,259 / 4,307 | 0.162 / 0.238 / 0.301 / 0.332 | 0.056 / 0.067 / 0.080 / 0.096 | 0.106 / 0.120 / 0.130 / 0.131 | 0.153 / 0.154 / 0.158 / 0.159 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,785 / 1,879 / 1,880 / 1,887 | 0.153 / 0.168 / 0.168 / 0.179 | 0.096 / 0.105 / 0.105 / 0.108 | 0.202 / 0.207 / 0.207 / 0.207 | 0.628 / 0.634 / 0.635 / 0.637 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 20,949 / 21,293 / 21,452 / 21,637 | 0.119 / 0.132 / 0.153 / 0.200 | 0.497 / 0.502 / 0.508 / 0.516 | 0.245 / 0.246 / 0.247 / 0.247 | 0.270 / 0.269 / 0.270 / 0.274 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 5,511 / 6,135 / 6,340 / 6,540 | 0.156 / 0.191 / 0.247 / 0.443 | 0.303 / 0.308 / 0.309 / 0.312 | 0.284 / 0.286 / 0.286 / 0.286 | 0.028 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 605 / 817 / 935 / 960 | 0.247 / 0.359 / 0.432 / 0.527 | 0.422 / 0.386 / 0.380 / 0.378 | 0.351 / 0.379 / 0.397 / 0.407 | 0.026 / 0.027 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 50.0 - 90.0m): 0.5590** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 54,217 | 40,773 / 46,131 / 48,671 / 49,739 | 0.657 / 0.787 / 0.840 / 0.863 | 0.755 / 0.828 / 0.855 / 0.866 | 0.227 / 0.189 / 0.163 / 0.155 | +| truck | 4,913 | 2,938 / 3,603 / 3,991 / 4,182 | 0.433 / 0.605 / 0.700 / 0.747 | 0.606 / 0.710 / 0.757 / 0.781 | 0.223 / 0.182 / 0.165 / 0.141 | +| bus | 2,116 | 1,374 / 1,777 / 1,950 / 1,994 | 0.516 / 0.753 / 0.876 / 0.900 | 0.645 / 0.798 / 0.865 / 0.881 | 0.329 / 0.140 / 0.140 / 0.110 | +| bicycle | 838 | 666 / 708 / 711 / 711 | 0.617 / 0.681 / 0.684 / 0.688 | 0.693 / 0.729 / 0.730 / 0.737 | 0.164 / 0.164 / 0.164 / 0.164 | +| pedestrian | 8,336 | 6,988 / 7,142 / 7,202 / 7,290 | 0.599 / 0.621 / 0.634 / 0.647 | 0.667 / 0.677 / 0.683 / 0.690 | 0.153 / 0.145 / 0.144 / 0.144 | +| traffic_cone | 2,632 | 1,574 / 1,688 / 1,748 / 1,850 | 0.215 / 0.254 / 0.277 / 0.338 | 0.405 / 0.430 / 0.449 / 0.484 | 0.153 / 0.153 / 0.126 / 0.134 | +| barrier | 622 | 180 / 276 / 305 / 321 | 0.057 / 0.195 / 0.227 / 0.244 | 0.268 / 0.401 / 0.429 / 0.440 | 0.140 / 0.121 / 0.116 / 0.116 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 5,963 / 5,963 / 5,963 / 5,963 | 0.159 / 0.185 / 0.203 / 0.219 | 0.112 / 0.145 / 0.167 / 0.171 | 0.157 / 0.161 / 0.162 / 0.162 | 0.158 / 0.158 / 0.158 / 0.158 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 540 / 540 / 540 / 540 | 0.191 / 0.240 / 0.292 / 0.338 | 0.034 / 0.040 / 0.046 / 0.048 | 0.152 / 0.163 / 0.170 / 0.175 | 0.438 / 0.449 / 0.453 / 0.458 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 232 / 232 / 232 / 232 | 0.168 / 0.223 / 0.268 / 0.281 | 0.117 / 0.109 / 0.113 / 0.119 | 0.119 / 0.126 / 0.133 / 0.133 | 0.131 / 0.131 / 0.130 / 0.130 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 92 / 92 / 92 / 92 | 0.172 / 0.187 / 0.188 / 0.200 | 0.114 / 0.121 / 0.120 / 0.123 | 0.199 / 0.201 / 0.201 / 0.201 | 0.691 / 0.685 / 0.686 / 0.689 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 916 / 916 / 916 / 916 | 0.119 / 0.129 / 0.148 / 0.192 | 0.569 / 0.574 / 0.579 / 0.587 | 0.230 / 0.231 / 0.231 / 0.231 | 0.291 / 0.290 / 0.292 / 0.297 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 289 / 289 / 289 / 289 | 0.165 / 0.201 / 0.289 / 0.670 | 0.269 / 0.283 / 0.286 / 0.308 | 0.290 / 0.290 / 0.291 / 0.292 | 0.038 / 0.038 / 0.038 / 0.039 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 68 / 68 / 68 / 68 | 0.240 / 0.385 / 0.440 / 0.537 | 0.297 / 0.260 / 0.257 / 0.255 | 0.309 / 0.350 / 0.357 / 0.361 | 0.034 / 0.034 / 0.033 / 0.033 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 22,228 / 22,228 / 22,228 / 22,228 | 0.171 / 0.204 / 0.228 / 0.250 | 0.138 / 0.177 / 0.205 / 0.210 | 0.163 / 0.167 / 0.168 / 0.169 | 0.164 / 0.164 / 0.163 / 0.163 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,014 / 2,014 / 2,014 / 2,014 | 0.204 / 0.269 / 0.334 / 0.393 | 0.046 / 0.054 / 0.061 / 0.064 | 0.163 / 0.174 / 0.182 / 0.189 | 0.465 / 0.481 / 0.484 / 0.492 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 867 / 867 / 867 / 867 | 0.187 / 0.263 / 0.322 / 0.341 | 0.089 / 0.087 / 0.097 / 0.108 | 0.125 / 0.134 / 0.142 / 0.143 | 0.141 / 0.137 / 0.135 / 0.135 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 343 / 343 / 343 / 343 | 0.180 / 0.202 / 0.204 / 0.223 | 0.142 / 0.149 / 0.148 / 0.153 | 0.199 / 0.202 / 0.202 / 0.201 | 0.711 / 0.704 / 0.704 / 0.709 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 3,417 / 3,417 / 3,417 / 3,417 | 0.124 / 0.136 / 0.159 / 0.215 | 0.590 / 0.596 / 0.602 / 0.612 | 0.230 / 0.231 / 0.231 / 0.232 | 0.315 / 0.313 / 0.314 / 0.320 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,079 / 1,079 / 1,079 / 1,079 | 0.175 / 0.218 / 0.328 / 0.718 | 0.348 / 0.355 / 0.359 / 0.379 | 0.297 / 0.297 / 0.296 / 0.297 | 0.038 / 0.038 / 0.038 / 0.038 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 255 / 255 / 255 | 1.000 / 0.398 / 0.489 / 0.692 | 1.000 / 0.332 / 0.312 / 0.307 | 1.000 / 0.409 / 0.415 / 0.424 | 1.000 / 0.036 / 0.035 / 0.035 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 37,866 / 42,472 / 44,630 / 45,417 | 0.182 / 0.229 / 0.269 / 0.309 | 0.167 / 0.219 / 0.256 / 0.266 | 0.168 / 0.173 / 0.175 / 0.176 | 0.167 / 0.169 / 0.170 / 0.170 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,598 / 3,145 / 3,407 / 3,593 | 0.206 / 0.281 / 0.355 / 0.432 | 0.047 / 0.061 / 0.072 / 0.081 | 0.165 / 0.181 / 0.190 / 0.200 | 0.477 / 0.495 / 0.499 / 0.513 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,175 / 1,604 / 1,740 / 1,815 | 0.193 / 0.302 / 0.379 / 0.440 | 0.087 / 0.090 / 0.118 / 0.151 | 0.126 / 0.142 / 0.151 / 0.153 | 0.136 / 0.136 / 0.133 / 0.139 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 524 / 551 / 552 / 557 | 0.179 / 0.201 / 0.203 / 0.230 | 0.141 / 0.147 / 0.147 / 0.154 | 0.197 / 0.202 / 0.202 / 0.202 | 0.701 / 0.690 / 0.692 / 0.696 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 5,196 / 5,378 / 5,448 / 5,510 | 0.124 / 0.136 / 0.159 / 0.216 | 0.574 / 0.584 / 0.590 / 0.601 | 0.230 / 0.230 / 0.231 / 0.231 | 0.316 / 0.319 / 0.321 / 0.327 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,014 / 1,077 / 1,237 / 1,290 | 0.166 / 0.203 / 0.318 / 0.694 | 0.293 / 0.300 / 0.335 / 0.360 | 0.291 / 0.291 / 0.293 / 0.294 | 0.038 / 0.038 / 0.038 / 0.039 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 136 / 209 / 227 / 233 | 0.241 / 0.390 / 0.465 / 0.548 | 0.322 / 0.295 / 0.284 / 0.278 | 0.319 / 0.355 / 0.365 / 0.371 | 0.035 / 0.034 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 90.0 - 121.0m): 0.4021** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 19,301 | 13,277 / 15,990 / 17,394 / 17,838 | 0.500 / 0.678 / 0.767 / 0.794 | 0.640 / 0.736 / 0.776 / 0.789 | 0.200 / 0.169 / 0.153 / 0.153 | +| truck | 2,906 | 1,247 / 1,799 / 2,243 / 2,429 | 0.212 / 0.427 / 0.622 / 0.697 | 0.430 / 0.589 / 0.701 / 0.743 | 0.191 / 0.144 / 0.144 / 0.107 | +| bus | 484 | 215 / 322 / 390 / 403 | 0.225 / 0.483 / 0.628 / 0.652 | 0.407 / 0.579 / 0.671 / 0.683 | 0.098 / 0.098 / 0.073 / 0.073 | +| bicycle | 291 | 211 / 238 / 242 / 242 | 0.395 / 0.517 / 0.525 / 0.529 | 0.575 / 0.641 / 0.641 / 0.641 | 0.182 / 0.182 / 0.182 / 0.182 | +| pedestrian | 2,564 | 1,978 / 2,019 / 2,045 / 2,066 | 0.407 / 0.420 / 0.429 / 0.437 | 0.540 / 0.547 / 0.549 / 0.552 | 0.121 / 0.129 / 0.123 / 0.129 | +| traffic_cone | 462 | 218 / 223 / 228 / 251 | 0.114 / 0.117 / 0.124 / 0.151 | 0.322 / 0.322 / 0.332 / 0.353 | 0.133 / 0.133 / 0.144 / 0.133 | +| barrier | 145 | 38 / 65 / 83 / 88 | 0.014 / 0.084 / 0.147 / 0.165 | 0.198 / 0.304 / 0.375 / 0.397 | 0.112 / 0.112 / 0.110 / 0.110 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 2,123 / 2,123 / 2,123 / 2,123 | 0.202 / 0.250 / 0.292 / 0.326 | 0.216 / 0.280 / 0.318 / 0.330 | 0.180 / 0.184 / 0.186 / 0.186 | 0.295 / 0.284 / 0.280 / 0.279 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 319 / 319 / 319 / 319 | 0.225 / 0.328 / 0.457 / 0.534 | 0.043 / 0.051 / 0.066 / 0.073 | 0.172 / 0.193 / 0.210 / 0.219 | 0.442 / 0.467 / 0.477 / 0.484 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 53 / 53 / 53 / 53 | 0.265 / 0.372 / 0.446 / 0.471 | 0.028 / 0.030 / 0.034 / 0.036 | 0.147 / 0.167 / 0.179 / 0.180 | 0.345 / 0.342 / 0.343 / 0.342 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 32 / 32 / 32 / 32 | 0.252 / 0.288 / 0.293 / 0.303 | 0.091 / 0.096 / 0.098 / 0.099 | 0.246 / 0.259 / 0.259 / 0.259 | 0.754 / 0.761 / 0.765 / 0.765 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 282 / 282 / 282 / 282 | 0.123 / 0.134 / 0.153 / 0.197 | 0.505 / 0.506 / 0.511 / 0.518 | 0.250 / 0.250 / 0.251 / 0.252 | 0.389 / 0.390 / 0.390 / 0.393 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 50 / 50 / 50 / 50 | 0.146 / 0.153 / 0.222 / 0.594 | 0.289 / 0.290 / 0.290 / 0.310 | 0.316 / 0.315 / 0.315 / 0.314 | 0.035 / 0.035 / 0.035 / 0.035 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 15 / 15 / 15 / 15 | 0.270 / 0.426 / 0.570 / 0.932 | 0.370 / 0.281 / 0.272 / 0.268 | 0.363 / 0.395 / 0.423 / 0.426 | 0.046 / 0.045 / 0.047 / 0.047 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 7,913 / 7,913 / 7,913 / 7,913 | 0.211 / 0.267 / 0.318 / 0.361 | 0.274 / 0.331 / 0.371 / 0.383 | 0.185 / 0.189 / 0.191 / 0.191 | 0.284 / 0.276 / 0.273 / 0.272 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,191 / 1,191 / 1,191 / 1,191 | 0.243 / 0.362 / 0.521 / 0.621 | 0.091 / 0.076 / 0.093 / 0.099 | 0.189 / 0.205 / 0.222 / 0.232 | 0.501 / 0.524 / 0.522 / 0.529 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 198 / 198 / 198 / 198 | 0.278 / 0.417 / 0.521 / 0.558 | 0.050 / 0.043 / 0.046 / 0.048 | 0.160 / 0.179 / 0.195 / 0.195 | 0.394 / 0.358 / 0.356 / 0.354 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 119 / 119 / 119 / 119 | 0.234 / 0.274 / 0.282 / 0.298 | 0.122 / 0.127 / 0.129 / 0.131 | 0.237 / 0.250 / 0.250 / 0.250 | 0.763 / 0.799 / 0.804 / 0.805 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,051 / 1,051 / 1,051 / 1,051 | 0.131 / 0.145 / 0.169 / 0.219 | 0.569 / 0.570 / 0.576 / 0.584 | 0.247 / 0.248 / 0.248 / 0.249 | 0.421 / 0.420 / 0.421 / 0.425 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 189 / 189 / 189 / 189 | 0.160 / 0.178 / 0.270 / 0.709 | 0.332 / 0.328 / 0.332 / 0.362 | 0.317 / 0.316 / 0.315 / 0.313 | 0.040 / 0.039 / 0.039 / 0.039 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 59 / 59 / 59 | 1.000 / 0.450 / 0.638 / 0.970 | 1.000 / 0.271 / 0.250 / 0.246 | 1.000 / 0.444 / 0.461 / 0.465 | 1.000 / 0.048 / 0.050 / 0.050 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 11,411 / 13,677 / 14,775 / 15,007 | 0.213 / 0.279 / 0.340 / 0.390 | 0.299 / 0.372 / 0.416 / 0.429 | 0.186 / 0.192 / 0.194 / 0.194 | 0.278 / 0.273 / 0.273 / 0.272 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,065 / 1,541 / 1,834 / 2,047 | 0.234 / 0.364 / 0.529 / 0.666 | 0.057 / 0.073 / 0.097 / 0.114 | 0.180 / 0.206 / 0.224 / 0.240 | 0.494 / 0.516 / 0.515 / 0.550 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 164 / 233 / 291 / 296 | 0.279 / 0.412 / 0.546 / 0.582 | 0.044 / 0.044 / 0.050 / 0.052 | 0.155 / 0.178 / 0.198 / 0.198 | 0.340 / 0.321 / 0.363 / 0.358 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 147 / 164 / 164 / 164 | 0.233 / 0.270 / 0.270 / 0.270 | 0.110 / 0.119 / 0.119 / 0.119 | 0.239 / 0.252 / 0.252 / 0.252 | 0.756 / 0.786 / 0.786 / 0.786 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,358 / 1,329 / 1,371 / 1,342 | 0.128 / 0.139 / 0.157 / 0.196 | 0.544 / 0.532 / 0.546 / 0.545 | 0.249 / 0.249 / 0.248 / 0.249 | 0.417 / 0.409 / 0.417 / 0.417 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 135 / 135 / 133 / 148 | 0.142 / 0.142 / 0.216 / 0.596 | 0.300 / 0.300 / 0.303 / 0.308 | 0.313 / 0.313 / 0.312 / 0.313 | 0.035 / 0.035 / 0.035 / 0.035 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 26 / 40 / 50 / 53 | 0.274 / 0.409 / 0.604 / 0.937 | 0.312 / 0.258 / 0.259 / 0.276 | 0.370 / 0.387 / 0.427 / 0.429 | 0.046 / 0.045 / 0.048 / 0.048 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +**Total BEV Center Distance mAP (eval range = 0.0 - 121.0m): 0.6463** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 134,456 | 107,307 / 117,848 / 123,019 / 125,254 | 0.730 / 0.828 / 0.873 / 0.893 | 0.804 / 0.858 / 0.879 / 0.887 | 0.240 / 0.200 / 0.165 / 0.162 | +| truck | 14,900 | 9,814 / 11,683 / 12,829 / 13,364 | 0.527 / 0.690 / 0.786 / 0.828 | 0.667 / 0.766 / 0.817 / 0.841 | 0.225 / 0.182 / 0.170 / 0.145 | +| bus | 4,970 | 3,654 / 4,321 / 4,639 / 4,711 | 0.643 / 0.809 / 0.902 / 0.916 | 0.740 / 0.842 / 0.892 / 0.901 | 0.318 / 0.140 / 0.103 / 0.102 | +| bicycle | 2,486 | 2,153 / 2,228 / 2,239 / 2,239 | 0.755 / 0.796 / 0.801 / 0.802 | 0.789 / 0.809 / 0.809 / 0.812 | 0.186 / 0.164 / 0.164 / 0.164 | +| pedestrian | 29,102 | 25,516 / 26,078 / 26,291 / 26,512 | 0.728 / 0.752 / 0.765 / 0.778 | 0.748 / 0.761 / 0.766 / 0.773 | 0.153 / 0.153 / 0.153 / 0.153 | +| traffic_cone | 11,344 | 7,980 / 8,516 / 8,749 / 9,099 | 0.342 / 0.388 / 0.410 / 0.448 | 0.509 / 0.538 / 0.551 / 0.575 | 0.177 / 0.156 / 0.153 / 0.157 | +| barrier | 2,117 | 737 / 1,055 / 1,164 / 1,216 | 0.104 / 0.236 / 0.275 / 0.290 | 0.323 / 0.439 / 0.467 / 0.479 | 0.151 / 0.155 / 0.115 / 0.115 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 14,790 / 14,790 / 14,790 / 14,790 | 0.129 / 0.145 / 0.156 / 0.167 | 0.064 / 0.082 / 0.095 / 0.099 | 0.129 / 0.133 / 0.134 / 0.135 | 0.151 / 0.153 / 0.154 / 0.155 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,639 / 1,639 / 1,639 / 1,639 | 0.161 / 0.202 / 0.244 / 0.276 | 0.029 / 0.033 / 0.037 / 0.039 | 0.134 / 0.144 / 0.150 / 0.154 | 0.360 / 0.375 / 0.382 / 0.385 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 546 / 546 / 546 / 546 | 0.132 / 0.163 / 0.196 / 0.203 | 0.057 / 0.059 / 0.061 / 0.064 | 0.094 / 0.100 / 0.107 / 0.107 | 0.126 / 0.130 / 0.131 / 0.131 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 273 / 273 / 273 / 273 | 0.146 / 0.154 / 0.155 / 0.158 | 0.079 / 0.081 / 0.082 / 0.083 | 0.195 / 0.197 / 0.197 / 0.197 | 0.593 / 0.594 / 0.595 / 0.596 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 3,201 / 3,201 / 3,201 / 3,201 | 0.110 / 0.118 / 0.133 / 0.160 | 0.455 / 0.459 / 0.464 / 0.469 | 0.238 / 0.239 / 0.239 / 0.240 | 0.245 / 0.245 / 0.246 / 0.248 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,247 / 1,247 / 1,247 / 1,247 | 0.152 / 0.181 / 0.225 / 0.389 | 0.292 / 0.291 / 0.290 / 0.293 | 0.283 / 0.283 / 0.283 / 0.284 | 0.027 / 0.027 / 0.027 / 0.027 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 232 / 232 / 232 / 232 | 0.235 / 0.339 / 0.389 / 0.459 | 0.390 / 0.353 / 0.349 / 0.349 | 0.319 / 0.353 / 0.362 / 0.370 | 0.026 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 55,126 / 55,126 / 55,126 / 55,126 | 0.143 / 0.165 / 0.181 / 0.196 | 0.084 / 0.109 / 0.126 / 0.131 | 0.139 / 0.143 / 0.144 / 0.145 | 0.164 / 0.165 / 0.166 / 0.166 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,109 / 6,109 / 6,109 / 6,109 | 0.177 / 0.231 / 0.287 / 0.332 | 0.037 / 0.042 / 0.047 / 0.049 | 0.144 / 0.155 / 0.164 / 0.169 | 0.386 / 0.403 / 0.410 / 0.414 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,037 / 2,037 / 2,037 / 2,037 | 0.149 / 0.192 / 0.229 / 0.239 | 0.056 / 0.060 / 0.064 / 0.069 | 0.102 / 0.108 / 0.115 / 0.115 | 0.149 / 0.150 / 0.149 / 0.150 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,019 / 1,019 / 1,019 / 1,019 | 0.151 / 0.161 / 0.163 / 0.168 | 0.090 / 0.093 / 0.094 / 0.096 | 0.201 / 0.204 / 0.204 / 0.204 | 0.618 / 0.619 / 0.621 / 0.622 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 11,931 / 11,931 / 11,931 / 11,931 | 0.115 / 0.126 / 0.145 / 0.183 | 0.487 / 0.491 / 0.496 / 0.503 | 0.243 / 0.244 / 0.245 / 0.245 | 0.261 / 0.260 / 0.261 / 0.264 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 4,651 / 4,651 / 4,651 / 4,651 | 0.161 / 0.197 / 0.257 / 0.463 | 0.318 / 0.316 / 0.315 / 0.318 | 0.288 / 0.287 / 0.287 / 0.288 | 0.028 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 867 / 867 / 867 | 1.000 / 0.374 / 0.445 / 0.567 | 1.000 / 0.395 / 0.385 / 0.383 | 1.000 / 0.407 / 0.414 / 0.425 | 1.000 / 0.028 / 0.029 / 0.029 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 100,294 / 109,159 / 113,989 / 115,141 | 0.159 / 0.194 / 0.227 / 0.255 | 0.120 / 0.160 / 0.190 / 0.196 | 0.148 / 0.154 / 0.156 / 0.157 | 0.172 / 0.175 / 0.178 / 0.178 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 8,931 / 10,558 / 11,357 / 11,896 | 0.184 / 0.254 / 0.327 / 0.403 | 0.044 / 0.054 / 0.063 / 0.072 | 0.150 / 0.167 / 0.177 / 0.186 | 0.393 / 0.411 / 0.422 / 0.428 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 3,256 / 3,944 / 4,259 / 4,307 | 0.162 / 0.238 / 0.301 / 0.332 | 0.056 / 0.067 / 0.080 / 0.096 | 0.106 / 0.120 / 0.130 / 0.131 | 0.153 / 0.154 / 0.158 / 0.159 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,785 / 1,879 / 1,880 / 1,887 | 0.153 / 0.168 / 0.168 / 0.179 | 0.096 / 0.105 / 0.105 / 0.108 | 0.202 / 0.207 / 0.207 / 0.207 | 0.628 / 0.634 / 0.635 / 0.637 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 20,949 / 21,293 / 21,452 / 21,637 | 0.119 / 0.132 / 0.153 / 0.200 | 0.497 / 0.502 / 0.508 / 0.516 | 0.245 / 0.246 / 0.247 / 0.247 | 0.270 / 0.269 / 0.270 / 0.274 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 5,511 / 6,135 / 6,340 / 6,540 | 0.156 / 0.191 / 0.247 / 0.443 | 0.303 / 0.308 / 0.309 / 0.312 | 0.284 / 0.286 / 0.286 / 0.286 | 0.028 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 605 / 817 / 935 / 960 | 0.247 / 0.359 / 0.432 / 0.527 | 0.422 / 0.386 / 0.380 / 0.378 | 0.351 / 0.379 / 0.397 / 0.407 | 0.026 / 0.027 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +--- + +**JPNTaxi_Gen2**: db_jpntaxigen2_v1 + db_jpntaxigen2_v2 (10,687 frames) + + +**Total BEV Center Distance mAP (eval range = 0.0 - 50.0m): 0.6595** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 50,954 | 45,644 / 48,317 / 49,034 / 49,324 | 0.858 / 0.925 / 0.946 / 0.948 | 0.900 / 0.942 / 0.948 / 0.950 | 0.249 / 0.199 / 0.191 / 0.161 | +| truck | 18,624 | 15,109 / 16,753 / 17,761 / 18,114 | 0.729 / 0.852 / 0.930 / 0.955 | 0.818 / 0.891 / 0.934 / 0.948 | 0.299 / 0.215 / 0.181 / 0.180 | +| bus | 3,853 | 3,182 / 3,437 / 3,532 / 3,538 | 0.769 / 0.850 / 0.875 / 0.875 | 0.811 / 0.863 / 0.882 / 0.883 | 0.047 / 0.038 / 0.036 / 0.036 | +| bicycle | 3,768 | 3,351 / 3,417 / 3,418 / 3,420 | 0.764 / 0.783 / 0.783 / 0.784 | 0.796 / 0.802 / 0.803 / 0.803 | 0.243 / 0.243 / 0.243 / 0.243 | +| pedestrian | 70,699 | 66,162 / 67,089 / 67,449 / 67,816 | 0.886 / 0.901 / 0.911 / 0.917 | 0.875 / 0.886 / 0.891 / 0.897 | 0.148 / 0.148 / 0.146 / 0.148 | +| traffic_cone | 12,525 | 7,726 / 8,331 / 8,729 / 8,980 | 0.258 / 0.290 / 0.323 / 0.354 | 0.457 / 0.480 / 0.503 / 0.524 | 0.138 / 0.135 / 0.135 / 0.140 | +| barrier | 2,009 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 5,604 / 5,604 / 5,604 / 5,604 | 0.104 / 0.112 / 0.114 / 0.119 | 0.021 / 0.021 / 0.022 / 0.022 | 0.103 / 0.105 / 0.106 / 0.106 | 0.349 / 0.353 / 0.356 / 0.356 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,048 / 2,048 / 2,048 / 2,048 | 0.135 / 0.162 / 0.183 / 0.197 | 0.016 / 0.016 / 0.017 / 0.017 | 0.115 / 0.121 / 0.126 / 0.127 | 0.406 / 0.412 / 0.423 / 0.427 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 423 / 423 / 423 / 423 | 0.154 / 0.163 / 0.168 / 0.169 | 0.042 / 0.041 / 0.041 / 0.041 | 0.105 / 0.109 / 0.110 / 0.110 | 0.303 / 0.315 / 0.317 / 0.318 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 414 / 414 / 414 / 414 | 0.126 / 0.129 / 0.129 / 0.131 | 0.096 / 0.098 / 0.098 / 0.099 | 0.183 / 0.184 / 0.184 / 0.184 | 0.878 / 0.878 / 0.879 / 0.880 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 7,776 / 7,776 / 7,776 / 7,776 | 0.106 / 0.109 / 0.113 / 0.122 | 0.249 / 0.250 / 0.251 / 0.254 | 0.227 / 0.228 / 0.228 / 0.228 | 0.426 / 0.427 / 0.427 / 0.428 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,377 / 1,377 / 1,377 / 1,377 | 0.145 / 0.170 / 0.260 / 0.408 | 0.702 / 0.711 / 0.715 / 0.702 | 0.300 / 0.302 / 0.302 / 0.301 | 0.100 / 0.100 / 0.099 / 0.098 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 20,891 / 20,891 / 20,891 / 20,891 | 0.110 / 0.120 / 0.123 / 0.130 | 0.022 / 0.024 / 0.025 / 0.025 | 0.106 / 0.109 / 0.109 / 0.109 | 0.380 / 0.384 / 0.388 / 0.387 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 7,635 / 7,635 / 7,635 / 7,635 | 0.143 / 0.174 / 0.202 / 0.223 | 0.017 / 0.017 / 0.018 / 0.018 | 0.118 / 0.125 / 0.130 / 0.133 | 0.474 / 0.478 / 0.490 / 0.494 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,579 / 1,579 / 1,579 / 1,579 | 0.157 / 0.171 / 0.180 / 0.181 | 0.034 / 0.034 / 0.034 / 0.034 | 0.108 / 0.113 / 0.116 / 0.116 | 0.359 / 0.369 / 0.371 / 0.372 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,544 / 1,544 / 1,544 / 1,544 | 0.131 / 0.135 / 0.135 / 0.138 | 0.097 / 0.099 / 0.099 / 0.100 | 0.190 / 0.191 / 0.191 / 0.191 | 0.819 / 0.822 / 0.824 / 0.824 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 28,986 / 28,986 / 28,986 / 28,986 | 0.111 / 0.115 / 0.121 / 0.135 | 0.252 / 0.253 / 0.255 / 0.259 | 0.230 / 0.230 / 0.230 / 0.230 | 0.439 / 0.439 / 0.439 / 0.441 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 5,135 / 5,135 / 5,135 / 5,135 | 0.161 / 0.200 / 0.316 / 0.500 | 0.758 / 0.759 / 0.763 / 0.751 | 0.307 / 0.309 / 0.310 / 0.309 | 0.098 / 0.097 / 0.097 / 0.097 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 44,376 / 46,850 / 47,216 / 47,606 | 0.123 / 0.149 / 0.156 / 0.170 | 0.031 / 0.036 / 0.037 / 0.040 | 0.114 / 0.120 / 0.121 / 0.121 | 0.432 / 0.444 / 0.452 / 0.457 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 14,516 / 16,104 / 17,017 / 17,294 | 0.154 / 0.204 / 0.260 / 0.301 | 0.018 / 0.020 / 0.022 / 0.023 | 0.125 / 0.136 / 0.146 / 0.151 | 0.532 / 0.559 / 0.590 / 0.602 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 3,080 / 3,354 / 3,439 / 3,443 | 0.166 / 0.201 / 0.224 / 0.228 | 0.033 / 0.034 / 0.034 / 0.034 | 0.116 / 0.132 / 0.139 / 0.139 | 0.414 / 0.419 / 0.419 / 0.422 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 2,874 / 2,897 / 2,900 / 2,900 | 0.134 / 0.138 / 0.139 / 0.141 | 0.105 / 0.105 / 0.105 / 0.106 | 0.193 / 0.194 / 0.194 / 0.194 | 0.806 / 0.809 / 0.810 / 0.810 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 59,982 / 60,714 / 61,271 / 61,531 | 0.118 / 0.127 / 0.140 / 0.172 | 0.271 / 0.274 / 0.279 / 0.286 | 0.235 / 0.235 / 0.236 / 0.236 | 0.451 / 0.451 / 0.451 / 0.454 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 5,720 / 6,079 / 6,376 / 6,515 | 0.156 / 0.190 / 0.300 / 0.475 | 0.748 / 0.753 / 0.755 / 0.743 | 0.305 / 0.308 / 0.307 / 0.306 | 0.097 / 0.097 / 0.096 / 0.096 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 50.0 - 90.0m): 0.5440** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 41,196 | 33,758 / 37,076 / 38,510 / 38,977 | 0.744 / 0.836 / 0.874 / 0.885 | 0.802 / 0.853 / 0.869 / 0.873 | 0.229 / 0.170 / 0.161 / 0.161 | +| truck | 22,942 | 13,827 / 16,980 / 19,337 / 20,306 | 0.471 / 0.638 / 0.771 / 0.817 | 0.626 / 0.731 / 0.809 / 0.833 | 0.224 / 0.192 / 0.160 / 0.129 | +| bus | 3,033 | 1,552 / 2,141 / 2,485 / 2,543 | 0.286 / 0.506 / 0.667 / 0.693 | 0.455 / 0.616 / 0.712 / 0.727 | 0.078 / 0.069 / 0.074 / 0.074 | +| bicycle | 2,310 | 1,862 / 1,959 / 1,963 / 1,977 | 0.584 / 0.633 / 0.633 / 0.643 | 0.665 / 0.689 / 0.689 / 0.695 | 0.160 / 0.142 / 0.142 / 0.142 | +| pedestrian | 36,881 | 33,364 / 33,924 / 34,094 / 34,294 | 0.764 / 0.779 / 0.790 / 0.799 | 0.772 / 0.781 / 0.785 / 0.791 | 0.159 / 0.148 / 0.148 / 0.148 | +| traffic_cone | 7,183 | 4,785 / 4,935 / 5,020 / 5,158 | 0.317 / 0.337 / 0.366 / 0.399 | 0.492 / 0.504 / 0.523 / 0.541 | 0.135 / 0.135 / 0.125 / 0.125 | +| barrier | 1,847 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 4,531 / 4,531 / 4,531 / 4,531 | 0.141 / 0.154 / 0.162 / 0.171 | 0.034 / 0.040 / 0.043 / 0.045 | 0.137 / 0.139 / 0.140 / 0.141 | 0.319 / 0.346 / 0.360 / 0.366 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,523 / 2,523 / 2,523 / 2,523 | 0.197 / 0.237 / 0.300 / 0.332 | 0.024 / 0.028 / 0.032 / 0.035 | 0.146 / 0.154 / 0.164 / 0.168 | 0.509 / 0.562 / 0.605 / 0.624 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 333 / 333 / 333 / 333 | 0.224 / 0.314 / 0.405 / 0.440 | 0.030 / 0.036 / 0.039 / 0.040 | 0.130 / 0.143 / 0.155 / 0.156 | 0.272 / 0.289 / 0.289 / 0.290 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 254 / 254 / 254 / 254 | 0.186 / 0.198 / 0.199 / 0.245 | 0.094 / 0.094 / 0.094 / 0.091 | 0.230 / 0.231 / 0.231 / 0.229 | 0.847 / 0.861 / 0.861 / 0.861 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 4,056 / 4,056 / 4,056 / 4,056 | 0.116 / 0.121 / 0.133 / 0.162 | 0.296 / 0.299 / 0.303 / 0.307 | 0.261 / 0.262 / 0.262 / 0.262 | 0.464 / 0.464 / 0.465 / 0.468 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 790 / 790 / 790 / 790 | 0.164 / 0.177 / 0.305 / 0.502 | 0.490 / 0.497 / 0.497 / 0.493 | 0.234 / 0.235 / 0.234 / 0.233 | 0.108 / 0.108 / 0.107 / 0.107 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 16,890 / 16,890 / 16,890 / 16,890 | 0.151 / 0.169 / 0.181 / 0.193 | 0.041 / 0.049 / 0.053 / 0.056 | 0.142 / 0.145 / 0.146 / 0.147 | 0.379 / 0.410 / 0.429 / 0.435 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 9,406 / 9,406 / 9,406 / 9,406 | 0.201 / 0.261 / 0.346 / 0.394 | 0.031 / 0.036 / 0.041 / 0.044 | 0.151 / 0.162 / 0.175 / 0.181 | 0.612 / 0.669 / 0.712 / 0.735 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,243 / 1,243 / 1,243 / 1,243 | 0.244 / 0.365 / 0.483 / 0.537 | 0.055 / 0.052 / 0.052 / 0.054 | 0.137 / 0.153 / 0.166 / 0.168 | 0.375 / 0.347 / 0.337 / 0.337 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 947 / 947 / 947 / 947 | 0.188 / 0.205 / 0.207 / 0.264 | 0.116 / 0.115 / 0.115 / 0.109 | 0.238 / 0.238 / 0.238 / 0.237 | 0.884 / 0.895 / 0.896 / 0.899 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 15,121 / 15,121 / 15,121 / 15,121 | 0.121 / 0.128 / 0.142 / 0.182 | 0.314 / 0.318 / 0.323 / 0.328 | 0.262 / 0.263 / 0.263 / 0.263 | 0.486 / 0.485 / 0.487 / 0.491 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 2,945 / 2,945 / 2,945 / 2,945 | 0.171 / 0.188 / 0.318 / 0.540 | 0.468 / 0.477 / 0.474 / 0.473 | 0.232 / 0.234 / 0.234 / 0.233 | 0.102 / 0.102 / 0.102 / 0.102 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 30,777 / 33,945 / 34,775 / 34,956 | 0.164 / 0.200 / 0.222 / 0.240 | 0.054 / 0.071 / 0.078 / 0.080 | 0.149 / 0.155 / 0.156 / 0.157 | 0.447 / 0.505 / 0.535 / 0.541 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 12,711 / 15,140 / 17,099 / 18,052 | 0.205 / 0.278 / 0.393 / 0.478 | 0.033 / 0.043 / 0.054 / 0.063 | 0.154 / 0.168 / 0.188 / 0.200 | 0.655 / 0.740 / 0.812 / 0.867 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,191 / 1,652 / 1,886 / 1,924 | 0.238 / 0.367 / 0.492 / 0.552 | 0.036 / 0.052 / 0.052 / 0.055 | 0.132 / 0.153 / 0.167 / 0.168 | 0.297 / 0.341 / 0.345 / 0.344 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,384 / 1,483 / 1,484 / 1,496 | 0.188 / 0.203 / 0.205 / 0.265 | 0.111 / 0.114 / 0.114 / 0.111 | 0.239 / 0.241 / 0.241 / 0.239 | 0.885 / 0.894 / 0.895 / 0.899 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 27,185 / 28,060 / 28,214 / 28,437 | 0.123 / 0.134 / 0.150 / 0.199 | 0.327 / 0.335 / 0.340 / 0.348 | 0.263 / 0.264 / 0.264 / 0.265 | 0.500 / 0.504 / 0.505 / 0.510 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 3,298 / 3,377 / 3,633 / 3,756 | 0.166 / 0.180 / 0.314 / 0.523 | 0.475 / 0.481 / 0.478 / 0.475 | 0.234 / 0.236 / 0.235 / 0.235 | 0.103 / 0.103 / 0.102 / 0.102 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 90.0 - 121.0m): 0.3700** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 17,510 | 13,179 / 15,034 / 15,801 / 16,115 | 0.586 / 0.706 / 0.755 / 0.770 | 0.663 / 0.735 / 0.759 / 0.764 | 0.164 / 0.129 / 0.125 / 0.125 | +| truck | 14,707 | 6,206 / 9,176 / 11,392 / 12,747 | 0.207 / 0.459 / 0.642 / 0.755 | 0.436 / 0.622 / 0.732 / 0.786 | 0.185 / 0.185 / 0.152 / 0.124 | +| bus | 2,997 | 1,331 / 1,867 / 2,079 / 2,176 | 0.168 / 0.369 / 0.448 / 0.487 | 0.412 / 0.538 / 0.581 / 0.601 | 0.049 / 0.046 / 0.046 / 0.046 | +| bicycle | 566 | 386 / 433 / 446 / 450 | 0.227 / 0.284 / 0.330 / 0.342 | 0.417 / 0.461 / 0.484 / 0.491 | 0.132 / 0.132 / 0.132 / 0.132 | +| pedestrian | 16,580 | 14,554 / 14,780 / 14,883 / 15,028 | 0.679 / 0.697 / 0.704 / 0.720 | 0.712 / 0.720 / 0.725 / 0.734 | 0.136 / 0.136 / 0.136 / 0.136 | +| traffic_cone | 769 | 236 / 251 / 256 / 273 | 0.003 / 0.005 / 0.007 / 0.010 | 0.142 / 0.150 / 0.155 / 0.168 | 0.102 / 0.102 / 0.148 / 0.154 | +| barrier | 566 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,926 / 1,926 / 1,926 / 1,926 | 0.189 / 0.215 / 0.237 / 0.254 | 0.053 / 0.066 / 0.071 / 0.073 | 0.183 / 0.187 / 0.189 / 0.189 | 0.568 / 0.597 / 0.617 / 0.622 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,617 / 1,617 / 1,617 / 1,617 | 0.239 / 0.347 / 0.444 / 0.602 | 0.030 / 0.032 / 0.036 / 0.042 | 0.179 / 0.193 / 0.203 / 0.223 | 0.621 / 0.620 / 0.643 / 0.653 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 329 / 329 / 329 / 329 | 0.252 / 0.359 / 0.439 / 0.498 | 0.027 / 0.033 / 0.036 / 0.041 | 0.135 / 0.142 / 0.155 / 0.162 | 0.188 / 0.219 / 0.248 / 0.256 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 62 / 62 / 62 / 62 | 0.188 / 0.233 / 0.295 / 0.376 | 0.273 / 0.256 / 0.247 / 0.266 | 0.236 / 0.245 / 0.266 / 0.264 | 0.925 / 0.985 / 1.007 / 0.996 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,823 / 1,823 / 1,823 / 1,823 | 0.133 / 0.140 / 0.150 / 0.206 | 0.245 / 0.248 / 0.252 / 0.261 | 0.283 / 0.283 / 0.283 / 0.284 | 0.561 / 0.562 / 0.563 / 0.568 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 84 / 84 / 84 / 84 | 0.214 / 0.252 / 0.380 / 0.734 | 1.068 / 1.069 / 1.057 / 1.038 | 0.369 / 0.369 / 0.368 / 0.363 | 0.191 / 0.190 / 0.192 / 0.196 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 7,179 / 7,179 / 7,179 / 7,179 | 0.202 / 0.238 / 0.266 / 0.290 | 0.066 / 0.085 / 0.090 / 0.093 | 0.187 / 0.192 / 0.194 / 0.194 | 0.633 / 0.667 / 0.691 / 0.698 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,029 / 6,029 / 6,029 / 6,029 | 0.244 / 0.378 / 0.512 / 0.679 | 0.068 / 0.045 / 0.048 / 0.055 | 0.191 / 0.202 / 0.214 / 0.235 | 0.768 / 0.733 / 0.741 / 0.752 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,228 / 1,228 / 1,228 / 1,228 | 0.270 / 0.368 / 0.460 / 0.525 | 0.030 / 0.037 / 0.041 / 0.048 | 0.141 / 0.148 / 0.163 / 0.172 | 0.198 / 0.254 / 0.284 / 0.293 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 232 / 232 / 232 / 232 | 0.194 / 0.249 / 0.291 / 0.366 | 0.306 / 0.275 / 0.267 / 0.279 | 0.224 / 0.234 / 0.248 / 0.246 | 0.939 / 1.037 / 1.056 / 1.050 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 6,797 / 6,797 / 6,797 / 6,797 | 0.139 / 0.148 / 0.162 / 0.233 | 0.281 / 0.284 / 0.288 / 0.300 | 0.280 / 0.280 / 0.280 / 0.281 | 0.574 / 0.574 / 0.576 / 0.582 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 10,800 / 12,644 / 13,151 / 13,260 | 0.205 / 0.254 / 0.291 / 0.316 | 0.068 / 0.095 / 0.102 / 0.105 | 0.187 / 0.194 / 0.196 / 0.196 | 0.661 / 0.706 / 0.737 / 0.745 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 5,665 / 8,075 / 9,757 / 10,776 | 0.241 / 0.377 / 0.531 / 0.726 | 0.043 / 0.045 / 0.053 / 0.068 | 0.185 / 0.204 / 0.220 / 0.244 | 0.716 / 0.746 / 0.803 / 0.839 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,062 / 1,422 / 1,542 / 1,587 | 0.253 / 0.354 / 0.439 / 0.498 | 0.032 / 0.035 / 0.039 / 0.044 | 0.132 / 0.142 / 0.156 / 0.163 | 0.194 / 0.240 / 0.273 / 0.278 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 221 / 245 / 257 / 261 | 0.185 / 0.238 / 0.286 / 0.362 | 0.251 / 0.234 / 0.225 / 0.256 | 0.229 / 0.237 / 0.254 / 0.252 | 0.957 / 1.037 / 1.058 / 1.044 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 11,283 / 11,409 / 11,480 / 11,627 | 0.141 / 0.150 / 0.164 / 0.243 | 0.284 / 0.287 / 0.291 / 0.307 | 0.280 / 0.280 / 0.280 / 0.281 | 0.578 / 0.577 / 0.578 / 0.586 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 167 / 177 / 127 / 133 | 0.210 / 0.249 / 0.392 / 0.736 | 1.018 / 1.021 / 1.158 / 1.135 | 0.365 / 0.365 / 0.381 / 0.374 | 0.190 / 0.191 / 0.198 / 0.204 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +--- + +**Total BEV Center Distance mAP (eval range = 0.0 - 121.0m): 0.5785** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 109,660 | 92,745 / 100,651 / 103,627 / 104,735 | 0.783 / 0.872 / 0.902 / 0.913 | 0.829 / 0.878 / 0.890 / 0.894 | 0.229 / 0.164 / 0.161 / 0.159 | +| truck | 56,273 | 35,229 / 43,057 / 48,764 / 51,517 | 0.495 / 0.675 / 0.803 / 0.862 | 0.647 / 0.762 / 0.837 / 0.865 | 0.249 / 0.192 / 0.166 / 0.136 | +| bus | 9,883 | 6,077 / 7,470 / 8,128 / 8,289 | 0.457 / 0.619 / 0.703 / 0.721 | 0.586 / 0.693 / 0.741 / 0.752 | 0.056 / 0.049 / 0.049 / 0.049 | +| bicycle | 6,644 | 5,610 / 5,822 / 5,840 / 5,859 | 0.666 / 0.697 / 0.701 / 0.705 | 0.723 / 0.736 / 0.738 / 0.740 | 0.202 / 0.198 / 0.202 / 0.202 | +| pedestrian | 124,160 | 114,192 / 115,885 / 116,521 / 117,246 | 0.831 / 0.851 / 0.856 / 0.868 | 0.824 / 0.833 / 0.839 / 0.845 | 0.148 / 0.148 / 0.148 / 0.148 | +| traffic_cone | 20,477 | 12,760 / 13,531 / 14,021 / 14,440 | 0.261 / 0.288 / 0.318 / 0.349 | 0.455 / 0.473 / 0.494 / 0.514 | 0.135 / 0.135 / 0.135 / 0.138 | +| barrier | 4,422 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 12,062 / 12,062 / 12,062 / 12,062 | 0.122 / 0.134 / 0.140 / 0.148 | 0.027 / 0.031 / 0.033 / 0.034 | 0.119 / 0.122 / 0.123 / 0.124 | 0.361 / 0.374 / 0.383 / 0.386 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,190 / 6,190 / 6,190 / 6,190 | 0.171 / 0.216 / 0.267 / 0.315 | 0.020 / 0.022 / 0.025 / 0.027 | 0.134 / 0.144 / 0.152 / 0.159 | 0.476 / 0.504 / 0.534 / 0.549 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,087 / 1,087 / 1,087 / 1,087 | 0.178 / 0.225 / 0.268 / 0.286 | 0.034 / 0.036 / 0.038 / 0.038 | 0.115 / 0.123 / 0.130 / 0.131 | 0.313 / 0.324 / 0.330 / 0.331 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 730 / 730 / 730 / 730 | 0.142 / 0.149 / 0.151 / 0.165 | 0.100 / 0.101 / 0.101 / 0.102 | 0.195 / 0.197 / 0.197 / 0.197 | 0.841 / 0.848 / 0.849 / 0.850 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 13,657 / 13,657 / 13,657 / 13,657 | 0.111 / 0.115 / 0.121 / 0.141 | 0.260 / 0.262 / 0.264 / 0.269 | 0.239 / 0.239 / 0.239 / 0.240 | 0.445 / 0.446 / 0.446 / 0.449 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 2,252 / 2,252 / 2,252 / 2,252 | 0.153 / 0.175 / 0.279 / 0.453 | 0.637 / 0.645 / 0.648 / 0.638 | 0.279 / 0.281 / 0.281 / 0.280 | 0.103 / 0.103 / 0.102 / 0.102 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 44,960 / 44,960 / 44,960 / 44,960 | 0.132 / 0.149 / 0.157 / 0.168 | 0.032 / 0.037 / 0.039 / 0.041 | 0.126 / 0.129 / 0.131 / 0.131 | 0.401 / 0.418 / 0.429 / 0.433 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 23,071 / 23,071 / 23,071 / 23,071 | 0.182 / 0.243 / 0.313 / 0.378 | 0.024 / 0.027 / 0.030 / 0.033 | 0.141 / 0.153 / 0.163 / 0.172 | 0.564 / 0.592 / 0.622 / 0.638 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 4,052 / 4,052 / 4,052 / 4,052 | 0.198 / 0.269 / 0.331 / 0.358 | 0.033 / 0.037 / 0.039 / 0.041 | 0.123 / 0.134 / 0.144 / 0.145 | 0.350 / 0.354 / 0.360 / 0.362 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 2,724 / 2,724 / 2,724 / 2,724 | 0.151 / 0.161 / 0.164 / 0.185 | 0.110 / 0.111 / 0.111 / 0.112 | 0.207 / 0.208 / 0.209 / 0.209 | 0.828 / 0.838 / 0.840 / 0.840 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 50,905 / 50,905 / 50,905 / 50,905 | 0.117 / 0.122 / 0.131 / 0.160 | 0.269 / 0.273 / 0.275 / 0.282 | 0.244 / 0.244 / 0.244 / 0.245 | 0.464 / 0.464 / 0.465 / 0.469 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 8,395 / 8,395 / 8,395 / 8,395 | 0.166 / 0.198 / 0.321 / 0.530 | 0.659 / 0.664 / 0.669 / 0.661 | 0.281 / 0.283 / 0.284 / 0.284 | 0.101 / 0.101 / 0.100 / 0.100 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 85,327 / 93,303 / 94,826 / 95,316 | 0.148 / 0.182 / 0.198 / 0.214 | 0.044 / 0.057 / 0.061 / 0.063 | 0.136 / 0.142 / 0.144 / 0.144 | 0.464 / 0.503 / 0.523 / 0.527 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 32,549 / 39,457 / 43,979 / 46,340 | 0.188 / 0.269 / 0.373 / 0.472 | 0.026 / 0.034 / 0.041 / 0.048 | 0.145 / 0.162 / 0.179 / 0.192 | 0.610 / 0.671 / 0.725 / 0.764 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 5,296 / 6,439 / 6,895 / 6,991 | 0.201 / 0.280 / 0.349 / 0.382 | 0.036 / 0.042 / 0.044 / 0.046 | 0.123 / 0.139 / 0.150 / 0.152 | 0.350 / 0.367 / 0.378 / 0.381 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 4,446 / 4,536 / 4,537 / 4,547 | 0.152 / 0.161 / 0.164 / 0.186 | 0.110 / 0.110 / 0.110 / 0.109 | 0.209 / 0.210 / 0.211 / 0.210 | 0.830 / 0.839 / 0.841 / 0.840 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 98,679 / 99,889 / 100,492 / 101,325 | 0.122 / 0.131 / 0.145 / 0.189 | 0.288 / 0.292 / 0.296 / 0.306 | 0.248 / 0.248 / 0.249 / 0.249 | 0.480 / 0.479 / 0.480 / 0.485 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 9,207 / 9,578 / 10,010 / 10,328 | 0.161 / 0.187 / 0.306 / 0.503 | 0.656 / 0.663 / 0.664 / 0.656 | 0.281 / 0.283 / 0.283 / 0.282 | 0.100 / 0.100 / 0.099 / 0.100 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +
+ +--- + ### BEVFusion-LiDAR base/2.7.0
diff --git a/projects/BEVFusion/docs/BEVFusion-L/v2/j6gen2_base.md b/projects/BEVFusion/docs/BEVFusion-L/v2/j6gen2_base.md index 54e994313..36812924c 100644 --- a/projects/BEVFusion/docs/BEVFusion-L/v2/j6gen2_base.md +++ b/projects/BEVFusion/docs/BEVFusion-L/v2/j6gen2_base.md @@ -13,7 +13,7 @@ - **Total Frames: 5,179**
- j6gen2 (3,951 frames) + j6gen2 (4,682 frames) - `db_j6gen2_v1` - `db_j6gen2_v2` @@ -24,6 +24,9 @@ - `db_j6gen2_v7` - `db_j6gen2_v8` - `db_j6gen2_v9` + - `db_j6gen2_v10` + - `db_j6gen2_v11` + - `db_j6gen2_v12`
@@ -37,7 +40,7 @@
- j6gen2_base (5,179 frames) + j6gen2_base (5,910 frames) - `db_j6gen2_v1` - `db_j6gen2_v2` @@ -48,6 +51,9 @@ - `db_j6gen2_v7` - `db_j6gen2_v8` - `db_j6gen2_v9` + - `db_j6gen2_v10` + - `db_j6gen2_v11` + - `db_j6gen2_v12` - `db_largebus_v1` - `db_largebus_v2` - `db_largebus_v3` @@ -62,43 +68,161 @@
Eval Range: 0.0 - 50.0m - | Model version | mAP | mAPH | car
(64,520) | truck
(6,947) | bus
(2,275) | bicycle
(1,379) | pedestrian
(19,421) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.8828 | 0.8387 | 0.9022 | 0.8627 | 0.9440 | 0.8483 | 0.8569 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.8810 | 0.8380 | 0.8873 | 0.8586 | 0.9476 | 0.8583 | 0.8534 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(75,589) | truck
(8,273) | bus
(2,706) | bicycle
(2,097) | pedestrian
(23,254) | traffic_cone
(8,310) | barrier
(1,350) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.7289 | 0.6808 | 0.6820 | 0.6757 | 0.6579 | 0.6516 | 0.9000 | 0.8398 | 0.9130 | 0.8907 | 0.8535 | 0.4465 | 0.2590 |
Eval Range: 50.0 - 90.0m - | Model version | mAP | mAPH | car
(58,562) | truck
(5,101) | bus
(2,078) | bicycle
(758) | pedestrian
(10,283) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.7193 | 0.6620 | 0.8197 | 0.6856 | 0.8249 | 0.5862 | 0.6801 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.7032 | 0.6483 | 0.7876 | 0.6830 | 0.7911 | 0.5802 | 0.6741 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(64,960) | truck
(5,922) | bus
(2,257) | bicycle
(1,298) | pedestrian
(12,052) | traffic_cone
(2,636) | barrier
(622) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.5802 | 0.5217 | 0.5876 | 0.5690 | 0.5584 | 0.5398 | 0.8127 | 0.6518 | 0.7926 | 0.6527 | 0.6690 | 0.2760 | 0.2064 |
Eval Range: 90.0 - 121.0m - | Model version | mAP | mAPH | car
(20,371) | truck
(3,172) | bus
(376) | bicycle
(155) | pedestrian
(2,794) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.5223 | 0.4757 | 0.6814 | 0.5181 | 0.5381 | 0.4165 | 0.4573 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.4938 | 0.4494 | 0.6564 | 0.5192 | 0.3777 | 0.4406 | 0.4752 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(22,141) | truck
(3,506) | bus
(544) | bicycle
(376) | pedestrian
(3,656) | traffic_cone
(462) | barrier
(145) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.4396 | 0.3969 | 0.5002 | 0.4696 | 0.4789 | 0.4483 | 0.7147 | 0.5324 | 0.5445 | 0.4977 | 0.4993 | 0.1329 | 0.1559 |
Eval Range: 0.0 - 121.0m - | Model version | mAP | mAPH | car
(143,453) | truck
(15,220) | bus
(4,729) | bicycle
(2,292) | pedestrian
(32,498) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.7990 | 0.7487 | 0.8508 | 0.7435 | 0.8711 | 0.7487 | 0.7809 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.7903 | 0.7413 | 0.8266 | 0.7409 | 0.8510 | 0.7541 | 0.7790 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(162,690) | truck
(17,701) | bus
(5,507) | bicycle
(3,771) | pedestrian
(38,962) | traffic_cone
(11,408) | barrier
(2,117) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.6590 | 0.6053 | 0.6391 | 0.6214 | 0.6122 | 0.5946 | 0.8547 | 0.7285 | 0.8389 | 0.7843 | 0.7789 | 0.3955 | 0.2321 |
+### Mean TPError - J6Gen2_base + +- Recalls: `0.10`, `0.40`, `optimal` + +
+ Eval Range: 0.0 - 50.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.1699 | 0.1847 | 0.2714 | 0.1985 | 1.0000 | 0.1878 | 0.2040 | 0.2866 | 0.2091 | 1.0000 | 0.2039 | 0.2176 | 0.2883 | 0.2138 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 75,589) | truck
0.5/1.0/2.0/4.0
(GTs: 8,273) | bus
0.5/1.0/2.0/4.0
(GTs: 2,706) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,097) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 23,254) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,310) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 8,314 / 8,314 / 8,314 / 8,314 | 910 / 910 / 910 / 910 | 297 / 297 / 297 / 297 | 230 / 230 / 230 / 230 | 2,557 / 2,557 / 2,557 / 2,557 | 914 / 914 / 914 / 914 | 148 / 148 / 148 / 148 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 75,589) | truck
0.5/1.0/2.0/4.0
(GTs: 8,273) | bus
0.5/1.0/2.0/4.0
(GTs: 2,706) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,097) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 23,254) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,310) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 30,991 / 30,991 / 30,991 / 30,991 | 3,391 / 3,391 / 3,391 / 3,391 | 1,109 / 1,109 / 1,109 / 1,109 | 859 / 859 / 859 / 859 | 9,534 / 9,534 / 9,534 / 9,534 | 3,407 / 3,407 / 3,407 / 3,407 | 553 / 553 / 553 / 553 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 75,589) | truck
0.5/1.0/2.0/4.0
(GTs: 8,273) | bus
0.5/1.0/2.0/4.0
(GTs: 2,706) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,097) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 23,254) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,310) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 65,297 / 67,749 / 68,945 / 69,400 | 6,298 / 7,033 / 7,266 / 7,405 | 2,282 / 2,494 / 2,565 / 2,589 | 1,789 / 1,815 / 1,822 / 1,825 | 18,701 / 19,099 / 19,305 / 19,357 | 4,529 / 4,841 / 5,046 / 5,561 | 453 / 529 / 550 / 562 | + +
+ +
+ Eval Range: 50.0 - 90.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.2380 | 0.2382 | 0.2946 | 0.2537 | 1.0000 | 0.2943 | 0.2886 | 0.3273 | 0.3009 | 1.0000 | 0.2809 | 0.2785 | 0.3078 | 0.2721 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 64,960) | truck
0.5/1.0/2.0/4.0
(GTs: 5,922) | bus
0.5/1.0/2.0/4.0
(GTs: 2,257) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,298) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 12,052) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,636) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 7,145 / 7,145 / 7,145 / 7,145 | 651 / 651 / 651 / 651 | 248 / 248 / 248 / 248 | 142 / 142 / 142 / 142 | 1,325 / 1,325 / 1,325 / 1,325 | 289 / 289 / 289 / 289 | 68 / 68 / 68 / 68 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 64,960) | truck
0.5/1.0/2.0/4.0
(GTs: 5,922) | bus
0.5/1.0/2.0/4.0
(GTs: 2,257) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,298) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 12,052) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,636) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 26,633 / 26,633 / 26,633 / 26,633 | 2,428 / 2,428 / 2,428 / 2,428 | 925 / 925 / 925 / 925 | 532 / 532 / 532 / 532 | 4,941 / 4,941 / 4,941 / 4,941 | 1,080 / 1,080 / 1,080 / 1,080 | 0 / 255 / 255 / 255 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 64,960) | truck
0.5/1.0/2.0/4.0
(GTs: 5,922) | bus
0.5/1.0/2.0/4.0
(GTs: 2,257) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,298) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 12,052) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,636) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 47,451 / 52,827 / 55,318 / 55,889 | 3,253 / 4,045 / 4,354 / 4,471 | 1,367 / 1,826 / 1,951 / 1,981 | 839 / 846 / 901 / 902 | 8,085 / 8,202 / 8,275 / 8,325 | 1,120 / 1,225 / 1,270 / 1,347 | 156 / 231 / 237 / 268 | + +
+ +
+ Eval Range: 90.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.3252 | 0.2112 | 0.3207 | 0.3389 | 1.0000 | 0.4093 | 0.3020 | 0.3601 | 0.4304 | 1.0000 | 0.3625 | 0.2467 | 0.3279 | 0.3624 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 22,141) | truck
0.5/1.0/2.0/4.0
(GTs: 3,506) | bus
0.5/1.0/2.0/4.0
(GTs: 544) | bicycle
0.5/1.0/2.0/4.0
(GTs: 376) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,656) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 2,435 / 2,435 / 2,435 / 2,435 | 385 / 385 / 385 / 385 | 59 / 59 / 59 / 59 | 41 / 41 / 41 / 41 | 402 / 402 / 402 / 402 | 50 / 50 / 50 / 50 | 15 / 15 / 15 / 15 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 22,141) | truck
0.5/1.0/2.0/4.0
(GTs: 3,506) | bus
0.5/1.0/2.0/4.0
(GTs: 544) | bicycle
0.5/1.0/2.0/4.0
(GTs: 376) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,656) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 9,077 / 9,077 / 9,077 / 9,077 | 1,437 / 1,437 / 1,437 / 1,437 | 223 / 223 / 223 / 223 | 154 / 154 / 154 / 154 | 1,498 / 1,498 / 1,498 / 1,498 | 0 / 189 / 189 / 189 | 0 / 59 / 59 / 59 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 22,141) | truck
0.5/1.0/2.0/4.0
(GTs: 3,506) | bus
0.5/1.0/2.0/4.0
(GTs: 544) | bicycle
0.5/1.0/2.0/4.0
(GTs: 376) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,656) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 13,838 / 16,236 / 17,440 / 17,774 | 1,483 / 1,981 / 2,484 / 2,599 | 192 / 312 / 394 / 405 | 187 / 218 / 223 / 223 | 2,141 / 2,167 / 2,181 / 2,199 | 132 / 147 / 178 / 186 | 33 / 52 / 72 / 73 | + +
+ +
+ Eval Range: 0.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.1972 | 0.2036 | 0.2826 | 0.2208 | 1.0000 | 0.2503 | 0.2473 | 0.3146 | 0.2684 | 1.0000 | 0.2405 | 0.2402 | 0.2976 | 0.2433 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 162,690) | truck
0.5/1.0/2.0/4.0
(GTs: 17,701) | bus
0.5/1.0/2.0/4.0
(GTs: 5,507) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,771) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 38,962) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,408) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 17,895 / 17,895 / 17,895 / 17,895 | 1,947 / 1,947 / 1,947 / 1,947 | 605 / 605 / 605 / 605 | 414 / 414 / 414 / 414 | 4,285 / 4,285 / 4,285 / 4,285 | 1,254 / 1,254 / 1,254 / 1,254 | 232 / 232 / 232 / 232 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 162,690) | truck
0.5/1.0/2.0/4.0
(GTs: 17,701) | bus
0.5/1.0/2.0/4.0
(GTs: 5,507) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,771) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 38,962) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,408) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 66,702 / 66,702 / 66,702 / 66,702 | 7,257 / 7,257 / 7,257 / 7,257 | 2,257 / 2,257 / 2,257 / 2,257 | 1,546 / 1,546 / 1,546 / 1,546 | 15,974 / 15,974 / 15,974 / 15,974 | 4,677 / 4,677 / 4,677 / 4,677 | 0 / 867 / 867 / 867 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 162,690) | truck
0.5/1.0/2.0/4.0
(GTs: 17,701) | bus
0.5/1.0/2.0/4.0
(GTs: 5,507) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,771) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 38,962) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,408) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 126,514 / 137,022 / 141,450 / 143,300 | 10,859 / 13,045 / 14,027 / 14,439 | 3,844 / 4,623 / 4,831 / 4,974 | 2,732 / 2,840 / 2,856 / 2,859 | 28,970 / 29,422 / 29,647 / 29,660 | 5,644 / 6,197 / 6,356 / 7,069 | 602 / 827 / 850 / 871 | + +
+ +
+ ## Datasets
@@ -114,40 +238,154 @@
Eval Range: 0.0 - 50.0m - | Model version | mAP | mAPH | car
(14,883) | truck
(1,193) | bus
(336) | bicycle
(740) | pedestrian
(5,059) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.8947 | 0.8393 | 0.9231 | 0.8893 | 0.9564 | 0.8264 | 0.8782 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.8985 | 0.8484 | 0.9087 | 0.8974 | 0.9636 | 0.8447 | 0.8780 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(14,872) | truck
(1,192) | bus
(336) | bicycle
(740) | pedestrian
(5,055) | traffic_cone
(60) | barrier
(0) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.6313 | 0.6015 | 0.5746 | 0.5493 | 0.5597 | 0.5344 | 0.9156 | 0.8702 | 0.9160 | 0.8586 | 0.8588 | 0.0000 | 0.0000 |
Eval Range: 50.0 - 90.0m - | Model version | mAP | mAPH | car
(10,994) | truck
(1,011) | bus
(143) | bicycle
(463) | pedestrian
(3,754) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.7679 | 0.7089 | 0.8567 | 0.7666 | 0.8723 | 0.5955 | 0.7485 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.7475 | 0.6925 | 0.8317 | 0.7758 | 0.7910 | 0.5959 | 0.7433 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(10,929) | truck
(1,009) | bus
(141) | bicycle
(460) | pedestrian
(3,721) | traffic_cone
(4) | barrier
(0) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.5281 | 0.4877 | 0.4942 | 0.4916 | 0.4740 | 0.4714 | 0.8442 | 0.7108 | 0.8522 | 0.5764 | 0.7129 | 0.0000 | 0.0000 |
Eval Range: 90.0 - 121.0m - | Model version | mAP | mAPH | car
(3,018) | truck
(602) | bus
(60) | bicycle
(85) | pedestrian
(1,121) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.5924 | 0.5370 | 0.7238 | 0.6616 | 0.6305 | 0.3964 | 0.5497 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.5636 | 0.5191 | 0.7125 | 0.6383 | 0.4781 | 0.4293 | 0.5595 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(2,883) | truck
(600) | bus
(60) | bicycle
(85) | pedestrian
(1,092) | traffic_cone
(0) | barrier
(0) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.4172 | 0.3831 | 0.4189 | 0.4104 | 0.4018 | 0.3934 | 0.7548 | 0.6586 | 0.5716 | 0.3759 | 0.5594 | 0.0000 | 0.0000 |
Eval Range: 0.0 - 121.0m - | Model version | mAP | mAPH | car
(28,895) | truck
(2,806) | bus
(539) | bicycle
(1,288) | pedestrian
(9,934) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.8267 | 0.7675 | 0.8888 | 0.8055 | 0.9009 | 0.7334 | 0.8051 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.8198 | 0.7666 | 0.8690 | 0.8052 | 0.8756 | 0.7455 | 0.8036 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(28,684) | truck
(2,801) | bus
(537) | bicycle
(1,285) | pedestrian
(9,868) | traffic_cone
(64) | barrier
(0) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.5779 | 0.5432 | 0.5404 | 0.5154 | 0.5230 | 0.4980 | 0.8813 | 0.7754 | 0.8642 | 0.7410 | 0.7836 | 0.0000 | 0.0000 | + +
+ +- **Mean TPError - LargeBus** + +
+ Eval Range: 0.0 - 50.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.2732 | 0.4747 | 0.3197 | 0.3430 | 1.0000 | 0.3937 | 0.3922 | 0.3996 | 0.4786 | 1.0000 | 0.1883 | 0.3943 | 0.2227 | 0.2329 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 14,872) | truck
0.5/1.0/2.0/4.0
(GTs: 1,192) | bus
0.5/1.0/2.0/4.0
(GTs: 336) | bicycle
0.5/1.0/2.0/4.0
(GTs: 740) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 5,055) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 60) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 1,635 / 1,635 / 1,635 / 1,635 | 131 / 131 / 131 / 131 | 36 / 36 / 36 / 36 | 81 / 81 / 81 / 81 | 556 / 556 / 556 / 556 | 6 / 6 / 6 / 6 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 14,872) | truck
0.5/1.0/2.0/4.0
(GTs: 1,192) | bus
0.5/1.0/2.0/4.0
(GTs: 336) | bicycle
0.5/1.0/2.0/4.0
(GTs: 740) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 5,055) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 60) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 6,097 / 6,097 / 6,097 / 6,097 | 488 / 488 / 488 / 488 | 137 / 137 / 137 / 137 | 303 / 303 / 303 / 303 | 2,072 / 2,072 / 2,072 / 2,072 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 14,872) | truck
0.5/1.0/2.0/4.0
(GTs: 1,192) | bus
0.5/1.0/2.0/4.0
(GTs: 336) | bicycle
0.5/1.0/2.0/4.0
(GTs: 740) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 5,055) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 60) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 13,178 / 13,676 / 13,748 / 13,798 | 925 / 1,041 / 1,064 / 1,073 | 254 / 330 / 333 / 333 | 612 / 628 / 640 / 643 | 4,247 / 4,294 / 4,313 / 4,330 | 19 / 20 / 20 / 21 | 0 / 0 / 0 / 0 | + +
+ +
+ Eval Range: 50.0 - 90.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.3091 | 0.6981 | 0.3081 | 0.3833 | 1.0000 | 0.3181 | 0.6966 | 0.3115 | 0.3980 | 1.0000 | 0.2197 | 0.6583 | 0.2015 | 0.3121 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 10,929) | truck
0.5/1.0/2.0/4.0
(GTs: 1,009) | bus
0.5/1.0/2.0/4.0
(GTs: 141) | bicycle
0.5/1.0/2.0/4.0
(GTs: 460) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,721) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 4) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 1,202 / 1,202 / 1,202 / 1,202 | 110 / 110 / 110 / 110 | 15 / 15 / 15 / 15 | 50 / 50 / 50 / 50 | 409 / 409 / 409 / 409 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 10,929) | truck
0.5/1.0/2.0/4.0
(GTs: 1,009) | bus
0.5/1.0/2.0/4.0
(GTs: 141) | bicycle
0.5/1.0/2.0/4.0
(GTs: 460) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,721) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 4) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 4,480 / 4,480 / 4,480 / 4,480 | 413 / 413 / 413 / 413 | 57 / 57 / 57 / 57 | 188 / 188 / 188 / 188 | 1,525 / 1,525 / 1,525 / 1,525 | 1 / 1 / 1 / 1 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 10,929) | truck
0.5/1.0/2.0/4.0
(GTs: 1,009) | bus
0.5/1.0/2.0/4.0
(GTs: 141) | bicycle
0.5/1.0/2.0/4.0
(GTs: 460) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 3,721) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 4) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 8,463 / 9,288 / 9,554 / 9,621 | 617 / 739 / 799 / 804 | 103 / 124 / 124 / 124 | 263 / 289 / 292 / 292 | 2,604 / 2,652 / 2,667 / 2,682 | 2 / 2 / 2 / 2 | 0 / 0 / 0 / 0 | + +
+ +
+ Eval Range: 90.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.4683 | 0.4107 | 0.4227 | 0.5956 | 1.0000 | 0.4839 | 0.4292 | 0.4259 | 0.6425 | 1.0000 | 0.2920 | 0.2122 | 0.1980 | 0.5124 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 2,883) | truck
0.5/1.0/2.0/4.0
(GTs: 600) | bus
0.5/1.0/2.0/4.0
(GTs: 60) | bicycle
0.5/1.0/2.0/4.0
(GTs: 85) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 1,092) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 0) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 317 / 317 / 317 / 317 | 66 / 66 / 66 / 66 | 6 / 6 / 6 / 6 | 9 / 9 / 9 / 9 | 120 / 120 / 120 / 120 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 2,883) | truck
0.5/1.0/2.0/4.0
(GTs: 600) | bus
0.5/1.0/2.0/4.0
(GTs: 60) | bicycle
0.5/1.0/2.0/4.0
(GTs: 85) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 1,092) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 0) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 1,182 / 1,182 / 1,182 / 1,182 | 246 / 246 / 246 / 246 | 24 / 24 / 24 / 24 | 34 / 34 / 34 / 34 | 447 / 447 / 447 / 447 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 2,883) | truck
0.5/1.0/2.0/4.0
(GTs: 600) | bus
0.5/1.0/2.0/4.0
(GTs: 60) | bicycle
0.5/1.0/2.0/4.0
(GTs: 85) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 1,092) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 0) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 1,929 / 2,144 / 2,236 / 2,337 | 301 / 408 / 466 / 475 | 26 / 32 / 41 / 41 | 39 / 42 / 46 / 46 | 691 / 697 / 700 / 730 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + +
+ +
+ Eval Range: 0.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.2907 | 0.5134 | 0.3242 | 0.3572 | 1.0000 | 0.4150 | 0.4172 | 0.4068 | 0.4972 | 1.0000 | 0.2100 | 0.4414 | 0.2253 | 0.2736 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 28,684) | truck
0.5/1.0/2.0/4.0
(GTs: 2,801) | bus
0.5/1.0/2.0/4.0
(GTs: 537) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,285) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 9,868) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 64) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 3,155 / 3,155 / 3,155 / 3,155 | 308 / 308 / 308 / 308 | 59 / 59 / 59 / 59 | 141 / 141 / 141 / 141 | 1,085 / 1,085 / 1,085 / 1,085 | 7 / 7 / 7 / 7 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 28,684) | truck
0.5/1.0/2.0/4.0
(GTs: 2,801) | bus
0.5/1.0/2.0/4.0
(GTs: 537) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,285) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 9,868) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 64) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 11,760 / 11,760 / 11,760 / 11,760 | 1,148 / 1,148 / 1,148 / 1,148 | 220 / 220 / 220 / 220 | 526 / 526 / 526 / 526 | 4,045 / 4,045 / 4,045 / 4,045 | 0 / 0 / 0 / 0 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 28,684) | truck
0.5/1.0/2.0/4.0
(GTs: 2,801) | bus
0.5/1.0/2.0/4.0
(GTs: 537) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,285) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 9,868) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 64) | barrier
0.5/1.0/2.0/4.0
(GTs: 0) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 23,528 / 24,950 / 25,596 / 25,735 | 1,866 / 2,196 / 2,328 / 2,367 | 379 / 486 / 490 / 490 | 874 / 918 / 941 / 944 | 7,461 / 7,553 / 7,587 / 7,622 | 19 / 22 / 22 / 23 | 0 / 0 / 0 / 0 |
@@ -156,7 +394,7 @@
J6Gen2 -- Datasets (3,951 Testing Frames): +- Datasets (4,682 Testing Frames): - `db_j6gen2_v1` - `db_j6gen2_v2` - `db_j6gen2_v3` @@ -166,46 +404,163 @@ - `db_j6gen2_v7` - `db_j6gen2_v8` - `db_j6gen2_v9` + - `db_j6gen2_v10` + - `db_j6gen2_v11` + - `db_j6gen2_v12` - **Class mAP for BEV Center Distance: 0.5m, 1.0m, 2.0m, 4.0m**
Eval Range: 0.0 - 50.0m - | Model version | mAP | mAPH | car
(49,637) | truck
(5,754) | bus
(1,939) | bicycle
(639) | pedestrian
(14,362) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.8836 | 0.8431 | 0.8942 | 0.8569 | 0.9393 | 0.8780 | 0.8494 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.8788 | 0.8368 | 0.8813 | 0.8505 | 0.9427 | 0.8749 | 0.8448 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(60,938) | truck
(7,081) | bus
(2,370) | bicycle
(1,357) | pedestrian
(18,202) | traffic_cone
(8,250) | barrier
(1,350) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.7371 | 0.6887 | 0.6863 | 0.6799 | 0.6621 | 0.6558 | 0.8940 | 0.8368 | 0.9124 | 0.9072 | 0.8537 | 0.4940 | 0.2617 |
Eval Range: 50.0 - 90.0m - | Model version | mAP | mAPH | car
(47,568) | truck
(4,090) | bus
(1,935) | bicycle
(295) | pedestrian
(6,529) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.7040 | 0.6488 | 0.8118 | 0.6662 | 0.8221 | 0.5781 | 0.6417 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.6864 | 0.6344 | 0.7772 | 0.6609 | 0.7913 | 0.5671 | 0.6357 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(54,217) | truck
(4,913) | bus
(2,116) | bicycle
(838) | pedestrian
(8,336) | traffic_cone
(2,632) | barrier
(622) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.5833 | 0.5245 | 0.5890 | 0.5704 | 0.5596 | 0.5410 | 0.8044 | 0.6387 | 0.7893 | 0.6949 | 0.6496 | 0.2967 | 0.2096 |
Eval Range: 90.0 - 121.0m - | Model version | mAP | mAPH | car
(17,353) | truck
(2,570) | bus
(316) | bicycle
(70) | pedestrian
(1,673) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.5030 | 0.4572 | 0.6739 | 0.4847 | 0.5186 | 0.4430 | 0.3948 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.4766 | 0.4309 | 0.6465 | 0.4903 | 0.3618 | 0.4627 | 0.4214 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(19,301) | truck
(2,906) | bus
(484) | bicycle
(291) | pedestrian
(2,564) | traffic_cone
(462) | barrier
(145) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.4384 | 0.3949 | 0.4973 | 0.4673 | 0.4756 | 0.4456 | 0.7075 | 0.5046 | 0.5412 | 0.5343 | 0.4732 | 0.1509 | 0.1571 | + +
+ +
+ Eval Range: 0.0 - 121.0m + + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(134,456) | truck
(14,900) | bus
(4,970) | bicycle
(2,486) | pedestrian
(29,102) | traffic_cone
(11,344) | barrier
(2,117) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.6650 | 0.6111 | 0.6420 | 0.6241 | 0.6150 | 0.5972 | 0.8448 | 0.7186 | 0.8363 | 0.8063 | 0.7779 | 0.4361 | 0.2350 | + +
+ +- **Mean TPError - J6Gen2** + +
+ Eval Range: 0.0 - 50.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.1692 | 0.1845 | 0.2711 | 0.1983 | 1.0000 | 0.1868 | 0.2021 | 0.2864 | 0.2108 | 1.0000 | 0.2026 | 0.2155 | 0.2887 | 0.2184 | 1.0000 | + +
+ Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 60,938) | truck
0.5/1.0/2.0/4.0
(GTs: 7,081) | bus
0.5/1.0/2.0/4.0
(GTs: 2,370) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,357) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 18,202) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,250) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 6,703 / 6,703 / 6,703 / 6,703 | 778 / 778 / 778 / 778 | 260 / 260 / 260 / 260 | 149 / 149 / 149 / 149 | 2,002 / 2,002 / 2,002 / 2,002 | 907 / 907 / 907 / 907 | 148 / 148 / 148 / 148 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 60,938) | truck
0.5/1.0/2.0/4.0
(GTs: 7,081) | bus
0.5/1.0/2.0/4.0
(GTs: 2,370) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,357) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 18,202) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,250) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 24,984 / 24,984 / 24,984 / 24,984 | 2,903 / 2,903 / 2,903 / 2,903 | 971 / 971 / 971 / 971 | 556 / 556 / 556 / 556 | 7,462 / 7,462 / 7,462 / 7,462 | 3,382 / 3,382 / 3,382 / 3,382 | 553 / 553 / 553 / 553 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 60,938) | truck
0.5/1.0/2.0/4.0
(GTs: 7,081) | bus
0.5/1.0/2.0/4.0
(GTs: 2,370) | bicycle
0.5/1.0/2.0/4.0
(GTs: 1,357) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 18,202) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 8,250) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,350) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 52,338 / 54,149 / 55,189 / 55,565 | 5,350 / 5,996 / 6,202 / 6,337 | 2,027 / 2,173 / 2,227 / 2,232 | 1,170 / 1,181 / 1,182 / 1,182 | 14,547 / 14,883 / 15,058 / 15,106 | 4,519 / 4,942 / 5,249 / 5,546 | 453 / 538 / 550 / 562 | + +
+ +
+ Eval Range: 50.0 - 90.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.2376 | 0.2394 | 0.2946 | 0.2552 | 1.0000 | 0.2952 | 0.2891 | 0.3267 | 0.3020 | 1.0000 | 0.2820 | 0.2779 | 0.3072 | 0.2730 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 54,217) | truck
0.5/1.0/2.0/4.0
(GTs: 4,913) | bus
0.5/1.0/2.0/4.0
(GTs: 2,116) | bicycle
0.5/1.0/2.0/4.0
(GTs: 838) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 8,336) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,632) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 5,963 / 5,963 / 5,963 / 5,963 | 540 / 540 / 540 / 540 | 232 / 232 / 232 / 232 | 92 / 92 / 92 / 92 | 916 / 916 / 916 / 916 | 289 / 289 / 289 / 289 | 68 / 68 / 68 / 68 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 54,217) | truck
0.5/1.0/2.0/4.0
(GTs: 4,913) | bus
0.5/1.0/2.0/4.0
(GTs: 2,116) | bicycle
0.5/1.0/2.0/4.0
(GTs: 838) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 8,336) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,632) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 22,228 / 22,228 / 22,228 / 22,228 | 2,014 / 2,014 / 2,014 / 2,014 | 867 / 867 / 867 / 867 | 343 / 343 / 343 / 343 | 3,417 / 3,417 / 3,417 / 3,417 | 1,079 / 1,079 / 1,079 / 1,079 | 0 / 255 / 255 / 255 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 54,217) | truck
0.5/1.0/2.0/4.0
(GTs: 4,913) | bus
0.5/1.0/2.0/4.0
(GTs: 2,116) | bicycle
0.5/1.0/2.0/4.0
(GTs: 838) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 8,336) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 2,632) | barrier
0.5/1.0/2.0/4.0
(GTs: 622) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 39,059 / 43,688 / 45,797 / 46,308 | 2,628 / 3,307 / 3,561 / 3,660 | 1,261 / 1,700 / 1,825 / 1,855 | 584 / 579 / 581 / 611 | 5,589 / 5,531 / 5,588 / 5,745 | 1,142 / 1,223 / 1,314 / 1,394 | 156 / 231 / 237 / 268 |
+ Eval Range: 90.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.3293 | 0.2166 | 0.3253 | 0.3476 | 1.0000 | 0.4132 | 0.3083 | 0.3639 | 0.4336 | 1.0000 | 0.3657 | 0.2567 | 0.3324 | 0.3634 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 19,301) | truck
0.5/1.0/2.0/4.0
(GTs: 2,906) | bus
0.5/1.0/2.0/4.0
(GTs: 484) | bicycle
0.5/1.0/2.0/4.0
(GTs: 291) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 2,564) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 2,123 / 2,123 / 2,123 / 2,123 | 319 / 319 / 319 / 319 | 53 / 53 / 53 / 53 | 32 / 32 / 32 / 32 | 282 / 282 / 282 / 282 | 50 / 50 / 50 / 50 | 15 / 15 / 15 / 15 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 19,301) | truck
0.5/1.0/2.0/4.0
(GTs: 2,906) | bus
0.5/1.0/2.0/4.0
(GTs: 484) | bicycle
0.5/1.0/2.0/4.0
(GTs: 291) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 2,564) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 7,913 / 7,913 / 7,913 / 7,913 | 1,191 / 1,191 / 1,191 / 1,191 | 198 / 198 / 198 / 198 | 119 / 119 / 119 / 119 | 1,051 / 1,051 / 1,051 / 1,051 | 0 / 189 / 189 / 189 | 0 / 59 / 59 / 59 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 19,301) | truck
0.5/1.0/2.0/4.0
(GTs: 2,906) | bus
0.5/1.0/2.0/4.0
(GTs: 484) | bicycle
0.5/1.0/2.0/4.0
(GTs: 291) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 2,564) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 462) | barrier
0.5/1.0/2.0/4.0
(GTs: 145) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 12,130 / 14,043 / 15,222 / 15,426 | 1,162 / 1,568 / 2,007 / 2,112 | 169 / 279 / 355 / 366 | 153 / 180 / 181 / 181 | 1,439 / 1,486 / 1,498 / 1,509 | 155 / 172 / 178 / 186 | 33 / 52 / 72 / 73 | + +
+ +
Eval Range: 0.0 - 121.0m - | Model version | mAP | mAPH | car
(114,558) | truck
(12,414) | bus
(4,190) | bicycle
(1,004) | pedestrian
(22,564) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR j6gen2_base/2.7.1 | 0.7958 | 0.7472 | 0.8408 | 0.7294 | 0.8673 | 0.7710 | 0.7706 | - | BEVFusion-LiDAR j6gen2_base/2.6.1 | 0.7851 | 0.7375 | 0.8166 | 0.7262 | 0.8481 | 0.7661 | 0.7687 | + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 0.1971 | 0.2029 | 0.2829 | 0.2224 | 1.0000 | 0.2506 | 0.2464 | 0.3150 | 0.2717 | 1.0000 | 0.2419 | 0.2401 | 0.2986 | 0.2465 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 134,456) | truck
0.5/1.0/2.0/4.0
(GTs: 14,900) | bus
0.5/1.0/2.0/4.0
(GTs: 4,970) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,486) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 29,102) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,344) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 14,790 / 14,790 / 14,790 / 14,790 | 1,639 / 1,639 / 1,639 / 1,639 | 546 / 546 / 546 / 546 | 273 / 273 / 273 / 273 | 3,201 / 3,201 / 3,201 / 3,201 | 1,247 / 1,247 / 1,247 / 1,247 | 232 / 232 / 232 / 232 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 134,456) | truck
0.5/1.0/2.0/4.0
(GTs: 14,900) | bus
0.5/1.0/2.0/4.0
(GTs: 4,970) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,486) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 29,102) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,344) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 55,126 / 55,126 / 55,126 / 55,126 | 6,109 / 6,109 / 6,109 / 6,109 | 2,037 / 2,037 / 2,037 / 2,037 | 1,019 / 1,019 / 1,019 / 1,019 | 11,931 / 11,931 / 11,931 / 11,931 | 4,651 / 4,651 / 4,651 / 4,651 | 0 / 867 / 867 / 867 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 134,456) | truck
0.5/1.0/2.0/4.0
(GTs: 14,900) | bus
0.5/1.0/2.0/4.0
(GTs: 4,970) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,486) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 29,102) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 11,344) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,117) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR j6gen2_base/2.8.1 | 102,297 / 112,278 / 116,527 / 117,516 | 9,001 / 10,847 / 11,695 / 12,090 | 3,495 / 4,146 / 4,350 / 4,483 | 1,859 / 1,919 / 1,923 / 1,923 | 21,639 / 21,986 / 21,863 / 22,136 | 5,782 / 6,632 / 6,752 / 7,077 | 602 / 827 / 850 / 872 |
@@ -213,6 +568,745 @@ ## Release +### BEVFusion-LiDAR J6Gen2_base/2.8.1 + +
+ Changes + +- Finetune from `BEVFusion-LiDAR base/2.8.0` with j6gen2 base dataset and intensity. +
+ +
+ Artifacts + +- Deployed onnx and ROS parameter files (for internal) + - [WebAuto](https://evaluation.ci.tier4.jp/evaluation/mlpackages/46f8188d-e3be-4f2f-b989-fd27002610d7/releases/fcf081e7-b3a9-4085-82f8-60023df3e854?project_id=zWhWRzei) + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/j6gen2_base/v2.8.1/deployment.zip) + - [Google drive](https://drive.google.com/file/d/1VwFa3BZnDI7WV1i3aq6VYsK3pII2axMb/view?usp=drive_link) +- Logs (for internal) + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/j6gen2_base/v2.8.1/logs.zip) + - [Google drive](https://drive.google.com/file/d/1n1EZUOMF6PKi9SciRQXzoMvCBkMnQaYL/view?usp=drive_link) +- Pytorch Best checkpoints: + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/j6gen2_base/v2.8.1/best_epoch_25.zip) + - [Google drive](https://drive.google.com/file/d/1mOVIs7rUGPumjl3dosuNZqJGZlNOdV-e/view?usp=drive_link) + +
+ +
+ Training configs + +- [Config file path](https://github.com/KSeangTan/AWML/blob/3d5e2fa3df7ad61d9ae773a3ea3f418f4916e05b/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m.py) +- Train time: NVIDIA H200 140GB * 8 * 30 epochs = 20 hours +- Batch size: 8*16 = 128 +- Training Dataset (frames: 63,813): + - j6gen2: db_j6gen2_v1 + db_j6gen2_v2 + db_j6gen2_v3 + db_j6gen2_v4 + db_j6gen2_v5 + db_j6gen2_v6 + db_j6gen2_v7 + db_j6gen2_v8 + db_j6gen2_v9 + db_j6gen2_v10 + db_j6gen2_v11 + db_j6gen2_v12 (51,208 frames) + - largebus: db_largebus_v1 + db_largebus_v2 + db_largebus_v3 (12,605 frames) + +
+ +
+ Evaluation + +**J6Gen2_base Datasets (5,910 frames)**: + + - j6gen2 (3,951 frames): db_j6gen2_v1 + db_j6gen2_v2 + db_j6gen2_v3 + db_j6gen2_v4 + db_j6gen2_v5 + db_j6gen2_v6 + db_j6gen2_v7 + db_j6gen2_v8 + db_j6gen2_v9 + db_j6gen2_v10 + db_j6gen2_v11 + db_j6gen2_v12 + - largebus (1,228 frames): db_largebus_v1 + db_largebus_v2 + db_largebus_v3 + +**Total BEV Center Distance mAP (eval range = 0.0 - 50.0m): 0.7289** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 75,589 | 67,025 / 69,724 / 70,866 / 71,572 | 0.855 / 0.903 / 0.915 / 0.927 | 0.904 / 0.929 / 0.937 / 0.942 | 0.279 / 0.218 / 0.158 / 0.147 | +| truck | 8,273 | 6,615 / 7,329 / 7,646 / 7,835 | 0.715 / 0.838 / 0.889 / 0.917 | 0.802 / 0.875 / 0.903 / 0.920 | 0.263 / 0.191 / 0.186 / 0.184 | +| bus | 2,706 | 2,339 / 2,562 / 2,628 / 2,640 | 0.810 / 0.916 / 0.962 / 0.963 | 0.874 / 0.943 / 0.962 / 0.965 | 0.260 / 0.167 / 0.130 / 0.096 | +| bicycle | 2,097 | 1,950 / 1,979 / 1,992 / 1,996 | 0.866 / 0.894 / 0.898 / 0.905 | 0.877 / 0.889 / 0.893 / 0.894 | 0.158 / 0.157 / 0.157 / 0.157 | +| pedestrian | 23,254 | 21,368 / 21,777 / 21,940 / 22,071 | 0.828 / 0.852 / 0.864 / 0.870 | 0.833 / 0.846 / 0.852 / 0.857 | 0.171 / 0.166 / 0.163 / 0.166 | +| traffic_cone | 8,310 | 5,479 / 5,915 / 6,096 / 6,331 | 0.385 / 0.444 / 0.463 / 0.494 | 0.559 / 0.594 / 0.607 / 0.624 | 0.123 / 0.121 / 0.111 / 0.086 | +| barrier | 1,350 | 572 / 754 / 803 / 843 | 0.174 / 0.267 / 0.289 / 0.306 | 0.409 / 0.462 / 0.472 / 0.483 | 0.283 / 0.260 / 0.248 / 0.248 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 8,314 / 8,314 / 8,314 / 8,314 | 0.107 / 0.112 / 0.113 / 0.117 | 0.033 / 0.036 / 0.036 / 0.037 | 0.116 / 0.117 / 0.118 / 0.118 | 0.126 / 0.127 / 0.128 / 0.128 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 910 / 910 / 910 / 910 | 0.144 / 0.165 / 0.181 / 0.200 | 0.028 / 0.030 / 0.031 / 0.031 | 0.127 / 0.132 / 0.135 / 0.137 | 0.297 / 0.306 / 0.311 / 0.310 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 297 / 297 / 297 / 297 | 0.108 / 0.121 / 0.142 / 0.143 | 0.044 / 0.045 / 0.045 / 0.045 | 0.083 / 0.085 / 0.091 / 0.091 | 0.128 / 0.130 / 0.129 / 0.129 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 230 / 230 / 230 / 230 | 0.131 / 0.137 / 0.138 / 0.140 | 0.080 / 0.080 / 0.080 / 0.081 | 0.202 / 0.204 / 0.204 / 0.205 | 0.537 / 0.536 / 0.535 / 0.536 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 2,557 / 2,557 / 2,557 / 2,557 | 0.102 / 0.108 / 0.117 / 0.133 | 0.395 / 0.397 / 0.401 / 0.404 | 0.232 / 0.233 / 0.234 / 0.234 | 0.240 / 0.239 / 0.239 / 0.241 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 914 / 914 / 914 / 914 | 0.176 / 0.198 / 0.219 / 0.297 | 0.328 / 0.325 / 0.327 / 0.329 | 0.644 / 0.648 / 0.649 / 0.650 | 0.026 / 0.026 / 0.026 / 0.026 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 148 / 148 / 148 / 148 | 0.232 / 0.293 / 0.318 / 0.363 | 0.374 / 0.376 / 0.378 / 0.375 | 0.458 / 0.477 / 0.484 / 0.492 | 0.024 / 0.025 / 0.025 / 0.025 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 30,991 / 30,991 / 30,991 / 30,991 | 0.115 / 0.121 / 0.124 / 0.128 | 0.037 / 0.041 / 0.043 / 0.044 | 0.121 / 0.123 / 0.123 / 0.123 | 0.139 / 0.140 / 0.141 / 0.141 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 3,391 / 3,391 / 3,391 / 3,391 | 0.153 / 0.181 / 0.202 / 0.230 | 0.032 / 0.034 / 0.035 / 0.036 | 0.133 / 0.138 / 0.142 / 0.145 | 0.317 / 0.325 / 0.331 / 0.330 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,109 / 1,109 / 1,109 / 1,109 | 0.118 / 0.136 / 0.152 / 0.154 | 0.051 / 0.052 / 0.052 / 0.052 | 0.086 / 0.089 / 0.094 / 0.094 | 0.147 / 0.147 / 0.147 / 0.147 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 859 / 859 / 859 / 859 | 0.132 / 0.138 / 0.139 / 0.142 | 0.087 / 0.087 / 0.087 / 0.087 | 0.207 / 0.209 / 0.209 / 0.210 | 0.555 / 0.554 / 0.553 / 0.553 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 9,534 / 9,534 / 9,534 / 9,534 | 0.107 / 0.114 / 0.127 / 0.149 | 0.427 / 0.429 / 0.433 / 0.437 | 0.239 / 0.240 / 0.240 / 0.241 | 0.244 / 0.244 / 0.244 / 0.245 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 3,407 / 3,407 / 3,407 / 3,407 | 0.186 / 0.215 / 0.249 / 0.353 | 0.376 / 0.370 / 0.370 / 0.371 | 0.654 / 0.659 / 0.660 / 0.661 | 0.028 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 553 / 553 / 553 / 553 | 0.251 / 0.333 / 0.368 / 0.443 | 0.422 / 0.406 / 0.410 / 0.403 | 0.542 / 0.541 / 0.547 / 0.554 | 0.025 / 0.025 / 0.025 / 0.025 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 65,297 / 67,749 / 68,945 / 69,400 | 0.130 / 0.146 / 0.159 / 0.175 | 0.052 / 0.063 / 0.072 / 0.076 | 0.131 / 0.133 / 0.135 / 0.135 | 0.149 / 0.152 / 0.154 / 0.154 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,298 / 7,033 / 7,266 / 7,405 | 0.164 / 0.211 / 0.246 / 0.296 | 0.041 / 0.053 / 0.054 / 0.058 | 0.142 / 0.153 / 0.158 / 0.163 | 0.313 / 0.324 / 0.331 / 0.331 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,282 / 2,494 / 2,565 / 2,589 | 0.138 / 0.181 / 0.205 / 0.217 | 0.065 / 0.069 / 0.073 / 0.077 | 0.095 / 0.102 / 0.106 / 0.107 | 0.173 / 0.166 / 0.166 / 0.170 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,789 / 1,815 / 1,822 / 1,825 | 0.136 / 0.144 / 0.149 / 0.153 | 0.097 / 0.097 / 0.097 / 0.097 | 0.214 / 0.216 / 0.217 / 0.218 | 0.550 / 0.548 / 0.547 / 0.547 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 18,701 / 19,099 / 19,305 / 19,357 | 0.113 / 0.125 / 0.144 / 0.177 | 0.453 / 0.457 / 0.462 / 0.467 | 0.244 / 0.246 / 0.247 / 0.247 | 0.250 / 0.249 / 0.250 / 0.252 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 4,529 / 4,841 / 5,046 / 5,561 | 0.187 / 0.217 / 0.256 / 0.403 | 0.385 / 0.383 / 0.382 / 0.387 | 0.654 / 0.659 / 0.662 / 0.669 | 0.028 / 0.028 / 0.028 / 0.029 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 453 / 529 / 550 / 562 | 0.240 / 0.298 / 0.323 / 0.375 | 0.394 / 0.397 / 0.395 / 0.389 | 0.491 / 0.504 / 0.510 / 0.516 | 0.023 / 0.024 / 0.024 / 0.024 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +**Total BEV Center Distance mAP (eval range = 50.0 - 90.0m): 0.5802** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 64,960 | 50,716 / 56,494 / 59,237 / 60,392 | 0.695 / 0.809 / 0.866 / 0.881 | 0.781 / 0.848 / 0.874 / 0.883 | 0.240 / 0.190 / 0.157 / 0.157 | +| truck | 5,922 | 3,638 / 4,443 / 4,919 / 5,132 | 0.459 / 0.634 / 0.737 / 0.777 | 0.625 / 0.731 / 0.786 / 0.805 | 0.249 / 0.165 / 0.164 / 0.159 | +| bus | 2,257 | 1,543 / 1,947 / 2,104 / 2,161 | 0.565 / 0.797 / 0.891 / 0.917 | 0.681 / 0.830 / 0.883 / 0.900 | 0.415 / 0.184 / 0.171 / 0.181 | +| bicycle | 1,298 | 986 / 1,068 / 1,079 / 1,080 | 0.576 / 0.670 / 0.682 / 0.683 | 0.683 / 0.722 / 0.726 / 0.727 | 0.110 / 0.135 / 0.106 / 0.106 | +| pedestrian | 12,052 | 10,341 / 10,570 / 10,667 / 10,768 | 0.642 / 0.664 / 0.679 / 0.692 | 0.694 / 0.705 / 0.711 / 0.716 | 0.145 / 0.145 / 0.145 / 0.146 | +| traffic_cone | 2,636 | 1,308 / 1,442 / 1,510 / 1,614 | 0.214 / 0.260 / 0.291 / 0.339 | 0.436 / 0.477 / 0.493 / 0.523 | 0.085 / 0.085 / 0.084 / 0.084 | +| barrier | 622 | 216 / 296 / 314 / 328 | 0.117 / 0.222 / 0.239 / 0.248 | 0.333 / 0.423 / 0.434 / 0.439 | 0.183 / 0.106 / 0.106 / 0.082 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 7,145 / 7,145 / 7,145 / 7,145 | 0.158 / 0.178 / 0.194 / 0.207 | 0.108 / 0.135 / 0.153 / 0.156 | 0.158 / 0.161 / 0.162 / 0.162 | 0.158 / 0.159 / 0.160 / 0.161 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 651 / 651 / 651 / 651 | 0.191 / 0.240 / 0.292 / 0.328 | 0.035 / 0.039 / 0.042 / 0.044 | 0.155 / 0.167 / 0.174 / 0.178 | 0.425 / 0.429 / 0.434 / 0.439 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 248 / 248 / 248 / 248 | 0.156 / 0.206 / 0.236 / 0.248 | 0.149 / 0.137 / 0.139 / 0.146 | 0.114 / 0.123 / 0.128 / 0.129 | 0.146 / 0.153 / 0.153 / 0.153 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 142 / 142 / 142 / 142 | 0.174 / 0.203 / 0.209 / 0.213 | 0.140 / 0.144 / 0.144 / 0.145 | 0.217 / 0.226 / 0.227 / 0.227 | 0.642 / 0.675 / 0.673 / 0.672 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,325 / 1,325 / 1,325 / 1,325 | 0.116 / 0.125 / 0.144 / 0.184 | 0.544 / 0.550 / 0.554 / 0.563 | 0.220 / 0.221 / 0.221 / 0.222 | 0.290 / 0.289 / 0.290 / 0.294 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 289 / 289 / 289 / 289 | 0.190 / 0.223 / 0.313 / 0.573 | 0.272 / 0.284 / 0.286 / 0.309 | 0.685 / 0.691 / 0.692 / 0.692 | 0.043 / 0.044 / 0.044 / 0.044 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 68 / 68 / 68 / 68 | 0.247 / 0.336 / 0.361 / 0.416 | 0.375 / 0.360 / 0.359 / 0.359 | 0.456 / 0.473 / 0.481 / 0.485 | 0.032 / 0.033 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 26,633 / 26,633 / 26,633 / 26,633 | 0.169 / 0.196 / 0.217 / 0.235 | 0.129 / 0.164 / 0.187 / 0.190 | 0.164 / 0.167 / 0.169 / 0.169 | 0.167 / 0.168 / 0.169 / 0.170 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,428 / 2,428 / 2,428 / 2,428 | 0.202 / 0.264 / 0.329 / 0.375 | 0.045 / 0.048 / 0.052 / 0.055 | 0.165 / 0.178 / 0.186 / 0.191 | 0.441 / 0.450 / 0.455 / 0.462 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 925 / 925 / 925 / 925 | 0.173 / 0.240 / 0.281 / 0.298 | 0.107 / 0.104 / 0.112 / 0.124 | 0.123 / 0.132 / 0.139 / 0.140 | 0.163 / 0.169 / 0.167 / 0.167 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 532 / 532 / 532 / 532 | 0.181 / 0.209 / 0.218 / 0.224 | 0.169 / 0.174 / 0.175 / 0.175 | 0.223 / 0.230 / 0.231 / 0.231 | 0.661 / 0.683 / 0.680 / 0.680 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 4,941 / 4,941 / 4,941 / 4,941 | 0.122 / 0.134 / 0.156 / 0.204 | 0.580 / 0.585 / 0.590 / 0.600 | 0.223 / 0.224 / 0.224 / 0.225 | 0.314 / 0.313 / 0.313 / 0.318 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,080 / 1,080 / 1,080 / 1,080 | 0.205 / 0.249 / 0.352 / 0.633 | 0.359 / 0.368 / 0.365 / 0.385 | 0.690 / 0.695 / 0.697 / 0.697 | 0.053 / 0.051 / 0.051 / 0.052 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 255 / 255 / 255 | 1.000 / 0.380 / 0.432 / 0.563 | 1.000 / 0.422 / 0.413 / 0.407 | 1.000 / 0.546 / 0.552 / 0.554 | 1.000 / 0.036 / 0.036 / 0.036 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 47,451 / 52,827 / 55,318 / 55,889 | 0.179 / 0.222 / 0.259 / 0.293 | 0.160 / 0.208 / 0.240 / 0.245 | 0.169 / 0.175 / 0.176 / 0.177 | 0.177 / 0.183 / 0.186 / 0.186 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 3,253 / 4,045 / 4,354 / 4,471 | 0.205 / 0.284 / 0.360 / 0.418 | 0.048 / 0.057 / 0.060 / 0.064 | 0.168 / 0.185 / 0.195 / 0.200 | 0.438 / 0.459 / 0.468 / 0.475 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,367 / 1,826 / 1,951 / 1,981 | 0.183 / 0.290 / 0.348 / 0.405 | 0.101 / 0.104 / 0.124 / 0.166 | 0.126 / 0.140 / 0.148 / 0.151 | 0.174 / 0.172 / 0.172 / 0.171 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 839 / 846 / 901 / 902 | 0.183 / 0.209 / 0.219 / 0.225 | 0.190 / 0.180 / 0.192 / 0.194 | 0.225 / 0.232 / 0.233 / 0.233 | 0.663 / 0.692 / 0.686 / 0.686 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 8,085 / 8,202 / 8,275 / 8,325 | 0.123 / 0.135 / 0.157 / 0.205 | 0.588 / 0.593 / 0.598 / 0.607 | 0.223 / 0.224 / 0.224 / 0.225 | 0.321 / 0.320 / 0.322 / 0.326 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,120 / 1,225 / 1,270 / 1,347 | 0.202 / 0.247 / 0.346 / 0.630 | 0.354 / 0.373 / 0.372 / 0.396 | 0.687 / 0.695 / 0.697 / 0.699 | 0.049 / 0.050 / 0.050 / 0.051 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 156 / 231 / 237 / 268 | 0.255 / 0.361 / 0.392 / 0.529 | 0.380 / 0.406 / 0.399 / 0.397 | 0.461 / 0.502 / 0.509 / 0.539 | 0.033 / 0.036 / 0.036 / 0.036 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +**Total BEV Center Distance mAP (eval range = 90.0 - 121.0m): 0.4396** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 22,141 | 15,775 / 18,697 / 20,145 / 20,550 | 0.541 / 0.714 / 0.790 / 0.814 | 0.666 / 0.762 / 0.800 / 0.811 | 0.204 / 0.181 / 0.160 / 0.156 | +| truck | 3,506 | 1,627 / 2,289 / 2,818 / 3,016 | 0.257 / 0.472 / 0.668 / 0.733 | 0.464 / 0.620 / 0.736 / 0.770 | 0.159 / 0.159 / 0.111 / 0.111 | +| bus | 544 | 257 / 368 / 432 / 448 | 0.273 / 0.540 / 0.667 / 0.698 | 0.467 / 0.639 / 0.717 / 0.737 | 0.349 / 0.126 / 0.066 / 0.066 | +| bicycle | 376 | 269 / 307 / 317 / 318 | 0.354 / 0.532 / 0.552 / 0.554 | 0.509 / 0.605 / 0.619 / 0.619 | 0.136 / 0.143 / 0.143 / 0.143 | +| pedestrian | 3,656 | 3,001 / 3,053 / 3,081 / 3,122 | 0.482 / 0.496 / 0.505 / 0.515 | 0.591 / 0.598 / 0.602 / 0.607 | 0.135 / 0.135 / 0.135 / 0.135 | +| traffic_cone | 462 | 183 / 207 / 225 / 235 | 0.100 / 0.129 / 0.141 / 0.162 | 0.304 / 0.339 / 0.349 / 0.365 | 0.127 / 0.127 / 0.088 / 0.088 | +| barrier | 145 | 49 / 72 / 90 / 96 | 0.041 / 0.139 / 0.203 / 0.240 | 0.237 / 0.362 / 0.425 / 0.449 | 0.139 / 0.119 / 0.085 / 0.095 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 2,435 / 2,435 / 2,435 / 2,435 | 0.199 / 0.241 / 0.275 / 0.305 | 0.198 / 0.246 / 0.274 / 0.284 | 0.180 / 0.184 / 0.185 / 0.186 | 0.299 / 0.294 / 0.293 / 0.294 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 385 / 385 / 385 / 385 | 0.226 / 0.312 / 0.434 / 0.493 | 0.043 / 0.048 / 0.054 / 0.060 | 0.175 / 0.191 / 0.209 / 0.215 | 0.387 / 0.414 / 0.428 / 0.437 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 59 / 59 / 59 / 59 | 0.234 / 0.326 / 0.385 / 0.409 | 0.037 / 0.054 / 0.058 / 0.059 | 0.141 / 0.156 / 0.164 / 0.167 | 0.378 / 0.406 / 0.428 / 0.431 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 41 / 41 / 41 / 41 | 0.237 / 0.297 / 0.311 / 0.314 | 0.101 / 0.094 / 0.095 / 0.095 | 0.249 / 0.264 / 0.266 / 0.266 | 0.777 / 0.771 / 0.776 / 0.779 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 402 / 402 / 402 / 402 | 0.126 / 0.137 / 0.155 / 0.197 | 0.496 / 0.502 / 0.508 / 0.514 | 0.229 / 0.230 / 0.230 / 0.230 | 0.382 / 0.382 / 0.383 / 0.386 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 50 / 50 / 50 / 50 | 0.193 / 0.234 / 0.272 / 0.526 | 0.288 / 0.286 / 0.309 / 0.312 | 0.702 / 0.701 / 0.703 / 0.699 | 0.044 / 0.046 / 0.046 / 0.046 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 15 / 15 / 15 / 15 | 0.301 / 0.435 / 0.535 / 0.997 | 0.250 / 0.220 / 0.216 / 0.212 | 0.487 / 0.511 / 0.530 / 0.530 | 0.045 / 0.045 / 0.046 / 0.046 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 9,077 / 9,077 / 9,077 / 9,077 | 0.208 / 0.259 / 0.301 / 0.339 | 0.248 / 0.296 / 0.327 / 0.336 | 0.186 / 0.190 / 0.191 / 0.191 | 0.306 / 0.302 / 0.301 / 0.302 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,437 / 1,437 / 1,437 / 1,437 | 0.240 / 0.346 / 0.494 / 0.573 | 0.067 / 0.062 / 0.068 / 0.075 | 0.187 / 0.201 / 0.220 / 0.229 | 0.457 / 0.479 / 0.483 / 0.490 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 223 / 223 / 223 / 223 | 0.249 / 0.363 / 0.448 / 0.486 | 0.061 / 0.072 / 0.073 / 0.074 | 0.149 / 0.162 / 0.175 / 0.178 | 0.429 / 0.434 / 0.460 / 0.464 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 154 / 154 / 154 / 154 | 0.222 / 0.284 / 0.307 / 0.313 | 0.131 / 0.120 / 0.120 / 0.120 | 0.246 / 0.259 / 0.263 / 0.263 | 0.766 / 0.795 / 0.801 / 0.806 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,498 / 1,498 / 1,498 / 1,498 | 0.132 / 0.145 / 0.167 / 0.220 | 0.550 / 0.554 / 0.563 / 0.572 | 0.228 / 0.228 / 0.229 / 0.229 | 0.415 / 0.414 / 0.415 / 0.419 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 189 / 189 / 189 | 1.000 / 0.274 / 0.365 / 0.629 | 1.000 / 0.405 / 0.430 / 0.444 | 1.000 / 0.718 / 0.719 / 0.714 | 1.000 / 0.060 / 0.059 / 0.055 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 59 / 59 / 59 | 1.000 / 0.438 / 0.604 / 1.056 | 1.000 / 0.237 / 0.228 / 0.223 | 1.000 / 0.567 / 0.578 / 0.581 | 1.000 / 0.045 / 0.047 / 0.047 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 13,838 / 16,236 / 17,440 / 17,774 | 0.211 / 0.272 / 0.327 / 0.375 | 0.275 / 0.339 / 0.383 / 0.394 | 0.188 / 0.193 / 0.195 / 0.196 | 0.306 / 0.304 / 0.306 / 0.308 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,483 / 1,981 / 2,484 / 2,599 | 0.238 / 0.351 / 0.530 / 0.622 | 0.055 / 0.061 / 0.083 / 0.093 | 0.186 / 0.205 / 0.230 / 0.238 | 0.465 / 0.493 / 0.490 / 0.496 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 192 / 312 / 394 / 405 | 0.240 / 0.373 / 0.509 / 0.561 | 0.044 / 0.085 / 0.084 / 0.084 | 0.141 / 0.164 / 0.184 / 0.189 | 0.369 / 0.450 / 0.478 / 0.483 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 187 / 218 / 223 / 223 | 0.223 / 0.285 / 0.310 / 0.310 | 0.090 / 0.095 / 0.095 / 0.095 | 0.244 / 0.257 / 0.261 / 0.261 | 0.760 / 0.794 / 0.798 / 0.798 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 2,141 / 2,167 / 2,181 / 2,199 | 0.130 / 0.142 / 0.162 / 0.211 | 0.536 / 0.542 / 0.547 / 0.555 | 0.226 / 0.227 / 0.228 / 0.228 | 0.413 / 0.413 / 0.412 / 0.417 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 132 / 147 / 178 / 186 | 0.201 / 0.255 / 0.313 / 0.600 | 0.338 / 0.322 / 0.401 / 0.422 | 0.704 / 0.702 / 0.710 / 0.705 | 0.047 / 0.048 / 0.055 / 0.054 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 33 / 52 / 72 / 73 | 0.295 / 0.437 / 0.603 / 1.064 | 0.239 / 0.211 / 0.222 / 0.219 | 0.469 / 0.512 / 0.568 / 0.572 | 0.048 / 0.047 / 0.047 / 0.047 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +**Total BEV Center Distance mAP (eval range = 0.0 - 121.0m): 0.6590** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 162,690 | 133,765 / 145,295 / 150,720 / 153,032 | 0.765 / 0.854 / 0.890 / 0.910 | 0.825 / 0.877 / 0.895 / 0.902 | 0.242 / 0.194 / 0.169 / 0.157 | +| truck | 17,701 | 11,903 / 14,102 / 15,459 / 16,076 | 0.550 / 0.709 / 0.807 / 0.848 | 0.683 / 0.781 / 0.835 / 0.856 | 0.266 / 0.176 / 0.165 / 0.157 | +| bus | 5,507 | 4,147 / 4,894 / 5,184 / 5,269 | 0.668 / 0.840 / 0.916 / 0.930 | 0.761 / 0.872 / 0.909 / 0.919 | 0.343 / 0.182 / 0.172 / 0.125 | +| bicycle | 3,771 | 3,210 / 3,360 / 3,393 / 3,399 | 0.732 / 0.795 / 0.802 / 0.808 | 0.775 / 0.805 / 0.809 / 0.810 | 0.158 / 0.156 / 0.156 / 0.156 | +| pedestrian | 38,962 | 34,759 / 35,452 / 35,735 / 36,011 | 0.755 / 0.774 / 0.787 / 0.799 | 0.768 / 0.780 / 0.786 / 0.792 | 0.154 / 0.154 / 0.154 / 0.158 | +| traffic_cone | 11,408 | 6,982 / 7,578 / 7,846 / 8,197 | 0.338 / 0.390 / 0.411 / 0.444 | 0.521 / 0.557 / 0.571 / 0.592 | 0.123 / 0.111 / 0.111 / 0.087 | +| barrier | 2,117 | 839 / 1,125 / 1,212 / 1,272 | 0.144 / 0.241 / 0.263 / 0.280 | 0.367 / 0.438 / 0.450 / 0.459 | 0.274 / 0.185 / 0.185 / 0.183 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 17,895 / 17,895 / 17,895 / 17,895 | 0.129 / 0.142 / 0.151 / 0.160 | 0.062 / 0.077 / 0.086 / 0.090 | 0.133 / 0.136 / 0.137 / 0.137 | 0.148 / 0.151 / 0.152 / 0.153 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,947 / 1,947 / 1,947 / 1,947 | 0.165 / 0.202 / 0.242 / 0.273 | 0.032 / 0.035 / 0.037 / 0.039 | 0.140 / 0.148 / 0.155 / 0.158 | 0.339 / 0.351 / 0.359 / 0.362 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 605 / 605 / 605 / 605 | 0.127 / 0.157 / 0.182 / 0.188 | 0.079 / 0.078 / 0.080 / 0.083 | 0.094 / 0.100 / 0.106 / 0.106 | 0.144 / 0.150 / 0.151 / 0.152 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 414 / 414 / 414 / 414 | 0.147 / 0.162 / 0.165 / 0.168 | 0.095 / 0.096 / 0.095 / 0.096 | 0.210 / 0.215 / 0.215 / 0.216 | 0.573 / 0.583 / 0.582 / 0.583 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 4,285 / 4,285 / 4,285 / 4,285 | 0.107 / 0.114 / 0.127 / 0.151 | 0.437 / 0.439 / 0.443 / 0.449 | 0.232 / 0.232 / 0.233 / 0.233 | 0.256 / 0.255 / 0.255 / 0.258 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,254 / 1,254 / 1,254 / 1,254 | 0.179 / 0.203 / 0.234 / 0.347 | 0.322 / 0.321 / 0.322 / 0.327 | 0.652 / 0.656 / 0.657 / 0.659 | 0.029 / 0.029 / 0.029 / 0.030 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 232 / 232 / 232 / 232 | 0.239 / 0.311 / 0.339 / 0.410 | 0.374 / 0.369 / 0.369 / 0.366 | 0.469 / 0.488 / 0.494 / 0.502 | 0.026 / 0.027 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 66,702 / 66,702 / 66,702 / 66,702 | 0.142 / 0.160 / 0.172 / 0.185 | 0.080 / 0.101 / 0.113 / 0.117 | 0.142 / 0.145 / 0.146 / 0.147 | 0.162 / 0.164 / 0.165 / 0.166 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 7,257 / 7,257 / 7,257 / 7,257 | 0.178 / 0.226 / 0.280 / 0.323 | 0.039 / 0.042 / 0.045 / 0.047 | 0.149 / 0.159 / 0.167 / 0.171 | 0.361 / 0.374 / 0.383 / 0.385 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,257 / 2,257 / 2,257 / 2,257 | 0.143 / 0.184 / 0.212 / 0.220 | 0.076 / 0.076 / 0.079 / 0.083 | 0.101 / 0.109 / 0.114 / 0.115 | 0.165 / 0.169 / 0.170 / 0.171 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,546 / 1,546 / 1,546 / 1,546 | 0.151 / 0.167 / 0.172 / 0.176 | 0.108 / 0.109 / 0.109 / 0.110 | 0.215 / 0.219 / 0.220 / 0.221 | 0.592 / 0.600 / 0.599 / 0.599 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 15,974 / 15,974 / 15,974 / 15,974 | 0.113 / 0.122 / 0.139 / 0.172 | 0.476 / 0.478 / 0.483 / 0.489 | 0.236 / 0.237 / 0.238 / 0.238 | 0.269 / 0.268 / 0.268 / 0.271 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 4,677 / 4,677 / 4,677 / 4,677 | 0.191 / 0.223 / 0.269 / 0.413 | 0.371 / 0.368 / 0.368 / 0.372 | 0.663 / 0.668 / 0.669 / 0.670 | 0.032 / 0.032 / 0.032 / 0.032 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 867 / 867 / 867 | 1.000 / 0.355 / 0.402 / 0.517 | 1.000 / 0.398 / 0.397 / 0.391 | 1.000 / 0.544 / 0.550 / 0.557 | 1.000 / 0.029 / 0.029 / 0.030 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 126,514 / 137,022 / 141,450 / 143,300 | 0.157 / 0.191 / 0.218 / 0.246 | 0.115 / 0.151 / 0.174 / 0.182 | 0.151 / 0.156 / 0.158 / 0.159 | 0.176 / 0.182 / 0.185 / 0.186 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 10,859 / 13,045 / 14,027 / 14,439 | 0.185 / 0.255 / 0.328 / 0.389 | 0.044 / 0.055 / 0.059 / 0.064 | 0.154 / 0.170 / 0.181 / 0.188 | 0.368 / 0.391 / 0.402 / 0.406 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 3,844 / 4,623 / 4,831 / 4,974 | 0.159 / 0.236 / 0.278 / 0.319 | 0.075 / 0.083 / 0.094 / 0.114 | 0.109 / 0.121 / 0.127 / 0.131 | 0.178 / 0.185 / 0.188 / 0.191 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 2,732 / 2,840 / 2,856 / 2,859 | 0.154 / 0.173 / 0.180 / 0.184 | 0.117 / 0.120 / 0.121 / 0.121 | 0.218 / 0.223 / 0.224 / 0.225 | 0.598 / 0.606 / 0.605 / 0.605 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 28,970 / 29,422 / 29,647 / 29,660 | 0.117 / 0.130 / 0.148 / 0.187 | 0.496 / 0.500 / 0.505 / 0.510 | 0.238 / 0.239 / 0.240 / 0.240 | 0.280 / 0.279 / 0.280 / 0.283 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 5,644 / 6,197 / 6,356 / 7,069 | 0.189 / 0.223 / 0.271 / 0.452 | 0.370 / 0.375 / 0.375 / 0.388 | 0.660 / 0.667 / 0.669 / 0.676 | 0.032 / 0.032 / 0.032 / 0.033 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 602 / 827 / 850 / 871 | 0.245 / 0.329 / 0.356 / 0.433 | 0.378 / 0.383 / 0.380 / 0.377 | 0.478 / 0.506 / 0.509 / 0.515 | 0.026 / 0.027 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +--- + +**LargeBus**: db_largebus_v1 + db_largebus_v2 + db_largebus_v3 (1,228 frames) + +**Total BEV Center Distance mAP (eval range = 0.0 - 50.0m): 0.6313** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 14,872 | 13,494 / 13,924 / 14,079 / 14,195 | 0.879 / 0.917 / 0.928 / 0.939 | 0.921 / 0.942 / 0.947 / 0.951 | 0.278 / 0.169 / 0.169 / 0.169 | +| truck | 1,192 | 981 / 1,080 / 1,113 / 1,136 | 0.760 / 0.876 / 0.913 / 0.932 | 0.837 / 0.907 / 0.922 / 0.925 | 0.357 / 0.208 / 0.187 / 0.157 | +| bus | 336 | 261 / 332 / 335 / 335 | 0.715 / 0.975 / 0.987 / 0.987 | 0.808 / 0.973 / 0.982 / 0.982 | 0.469 / 0.099 / 0.099 / 0.099 | +| bicycle | 740 | 676 / 694 / 706 / 710 | 0.817 / 0.857 / 0.878 / 0.883 | 0.846 / 0.864 / 0.871 / 0.875 | 0.174 / 0.166 / 0.157 / 0.157 | +| pedestrian | 5,055 | 4,706 / 4,761 / 4,785 / 4,800 | 0.844 / 0.859 / 0.864 / 0.868 | 0.851 / 0.860 / 0.864 / 0.868 | 0.151 / 0.151 / 0.151 / 0.151 | +| traffic_cone | 60 | 20 / 21 / 21 / 22 | 0.000 / 0.000 / 0.000 / 0.000 | 0.038 / 0.040 / 0.040 / 0.042 | 0.065 / 0.065 / 0.065 / 0.065 | +| barrier | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,635 / 1,635 / 1,635 / 1,635 | 0.110 / 0.113 / 0.114 / 0.116 | 0.040 / 0.041 / 0.042 / 0.043 | 0.117 / 0.117 / 0.118 / 0.118 | 0.138 / 0.140 / 0.140 / 0.140 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 131 / 131 / 131 / 131 | 0.146 / 0.166 / 0.175 / 0.181 | 0.030 / 0.031 / 0.032 / 0.034 | 0.127 / 0.131 / 0.132 / 0.133 | 0.205 / 0.208 / 0.207 / 0.207 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 36 / 36 / 36 / 36 | 0.141 / 0.172 / 0.174 / 0.174 | 0.205 / 0.198 / 0.198 / 0.198 | 0.084 / 0.090 / 0.090 / 0.090 | 0.185 / 0.180 / 0.181 / 0.181 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 81 / 81 / 81 / 81 | 0.136 / 0.148 / 0.152 / 0.156 | 0.109 / 0.108 / 0.109 / 0.109 | 0.218 / 0.223 / 0.224 / 0.225 | 0.547 / 0.542 / 0.541 / 0.541 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 556 / 556 / 556 / 556 | 0.096 / 0.099 / 0.104 / 0.120 | 0.295 / 0.296 / 0.296 / 0.302 | 0.210 / 0.210 / 0.210 / 0.210 | 0.249 / 0.248 / 0.248 / 0.251 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 6 / 6 / 6 / 6 | 0.155 / 0.194 / 0.194 / 0.312 | 1.696 / 1.646 / 1.646 / 1.590 | 0.453 / 0.468 / 0.468 / 0.485 | 0.077 / 0.082 / 0.082 / 0.083 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 6,097 / 6,097 / 6,097 / 6,097 | 0.118 / 0.122 / 0.124 / 0.127 | 0.044 / 0.047 / 0.048 / 0.049 | 0.121 / 0.122 / 0.122 / 0.123 | 0.150 / 0.151 / 0.152 / 0.152 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 488 / 488 / 488 / 488 | 0.155 / 0.179 / 0.193 / 0.202 | 0.034 / 0.035 / 0.036 / 0.039 | 0.136 / 0.140 / 0.142 / 0.143 | 0.213 / 0.215 / 0.214 / 0.213 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 137 / 137 / 137 / 137 | 0.154 / 0.196 / 0.199 / 0.199 | 0.216 / 0.201 / 0.201 / 0.201 | 0.088 / 0.095 / 0.096 / 0.096 | 0.194 / 0.183 / 0.184 / 0.184 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 303 / 303 / 303 / 303 | 0.140 / 0.150 / 0.156 / 0.162 | 0.122 / 0.121 / 0.122 / 0.121 | 0.221 / 0.225 / 0.227 / 0.228 | 0.547 / 0.544 / 0.541 / 0.542 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 2,072 / 2,072 / 2,072 / 2,072 | 0.100 / 0.104 / 0.110 / 0.130 | 0.334 / 0.335 / 0.335 / 0.342 | 0.216 / 0.216 / 0.217 / 0.217 | 0.255 / 0.255 / 0.255 / 0.258 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 13,178 / 13,676 / 13,748 / 13,798 | 0.133 / 0.147 / 0.154 / 0.166 | 0.062 / 0.074 / 0.078 / 0.080 | 0.132 / 0.134 / 0.135 / 0.135 | 0.155 / 0.157 / 0.158 / 0.158 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 925 / 1,041 / 1,064 / 1,073 | 0.167 / 0.206 / 0.227 / 0.247 | 0.040 / 0.046 / 0.051 / 0.062 | 0.148 / 0.155 / 0.159 / 0.160 | 0.204 / 0.203 / 0.202 / 0.201 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 254 / 330 / 333 / 333 | 0.167 / 0.272 / 0.283 / 0.283 | 0.213 / 0.172 / 0.171 / 0.171 | 0.092 / 0.108 / 0.110 / 0.110 | 0.192 / 0.163 / 0.166 / 0.166 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 612 / 628 / 640 / 643 | 0.145 / 0.156 / 0.171 / 0.182 | 0.136 / 0.135 / 0.136 / 0.135 | 0.223 / 0.228 / 0.232 / 0.234 | 0.529 / 0.525 / 0.517 / 0.516 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 4,247 / 4,294 / 4,313 / 4,330 | 0.108 / 0.117 / 0.126 / 0.152 | 0.374 / 0.377 / 0.379 / 0.388 | 0.222 / 0.222 / 0.223 / 0.223 | 0.257 / 0.255 / 0.256 / 0.260 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 19 / 20 / 20 / 21 | 0.158 / 0.207 / 0.207 / 0.339 | 1.613 / 1.543 / 1.543 / 1.485 | 0.469 / 0.492 / 0.492 / 0.508 | 0.086 / 0.087 / 0.087 / 0.087 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +**Total BEV Center Distance mAP (eval range = 50.0 - 90.0m): 0.5281** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 10,929 | 8,919 / 9,721 / 10,089 / 10,240 | 0.747 / 0.843 / 0.887 / 0.899 | 0.816 / 0.875 / 0.895 / 0.901 | 0.216 / 0.166 / 0.149 / 0.149 | +| truck | 1,009 | 664 / 792 / 859 / 874 | 0.548 / 0.705 / 0.789 / 0.802 | 0.701 / 0.790 / 0.844 / 0.849 | 0.283 / 0.180 / 0.155 / 0.155 | +| bus | 141 | 114 / 134 / 135 / 137 | 0.650 / 0.913 / 0.916 / 0.929 | 0.769 / 0.905 / 0.905 / 0.905 | 0.486 / 0.444 / 0.444 / 0.444 | +| bicycle | 460 | 320 / 362 / 371 / 371 | 0.468 / 0.598 / 0.619 / 0.619 | 0.610 / 0.670 / 0.677 / 0.677 | 0.098 / 0.098 / 0.098 / 0.098 | +| pedestrian | 3,721 | 3,190 / 3,254 / 3,271 / 3,297 | 0.691 / 0.713 / 0.718 / 0.729 | 0.728 / 0.740 / 0.744 / 0.749 | 0.125 / 0.124 / 0.124 / 0.124 | +| traffic_cone | 4 | 2 / 2 / 2 / 2 | 0.000 / 0.000 / 0.000 / 0.000 | 0.027 / 0.027 / 0.027 / 0.027 | 0.099 / 0.099 / 0.099 / 0.099 | +| barrier | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,202 / 1,202 / 1,202 / 1,202 | 0.157 / 0.172 / 0.180 / 0.188 | 0.079 / 0.094 / 0.103 / 0.104 | 0.147 / 0.151 / 0.152 / 0.152 | 0.204 / 0.209 / 0.213 / 0.213 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 110 / 110 / 110 / 110 | 0.188 / 0.222 / 0.253 / 0.258 | 0.038 / 0.043 / 0.046 / 0.048 | 0.163 / 0.173 / 0.178 / 0.178 | 0.237 / 0.240 / 0.257 / 0.258 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 15 / 15 / 15 / 15 | 0.208 / 0.258 / 0.258 / 0.261 | 0.597 / 0.536 / 0.536 / 0.532 | 0.082 / 0.089 / 0.090 / 0.090 | 0.211 / 0.208 / 0.208 / 0.210 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 50 / 50 / 50 / 50 | 0.185 / 0.236 / 0.248 / 0.249 | 0.182 / 0.184 / 0.185 / 0.185 | 0.243 / 0.258 / 0.262 / 0.262 | 0.621 / 0.730 / 0.726 / 0.726 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 409 / 409 / 409 / 409 | 0.113 / 0.122 / 0.129 / 0.155 | 0.446 / 0.450 / 0.452 / 0.459 | 0.203 / 0.204 / 0.205 / 0.205 | 0.279 / 0.279 / 0.279 / 0.282 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 0.154 / 0.154 / 0.154 / 0.154 | 2.562 / 2.562 / 2.562 / 2.562 | 0.285 / 0.285 / 0.285 / 0.285 | 0.035 / 0.035 / 0.035 / 0.035 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 4,480 / 4,480 / 4,480 / 4,480 | 0.166 / 0.186 / 0.198 / 0.208 | 0.099 / 0.118 / 0.130 / 0.131 | 0.154 / 0.158 / 0.159 / 0.160 | 0.223 / 0.228 / 0.233 / 0.233 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 413 / 413 / 413 / 413 | 0.199 / 0.241 / 0.285 / 0.293 | 0.045 / 0.051 / 0.054 / 0.057 | 0.169 / 0.180 / 0.187 / 0.187 | 0.271 / 0.270 / 0.281 / 0.283 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 57 / 57 / 57 / 57 | 0.216 / 0.269 / 0.269 / 0.272 | 0.432 / 0.394 / 0.394 / 0.393 | 0.091 / 0.099 / 0.099 / 0.099 | 0.241 / 0.238 / 0.239 / 0.241 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 188 / 188 / 188 / 188 | 0.184 / 0.230 / 0.244 / 0.245 | 0.225 / 0.227 / 0.228 / 0.227 | 0.255 / 0.266 / 0.269 / 0.269 | 0.652 / 0.730 / 0.723 / 0.723 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,525 / 1,525 / 1,525 / 1,525 | 0.120 / 0.132 / 0.142 / 0.178 | 0.490 / 0.495 / 0.497 / 0.504 | 0.208 / 0.209 / 0.210 / 0.211 | 0.299 / 0.297 / 0.298 / 0.302 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1 / 1 / 1 / 1 | 0.157 / 0.157 / 0.157 / 0.157 | 2.579 / 2.579 / 2.579 / 2.579 | 0.270 / 0.270 / 0.270 / 0.270 | 0.034 / 0.034 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 8,463 / 9,288 / 9,554 / 9,621 | 0.178 / 0.214 / 0.240 / 0.263 | 0.140 / 0.176 / 0.194 / 0.196 | 0.162 / 0.168 / 0.170 / 0.171 | 0.241 / 0.261 / 0.269 / 0.269 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 617 / 739 / 799 / 804 | 0.205 / 0.274 / 0.347 / 0.361 | 0.046 / 0.062 / 0.070 / 0.079 | 0.173 / 0.184 / 0.195 / 0.197 | 0.262 / 0.270 / 0.275 / 0.283 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 103 / 124 / 124 / 124 | 0.222 / 0.283 / 0.283 / 0.283 | 0.374 / 0.341 / 0.341 / 0.341 | 0.097 / 0.107 / 0.107 / 0.107 | 0.296 / 0.295 / 0.295 / 0.295 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 263 / 289 / 292 / 292 | 0.183 / 0.227 / 0.237 / 0.237 | 0.249 / 0.257 / 0.255 / 0.255 | 0.256 / 0.268 / 0.271 / 0.271 | 0.631 / 0.726 / 0.721 / 0.721 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 2,604 / 2,652 / 2,667 / 2,682 | 0.124 / 0.139 / 0.152 / 0.191 | 0.521 / 0.526 / 0.528 / 0.535 | 0.211 / 0.212 / 0.213 / 0.214 | 0.310 / 0.308 / 0.310 / 0.313 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 2 / 2 / 2 / 2 | 0.157 / 0.157 / 0.157 / 0.157 | 2.579 / 2.579 / 2.579 / 2.579 | 0.270 / 0.270 / 0.270 / 0.270 | 0.034 / 0.034 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +**Total BEV Center Distance mAP (eval range = 90.0 - 121.0m): 0.4172** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 2,883 | 2,173 / 2,512 / 2,669 / 2,713 | 0.616 / 0.762 / 0.812 / 0.829 | 0.705 / 0.783 / 0.806 / 0.814 | 0.203 / 0.202 / 0.192 / 0.162 | +| truck | 600 | 343 / 461 / 519 / 534 | 0.393 / 0.654 / 0.778 / 0.809 | 0.572 / 0.727 / 0.808 / 0.824 | 0.279 / 0.178 / 0.145 / 0.145 | +| bus | 60 | 32 / 44 / 47 / 47 | 0.379 / 0.599 / 0.655 / 0.655 | 0.536 / 0.681 / 0.707 / 0.707 | 0.134 / 0.176 / 0.049 / 0.049 | +| bicycle | 85 | 54 / 61 / 66 / 66 | 0.262 / 0.373 / 0.434 / 0.434 | 0.433 / 0.528 / 0.579 / 0.579 | 0.102 / 0.144 / 0.144 / 0.144 | +| pedestrian | 1,092 | 945 / 960 / 963 / 979 | 0.545 / 0.554 / 0.562 / 0.576 | 0.638 / 0.644 / 0.646 / 0.651 | 0.145 / 0.145 / 0.145 / 0.135 | +| traffic_cone | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | +| barrier | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 317 / 317 / 317 / 317 | 0.195 / 0.225 / 0.242 / 0.263 | 0.082 / 0.107 / 0.125 / 0.131 | 0.178 / 0.184 / 0.185 / 0.185 | 0.449 / 0.461 / 0.470 / 0.476 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 66 / 66 / 66 / 66 | 0.210 / 0.284 / 0.333 / 0.350 | 0.040 / 0.041 / 0.045 / 0.049 | 0.169 / 0.177 / 0.185 / 0.188 | 0.152 / 0.154 / 0.157 / 0.161 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 6 / 6 / 6 / 6 | 0.264 / 0.325 / 0.348 / 0.348 | 0.039 / 0.208 / 0.205 / 0.205 | 0.135 / 0.141 / 0.145 / 0.145 | 0.137 / 0.266 / 0.428 / 0.428 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 9 / 9 / 9 / 9 | 0.203 / 0.257 / 0.320 / 0.320 | 0.120 / 0.110 / 0.108 / 0.108 | 0.270 / 0.272 / 0.282 / 0.282 | 0.796 / 0.879 / 0.888 / 0.888 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 120 / 120 / 120 / 120 | 0.131 / 0.135 / 0.155 / 0.204 | 0.441 / 0.441 / 0.444 / 0.453 | 0.178 / 0.178 / 0.179 / 0.178 | 0.369 / 0.370 / 0.371 / 0.376 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,182 / 1,182 / 1,182 / 1,182 | 0.204 / 0.243 / 0.266 / 0.294 | 0.107 / 0.142 / 0.167 / 0.175 | 0.186 / 0.191 / 0.192 / 0.192 | 0.512 / 0.522 / 0.531 / 0.538 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 246 / 246 / 246 / 246 | 0.217 / 0.307 / 0.381 / 0.406 | 0.058 / 0.052 / 0.056 / 0.061 | 0.176 / 0.185 / 0.197 / 0.200 | 0.196 / 0.193 / 0.189 / 0.194 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 24 / 24 / 24 / 24 | 0.236 / 0.355 / 0.393 / 0.393 | 0.039 / 0.269 / 0.255 / 0.255 | 0.134 / 0.146 / 0.152 / 0.152 | 0.234 / 0.402 / 0.674 / 0.674 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 34 / 34 / 34 / 34 | 0.202 / 0.269 / 0.361 / 0.361 | 0.120 / 0.104 / 0.102 / 0.102 | 0.269 / 0.271 / 0.284 / 0.284 | 0.792 / 0.904 / 0.924 / 0.924 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 447 / 447 / 447 / 447 | 0.135 / 0.142 / 0.162 / 0.225 | 0.483 / 0.483 / 0.486 / 0.500 | 0.179 / 0.179 / 0.179 / 0.179 | 0.394 / 0.393 / 0.396 / 0.403 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,929 / 2,144 / 2,236 / 2,337 | 0.208 / 0.254 / 0.285 / 0.333 | 0.124 / 0.166 / 0.200 / 0.232 | 0.189 / 0.196 / 0.196 / 0.197 | 0.524 / 0.543 / 0.557 / 0.563 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 301 / 408 / 466 / 475 | 0.217 / 0.320 / 0.431 / 0.466 | 0.048 / 0.062 / 0.071 / 0.076 | 0.177 / 0.193 / 0.209 / 0.213 | 0.195 / 0.228 / 0.220 / 0.222 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 26 / 32 / 41 / 41 | 0.236 / 0.360 / 0.411 / 0.411 | 0.037 / 0.331 / 0.268 / 0.268 | 0.129 / 0.140 / 0.162 / 0.162 | 0.191 / 0.325 / 0.784 / 0.784 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 39 / 42 / 46 / 46 | 0.206 / 0.281 / 0.376 / 0.376 | 0.105 / 0.093 / 0.092 / 0.092 | 0.266 / 0.265 / 0.280 / 0.280 | 0.783 / 0.918 / 0.916 / 0.916 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 691 / 697 / 700 / 730 | 0.136 / 0.142 / 0.161 / 0.230 | 0.492 / 0.492 / 0.490 / 0.504 | 0.175 / 0.176 / 0.176 / 0.178 | 0.393 / 0.393 / 0.393 / 0.402 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +**Total BEV Center Distance mAP (eval range = 0.0 - 121.0m): 0.5779** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 28,684 | 24,624 / 26,214 / 26,901 / 27,216 | 0.811 / 0.885 / 0.909 / 0.920 | 0.862 / 0.903 / 0.915 / 0.920 | 0.236 / 0.200 / 0.166 / 0.166 | +| truck | 2,801 | 1,991 / 2,336 / 2,497 / 2,554 | 0.611 / 0.775 / 0.848 / 0.868 | 0.735 / 0.827 / 0.870 / 0.878 | 0.281 / 0.178 / 0.157 / 0.144 | +| bus | 537 | 407 / 510 / 517 / 519 | 0.662 / 0.919 / 0.938 / 0.938 | 0.771 / 0.907 / 0.914 / 0.914 | 0.486 / 0.125 / 0.125 / 0.125 | +| bicycle | 1,285 | 1,053 / 1,121 / 1,146 / 1,150 | 0.667 / 0.750 / 0.772 / 0.775 | 0.739 / 0.775 / 0.784 / 0.787 | 0.169 / 0.166 / 0.156 / 0.156 | +| pedestrian | 9,868 | 8,856 / 8,991 / 9,035 / 9,095 | 0.764 / 0.783 / 0.788 / 0.799 | 0.783 / 0.792 / 0.796 / 0.799 | 0.146 / 0.146 / 0.146 / 0.146 | +| traffic_cone | 64 | 22 / 23 / 23 / 24 | 0.000 / 0.000 / 0.000 / 0.000 | 0.033 / 0.034 / 0.034 / 0.036 | 0.079 / 0.065 / 0.065 / 0.065 | +| barrier | 0 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 3,155 / 3,155 / 3,155 / 3,155 | 0.127 / 0.136 / 0.141 / 0.145 | 0.053 / 0.060 / 0.064 / 0.065 | 0.128 / 0.130 / 0.131 / 0.131 | 0.168 / 0.173 / 0.175 / 0.176 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 308 / 308 / 308 / 308 | 0.169 / 0.202 / 0.224 / 0.232 | 0.034 / 0.036 / 0.038 / 0.041 | 0.145 / 0.152 / 0.156 / 0.157 | 0.204 / 0.206 / 0.211 / 0.212 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 59 / 59 / 59 / 59 | 0.163 / 0.201 / 0.205 / 0.205 | 0.310 / 0.286 / 0.285 / 0.285 | 0.085 / 0.092 / 0.093 / 0.093 | 0.190 / 0.188 / 0.194 / 0.194 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 141 / 141 / 141 / 141 | 0.149 / 0.173 / 0.182 / 0.187 | 0.128 / 0.128 / 0.129 / 0.128 | 0.224 / 0.233 / 0.235 / 0.236 | 0.561 / 0.594 / 0.593 / 0.593 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,085 / 1,085 / 1,085 / 1,085 | 0.103 / 0.108 / 0.115 / 0.137 | 0.352 / 0.355 / 0.356 / 0.364 | 0.209 / 0.209 / 0.210 / 0.210 | 0.265 / 0.264 / 0.265 / 0.268 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 7 / 7 / 7 / 7 | 0.155 / 0.190 / 0.190 / 0.300 | 1.777 / 1.717 / 1.717 / 1.666 | 0.439 / 0.454 / 0.454 / 0.471 | 0.073 / 0.078 / 0.078 / 0.079 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 11,760 / 11,760 / 11,760 / 11,760 | 0.139 / 0.151 / 0.157 / 0.163 | 0.063 / 0.073 / 0.078 / 0.080 | 0.135 / 0.138 / 0.139 / 0.139 | 0.186 / 0.191 / 0.194 / 0.195 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,148 / 1,148 / 1,148 / 1,148 | 0.179 / 0.221 / 0.253 / 0.263 | 0.040 / 0.042 / 0.045 / 0.049 | 0.154 / 0.161 / 0.167 / 0.168 | 0.222 / 0.222 / 0.226 / 0.227 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 220 / 220 / 220 / 220 | 0.177 / 0.227 / 0.231 / 0.232 | 0.270 / 0.250 / 0.250 / 0.250 | 0.091 / 0.099 / 0.100 / 0.100 | 0.201 / 0.196 / 0.204 / 0.204 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 526 / 526 / 526 / 526 | 0.155 / 0.177 / 0.191 / 0.197 | 0.145 / 0.145 / 0.146 / 0.145 | 0.230 / 0.237 / 0.240 / 0.241 | 0.570 / 0.595 / 0.592 / 0.592 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 4,045 / 4,045 / 4,045 / 4,045 | 0.110 / 0.117 / 0.126 / 0.154 | 0.397 / 0.401 / 0.402 / 0.410 | 0.212 / 0.213 / 0.213 / 0.213 | 0.276 / 0.275 / 0.276 / 0.279 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 23,528 / 24,950 / 25,596 / 25,735 | 0.155 / 0.180 / 0.198 / 0.216 | 0.093 / 0.117 / 0.133 / 0.137 | 0.147 / 0.152 / 0.153 / 0.154 | 0.214 / 0.228 / 0.234 / 0.235 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,866 / 2,196 / 2,328 / 2,367 | 0.189 / 0.251 / 0.308 / 0.333 | 0.043 / 0.054 / 0.061 / 0.073 | 0.161 / 0.172 / 0.181 / 0.184 | 0.222 / 0.230 / 0.230 / 0.234 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 379 / 486 / 490 / 490 | 0.187 / 0.278 / 0.288 / 0.288 | 0.246 / 0.230 / 0.229 / 0.229 | 0.095 / 0.110 / 0.112 / 0.112 | 0.215 / 0.212 / 0.234 / 0.234 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 874 / 918 / 941 / 944 | 0.156 / 0.179 / 0.199 / 0.206 | 0.151 / 0.153 / 0.162 / 0.161 | 0.231 / 0.238 / 0.243 / 0.244 | 0.577 / 0.602 / 0.594 / 0.593 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 7,461 / 7,553 / 7,587 / 7,622 | 0.116 / 0.125 / 0.136 / 0.167 | 0.429 / 0.432 / 0.433 / 0.442 | 0.213 / 0.214 / 0.215 / 0.215 | 0.287 / 0.286 / 0.287 / 0.291 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 19 / 22 / 22 / 23 | 0.156 / 0.203 / 0.203 / 0.324 | 1.731 / 1.638 / 1.638 / 1.580 | 0.430 / 0.472 / 0.472 / 0.487 | 0.079 / 0.082 / 0.082 / 0.083 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +--- + +**J6Gen2**: db_j6gen2_v1 + db_j6gen2_v2 + db_j6gen2_v3 + db_j6gen2_v4 + db_j6gen2_v5 + db_j6gen2_v6 + db_j6gen2_v7 + db_j6gen2_v8 + db_j6gen2_v9 + db_j6gen2_v10 + db_j6gen2_v11 + db_j6gen2_v12 (4,682 frames) + +**Total BEV Center Distance mAP (eval range = 0.0 - 50.0m): 0.7371** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 60,938 | 53,537 / 55,808 / 56,797 / 57,392 | 0.844 / 0.892 / 0.914 / 0.925 | 0.898 / 0.925 / 0.933 / 0.938 | 0.251 / 0.220 / 0.157 / 0.147 | +| truck | 7,081 | 5,634 / 6,249 / 6,533 / 6,699 | 0.712 / 0.833 / 0.886 / 0.916 | 0.797 / 0.870 / 0.900 / 0.919 | 0.263 / 0.186 / 0.186 / 0.184 | +| bus | 2,370 | 2,078 / 2,230 / 2,293 / 2,305 | 0.822 / 0.913 / 0.952 / 0.963 | 0.885 / 0.940 / 0.961 / 0.963 | 0.243 / 0.168 / 0.153 / 0.153 | +| bicycle | 1,357 | 1,274 / 1,285 / 1,286 / 1,286 | 0.895 / 0.911 / 0.912 / 0.912 | 0.896 / 0.904 / 0.905 / 0.905 | 0.158 / 0.158 / 0.158 / 0.158 | +| pedestrian | 18,202 | 16,664 / 17,018 / 17,157 / 17,273 | 0.827 / 0.852 / 0.865 / 0.872 | 0.828 / 0.842 / 0.849 / 0.855 | 0.171 / 0.166 / 0.163 / 0.166 | +| traffic_cone | 8,250 | 5,459 / 5,894 / 6,075 / 6,309 | 0.430 / 0.490 / 0.511 / 0.545 | 0.582 / 0.619 / 0.634 / 0.654 | 0.123 / 0.110 / 0.095 / 0.086 | +| barrier | 1,350 | 572 / 754 / 803 / 843 | 0.175 / 0.270 / 0.292 / 0.310 | 0.410 / 0.464 / 0.474 / 0.485 | 0.283 / 0.248 / 0.248 / 0.248 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 6,703 / 6,703 / 6,703 / 6,703 | 0.107 / 0.111 / 0.113 / 0.117 | 0.031 / 0.034 / 0.036 / 0.036 | 0.116 / 0.117 / 0.118 / 0.118 | 0.123 / 0.124 / 0.125 / 0.126 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 778 / 778 / 778 / 778 | 0.144 / 0.166 / 0.182 / 0.204 | 0.028 / 0.030 / 0.031 / 0.031 | 0.128 / 0.132 / 0.135 / 0.137 | 0.312 / 0.322 / 0.328 / 0.327 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 260 / 260 / 260 / 260 | 0.105 / 0.115 / 0.137 / 0.139 | 0.026 / 0.027 / 0.028 / 0.028 | 0.083 / 0.085 / 0.091 / 0.091 | 0.122 / 0.125 / 0.123 / 0.124 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 149 / 149 / 149 / 149 | 0.129 / 0.133 / 0.133 / 0.133 | 0.067 / 0.068 / 0.068 / 0.068 | 0.194 / 0.194 / 0.194 / 0.194 | 0.530 / 0.530 / 0.530 / 0.530 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 2,002 / 2,002 / 2,002 / 2,002 | 0.105 / 0.111 / 0.121 / 0.138 | 0.427 / 0.429 / 0.433 / 0.435 | 0.240 / 0.241 / 0.241 / 0.241 | 0.236 / 0.236 / 0.236 / 0.237 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 907 / 907 / 907 / 907 | 0.176 / 0.198 / 0.219 / 0.296 | 0.327 / 0.324 / 0.326 / 0.327 | 0.645 / 0.648 / 0.649 / 0.650 | 0.026 / 0.026 / 0.026 / 0.026 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 148 / 148 / 148 / 148 | 0.232 / 0.293 / 0.318 / 0.363 | 0.374 / 0.376 / 0.378 / 0.375 | 0.458 / 0.477 / 0.484 / 0.492 | 0.024 / 0.025 / 0.025 / 0.025 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 24,984 / 24,984 / 24,984 / 24,984 | 0.115 / 0.121 / 0.124 / 0.130 | 0.036 / 0.040 / 0.042 / 0.043 | 0.122 / 0.123 / 0.123 / 0.124 | 0.136 / 0.137 / 0.138 / 0.139 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,903 / 2,903 / 2,903 / 2,903 | 0.153 / 0.181 / 0.204 / 0.235 | 0.031 / 0.034 / 0.035 / 0.036 | 0.132 / 0.138 / 0.142 / 0.145 | 0.336 / 0.345 / 0.352 / 0.350 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 971 / 971 / 971 / 971 | 0.113 / 0.129 / 0.146 / 0.148 | 0.030 / 0.032 / 0.032 / 0.033 | 0.086 / 0.089 / 0.093 / 0.094 | 0.140 / 0.142 / 0.141 / 0.141 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 556 / 556 / 556 / 556 | 0.128 / 0.131 / 0.131 / 0.132 | 0.070 / 0.070 / 0.070 / 0.070 | 0.200 / 0.200 / 0.201 / 0.201 | 0.558 / 0.558 / 0.558 / 0.558 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 7,462 / 7,462 / 7,462 / 7,462 | 0.109 / 0.117 / 0.132 / 0.154 | 0.455 / 0.457 / 0.462 / 0.464 | 0.245 / 0.247 / 0.247 / 0.248 | 0.241 / 0.240 / 0.241 / 0.242 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 3,382 / 3,382 / 3,382 / 3,382 | 0.187 / 0.215 / 0.248 / 0.352 | 0.373 / 0.367 / 0.367 / 0.369 | 0.655 / 0.659 / 0.660 / 0.661 | 0.028 / 0.028 / 0.027 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 553 / 553 / 553 / 553 | 0.251 / 0.333 / 0.368 / 0.443 | 0.422 / 0.406 / 0.410 / 0.403 | 0.542 / 0.541 / 0.547 / 0.554 | 0.025 / 0.025 / 0.025 / 0.025 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 52,338 / 54,149 / 55,189 / 55,565 | 0.130 / 0.146 / 0.161 / 0.177 | 0.050 / 0.061 / 0.071 / 0.074 | 0.131 / 0.133 / 0.134 / 0.135 | 0.148 / 0.151 / 0.153 / 0.153 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 5,350 / 5,996 / 6,202 / 6,337 | 0.164 / 0.212 / 0.249 / 0.305 | 0.041 / 0.055 / 0.055 / 0.058 | 0.141 / 0.153 / 0.158 / 0.164 | 0.333 / 0.345 / 0.353 / 0.353 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,027 / 2,173 / 2,227 / 2,232 | 0.134 / 0.168 / 0.193 / 0.202 | 0.049 / 0.054 / 0.059 / 0.061 | 0.095 / 0.101 / 0.106 / 0.106 | 0.171 / 0.166 / 0.166 / 0.168 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,170 / 1,181 / 1,182 / 1,182 | 0.131 / 0.137 / 0.137 / 0.137 | 0.076 / 0.077 / 0.077 / 0.077 | 0.209 / 0.210 / 0.210 / 0.210 | 0.563 / 0.562 / 0.563 / 0.563 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 14,547 / 14,883 / 15,058 / 15,106 | 0.115 / 0.128 / 0.149 / 0.185 | 0.478 / 0.482 / 0.488 / 0.491 | 0.251 / 0.253 / 0.254 / 0.254 | 0.248 / 0.248 / 0.249 / 0.250 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 4,519 / 4,942 / 5,249 / 5,546 | 0.187 / 0.219 / 0.265 / 0.403 | 0.382 / 0.379 / 0.382 / 0.383 | 0.654 / 0.661 / 0.666 / 0.670 | 0.028 / 0.028 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 453 / 538 / 550 / 562 | 0.240 / 0.301 / 0.323 / 0.375 | 0.394 / 0.397 / 0.395 / 0.389 | 0.491 / 0.508 / 0.510 / 0.516 | 0.023 / 0.024 / 0.024 / 0.024 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +**Total BEV Center Distance mAP (eval range = 50.0 - 90.0m): 0.5833** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 54,217 | 41,798 / 46,774 / 49,152 / 50,157 | 0.682 / 0.803 / 0.855 / 0.877 | 0.772 / 0.841 / 0.868 / 0.877 | 0.242 / 0.189 / 0.157 / 0.157 | +| truck | 4,913 | 2,974 / 3,651 / 4,060 / 4,258 | 0.440 / 0.619 / 0.724 / 0.772 | 0.610 / 0.720 / 0.775 / 0.797 | 0.249 / 0.164 / 0.164 / 0.164 | +| bus | 2,116 | 1,429 / 1,813 / 1,969 / 2,024 | 0.559 / 0.788 / 0.892 / 0.918 | 0.675 / 0.828 / 0.885 / 0.904 | 0.415 / 0.184 / 0.171 / 0.181 | +| bicycle | 838 | 666 / 706 / 708 / 709 | 0.642 / 0.708 / 0.713 / 0.716 | 0.723 / 0.752 / 0.755 / 0.756 | 0.110 / 0.136 / 0.136 / 0.110 | +| pedestrian | 8,336 | 7,155 / 7,320 / 7,400 / 7,475 | 0.621 / 0.643 / 0.660 / 0.674 | 0.681 / 0.691 / 0.698 / 0.704 | 0.145 / 0.155 / 0.155 / 0.148 | +| traffic_cone | 2,632 | 1,306 / 1,440 / 1,508 / 1,612 | 0.231 / 0.280 / 0.312 / 0.364 | 0.450 / 0.492 / 0.509 / 0.540 | 0.079 / 0.085 / 0.075 / 0.075 | +| barrier | 622 | 216 / 296 / 314 / 328 | 0.118 / 0.226 / 0.242 / 0.252 | 0.335 / 0.427 / 0.438 / 0.445 | 0.183 / 0.106 / 0.106 / 0.082 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 5,963 / 5,963 / 5,963 / 5,963 | 0.158 / 0.180 / 0.197 / 0.212 | 0.113 / 0.144 / 0.163 / 0.167 | 0.160 / 0.163 / 0.164 / 0.164 | 0.148 / 0.149 / 0.149 / 0.150 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 540 / 540 / 540 / 540 | 0.192 / 0.244 / 0.299 / 0.344 | 0.034 / 0.037 / 0.041 / 0.043 | 0.153 / 0.166 / 0.173 / 0.178 | 0.472 / 0.474 / 0.475 / 0.481 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 232 / 232 / 232 / 232 | 0.151 / 0.201 / 0.235 / 0.248 | 0.115 / 0.107 / 0.111 / 0.119 | 0.117 / 0.125 / 0.131 / 0.132 | 0.140 / 0.147 / 0.147 / 0.147 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 92 / 92 / 92 / 92 | 0.169 / 0.186 / 0.190 / 0.195 | 0.123 / 0.127 / 0.127 / 0.127 | 0.208 / 0.212 / 0.212 / 0.212 | 0.647 / 0.643 / 0.642 / 0.641 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 916 / 916 / 916 / 916 | 0.117 / 0.127 / 0.152 / 0.197 | 0.592 / 0.598 / 0.603 / 0.612 | 0.229 / 0.230 / 0.230 / 0.230 | 0.295 / 0.295 / 0.296 / 0.299 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 289 / 289 / 289 / 289 | 0.190 / 0.223 / 0.313 / 0.573 | 0.271 / 0.283 / 0.285 / 0.308 | 0.686 / 0.691 / 0.692 / 0.692 | 0.043 / 0.044 / 0.044 / 0.044 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 68 / 68 / 68 / 68 | 0.247 / 0.336 / 0.361 / 0.416 | 0.375 / 0.360 / 0.359 / 0.359 | 0.456 / 0.473 / 0.481 / 0.485 | 0.032 / 0.033 / 0.034 / 0.034 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 22,228 / 22,228 / 22,228 / 22,228 | 0.169 / 0.199 / 0.221 / 0.242 | 0.135 / 0.174 / 0.198 / 0.203 | 0.166 / 0.169 / 0.170 / 0.171 | 0.155 / 0.156 / 0.156 / 0.157 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,014 / 2,014 / 2,014 / 2,014 | 0.202 / 0.268 / 0.337 / 0.393 | 0.045 / 0.047 / 0.051 / 0.054 | 0.164 / 0.177 / 0.186 / 0.192 | 0.482 / 0.489 / 0.493 / 0.501 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 867 / 867 / 867 / 867 | 0.170 / 0.238 / 0.284 / 0.303 | 0.082 / 0.083 / 0.092 / 0.105 | 0.126 / 0.135 / 0.143 / 0.144 | 0.156 / 0.161 / 0.160 / 0.160 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 343 / 343 / 343 / 343 | 0.179 / 0.198 / 0.204 / 0.212 | 0.147 / 0.150 / 0.150 / 0.150 | 0.209 / 0.213 / 0.213 / 0.213 | 0.663 / 0.657 / 0.656 / 0.655 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 3,417 / 3,417 / 3,417 / 3,417 | 0.123 / 0.135 / 0.161 / 0.214 | 0.621 / 0.626 / 0.632 / 0.642 | 0.230 / 0.231 / 0.231 / 0.232 | 0.321 / 0.319 / 0.320 / 0.324 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,079 / 1,079 / 1,079 / 1,079 | 0.205 / 0.249 / 0.352 / 0.633 | 0.355 / 0.365 / 0.362 / 0.382 | 0.690 / 0.696 / 0.697 / 0.698 | 0.053 / 0.051 / 0.051 / 0.052 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 255 / 255 / 255 | 1.000 / 0.380 / 0.432 / 0.563 | 1.000 / 0.422 / 0.413 / 0.407 | 1.000 / 0.546 / 0.552 / 0.554 | 1.000 / 0.036 / 0.036 / 0.036 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 39,059 / 43,688 / 45,797 / 46,308 | 0.180 / 0.225 / 0.264 / 0.299 | 0.165 / 0.216 / 0.249 / 0.256 | 0.171 / 0.176 / 0.177 / 0.178 | 0.164 / 0.167 / 0.169 / 0.169 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,628 / 3,307 / 3,561 / 3,660 | 0.205 / 0.286 / 0.364 / 0.427 | 0.047 / 0.056 / 0.059 / 0.060 | 0.167 / 0.185 / 0.195 / 0.201 | 0.480 / 0.500 / 0.512 / 0.518 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,261 / 1,700 / 1,825 / 1,855 | 0.179 / 0.290 / 0.353 / 0.413 | 0.078 / 0.086 / 0.109 / 0.153 | 0.129 / 0.143 / 0.151 / 0.154 | 0.164 / 0.163 / 0.163 / 0.163 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 584 / 579 / 581 / 611 | 0.182 / 0.202 / 0.207 / 0.216 | 0.167 / 0.158 / 0.157 / 0.168 | 0.213 / 0.217 / 0.216 / 0.216 | 0.672 / 0.661 / 0.659 / 0.664 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 5,589 / 5,531 / 5,588 / 5,745 | 0.123 / 0.135 / 0.159 / 0.216 | 0.625 / 0.626 / 0.632 / 0.647 | 0.229 / 0.230 / 0.230 / 0.231 | 0.326 / 0.323 / 0.325 / 0.332 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,142 / 1,223 / 1,314 / 1,394 | 0.203 / 0.248 / 0.349 / 0.635 | 0.349 / 0.369 / 0.370 / 0.395 | 0.689 / 0.696 / 0.697 / 0.699 | 0.051 / 0.050 / 0.053 / 0.054 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 156 / 231 / 237 / 268 | 0.255 / 0.361 / 0.392 / 0.529 | 0.380 / 0.406 / 0.399 / 0.397 | 0.461 / 0.502 / 0.509 / 0.539 | 0.033 / 0.036 / 0.036 / 0.036 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +**Total BEV Center Distance mAP (eval range = 90.0 - 121.0m): 0.4384** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 19,301 | 13,605 / 16,188 / 17,481 / 17,847 | 0.528 / 0.703 / 0.787 / 0.811 | 0.660 / 0.759 / 0.799 / 0.810 | 0.188 / 0.181 / 0.156 / 0.156 | +| truck | 2,906 | 1,284 / 1,828 / 2,299 / 2,482 | 0.229 / 0.431 / 0.643 / 0.716 | 0.442 / 0.598 / 0.722 / 0.760 | 0.158 / 0.159 / 0.111 / 0.111 | +| bus | 484 | 225 / 324 / 385 / 401 | 0.261 / 0.534 / 0.668 / 0.702 | 0.460 / 0.635 / 0.719 / 0.742 | 0.349 / 0.126 / 0.066 / 0.066 | +| bicycle | 291 | 215 / 246 / 251 / 252 | 0.381 / 0.577 / 0.588 / 0.590 | 0.535 / 0.629 / 0.633 / 0.633 | 0.136 / 0.136 / 0.136 / 0.136 | +| pedestrian | 2,564 | 2,056 / 2,093 / 2,118 / 2,143 | 0.455 / 0.470 / 0.480 / 0.488 | 0.572 / 0.580 / 0.585 / 0.589 | 0.133 / 0.128 / 0.128 / 0.128 | +| traffic_cone | 462 | 183 / 207 / 225 / 235 | 0.114 / 0.146 / 0.161 / 0.183 | 0.324 / 0.359 / 0.372 / 0.388 | 0.088 / 0.088 / 0.088 / 0.088 | +| barrier | 145 | 49 / 72 / 90 / 96 | 0.042 / 0.140 / 0.204 / 0.242 | 0.237 / 0.362 / 0.427 / 0.452 | 0.139 / 0.119 / 0.085 / 0.095 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 2,123 / 2,123 / 2,123 / 2,123 | 0.199 / 0.243 / 0.281 / 0.312 | 0.217 / 0.268 / 0.299 / 0.309 | 0.180 / 0.184 / 0.186 / 0.186 | 0.274 / 0.267 / 0.265 / 0.265 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 319 / 319 / 319 / 319 | 0.232 / 0.319 / 0.462 / 0.532 | 0.044 / 0.048 / 0.057 / 0.062 | 0.178 / 0.194 / 0.215 / 0.222 | 0.461 / 0.495 / 0.507 / 0.513 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 53 / 53 / 53 / 53 | 0.230 / 0.326 / 0.388 / 0.414 | 0.036 / 0.034 / 0.039 / 0.040 | 0.143 / 0.158 / 0.167 / 0.170 | 0.416 / 0.429 / 0.428 / 0.430 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 32 / 32 / 32 / 32 | 0.245 / 0.306 / 0.312 / 0.316 | 0.096 / 0.091 / 0.093 / 0.092 | 0.247 / 0.264 / 0.265 / 0.264 | 0.779 / 0.757 / 0.761 / 0.763 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 282 / 282 / 282 / 282 | 0.124 / 0.137 / 0.155 / 0.193 | 0.525 / 0.533 / 0.542 / 0.546 | 0.255 / 0.256 / 0.256 / 0.257 | 0.389 / 0.389 / 0.389 / 0.391 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 50 / 50 / 50 / 50 | 0.193 / 0.234 / 0.272 / 0.526 | 0.288 / 0.286 / 0.309 / 0.312 | 0.702 / 0.701 / 0.703 / 0.699 | 0.044 / 0.046 / 0.046 / 0.046 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 15 / 15 / 15 / 15 | 0.301 / 0.435 / 0.535 / 0.997 | 0.250 / 0.220 / 0.216 / 0.212 | 0.487 / 0.511 / 0.530 / 0.530 | 0.045 / 0.045 / 0.046 / 0.046 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 7,913 / 7,913 / 7,913 / 7,913 | 0.208 / 0.262 / 0.308 / 0.347 | 0.271 / 0.321 / 0.353 / 0.363 | 0.186 / 0.189 / 0.191 / 0.191 | 0.272 / 0.266 / 0.265 / 0.265 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,191 / 1,191 / 1,191 / 1,191 | 0.247 / 0.356 / 0.521 / 0.613 | 0.072 / 0.063 / 0.071 / 0.078 | 0.191 / 0.206 / 0.226 / 0.235 | 0.513 / 0.557 / 0.558 / 0.562 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 198 / 198 / 198 / 198 | 0.250 / 0.363 / 0.453 / 0.493 | 0.063 / 0.047 / 0.051 / 0.053 | 0.151 / 0.164 / 0.177 / 0.181 | 0.456 / 0.444 / 0.437 / 0.441 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 119 / 119 / 119 / 119 | 0.227 / 0.289 / 0.299 / 0.306 | 0.129 / 0.119 / 0.120 / 0.119 | 0.241 / 0.257 / 0.258 / 0.258 | 0.760 / 0.771 / 0.776 / 0.781 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,051 / 1,051 / 1,051 / 1,051 | 0.131 / 0.147 / 0.171 / 0.217 | 0.581 / 0.588 / 0.599 / 0.605 | 0.252 / 0.252 / 0.253 / 0.253 | 0.427 / 0.425 / 0.425 / 0.427 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 189 / 189 / 189 | 1.000 / 0.274 / 0.365 / 0.629 | 1.000 / 0.405 / 0.430 / 0.444 | 1.000 / 0.718 / 0.719 / 0.714 | 1.000 / 0.060 / 0.058 / 0.055 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 59 / 59 / 59 | 1.000 / 0.438 / 0.604 / 1.056 | 1.000 / 0.237 / 0.228 / 0.223 | 1.000 / 0.567 / 0.578 / 0.581 | 1.000 / 0.045 / 0.047 / 0.047 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 12,130 / 14,043 / 15,222 / 15,426 | 0.212 / 0.274 / 0.334 / 0.381 | 0.306 / 0.364 / 0.410 / 0.419 | 0.188 / 0.193 / 0.195 / 0.195 | 0.272 / 0.267 / 0.270 / 0.270 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,162 / 1,568 / 2,007 / 2,112 | 0.243 / 0.360 / 0.552 / 0.656 | 0.052 / 0.061 / 0.085 / 0.094 | 0.188 / 0.209 / 0.235 / 0.244 | 0.533 / 0.561 / 0.554 / 0.558 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 169 / 279 / 355 / 366 | 0.240 / 0.375 / 0.521 / 0.578 | 0.045 / 0.057 / 0.062 / 0.063 | 0.143 / 0.167 / 0.187 / 0.193 | 0.407 / 0.458 / 0.445 / 0.451 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 153 / 180 / 181 / 181 | 0.228 / 0.284 / 0.290 / 0.290 | 0.086 / 0.094 / 0.094 / 0.094 | 0.240 / 0.255 / 0.256 / 0.256 | 0.747 / 0.757 / 0.760 / 0.760 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,439 / 1,486 / 1,498 / 1,509 | 0.127 / 0.142 / 0.164 / 0.206 | 0.558 / 0.566 / 0.576 / 0.581 | 0.250 / 0.251 / 0.252 / 0.253 | 0.425 / 0.424 / 0.423 / 0.425 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 155 / 172 / 178 / 186 | 0.209 / 0.261 / 0.313 / 0.600 | 0.413 / 0.390 / 0.401 / 0.422 | 0.714 / 0.709 / 0.710 / 0.705 | 0.056 / 0.055 / 0.055 / 0.054 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 33 / 52 / 72 / 73 | 0.295 / 0.437 / 0.603 / 1.064 | 0.239 / 0.211 / 0.222 / 0.219 | 0.469 / 0.512 / 0.568 / 0.572 | 0.048 / 0.047 / 0.047 / 0.047 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +**Total BEV Center Distance mAP (eval range = 0.0 - 121.0m): 0.6650** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 134,456 | 109,151 / 119,093 / 123,838 / 125,849 | 0.751 / 0.842 / 0.886 / 0.900 | 0.816 / 0.870 / 0.890 / 0.897 | 0.260 / 0.189 / 0.157 / 0.157 | +| truck | 14,900 | 9,912 / 11,766 / 12,962 / 13,522 | 0.538 / 0.695 / 0.797 / 0.845 | 0.673 / 0.772 / 0.828 / 0.851 | 0.264 / 0.176 / 0.166 / 0.157 | +| bus | 4,970 | 3,740 / 4,384 / 4,667 / 4,750 | 0.669 / 0.836 / 0.909 / 0.931 | 0.761 / 0.869 / 0.909 / 0.919 | 0.314 / 0.182 / 0.172 / 0.126 | +| bicycle | 2,486 | 2,157 / 2,239 / 2,247 / 2,249 | 0.763 / 0.818 / 0.821 / 0.822 | 0.795 / 0.821 / 0.823 / 0.823 | 0.152 / 0.152 / 0.152 / 0.152 | +| pedestrian | 29,102 | 25,909 / 26,467 / 26,706 / 26,922 | 0.753 / 0.773 / 0.787 / 0.799 | 0.764 / 0.777 / 0.784 / 0.790 | 0.154 / 0.154 / 0.162 / 0.159 | +| traffic_cone | 11,344 | 6,960 / 7,555 / 7,823 / 8,173 | 0.372 / 0.429 / 0.451 / 0.493 | 0.542 / 0.580 / 0.595 / 0.619 | 0.112 / 0.086 / 0.089 / 0.086 | +| barrier | 2,117 | 839 / 1,125 / 1,212 / 1,272 | 0.145 / 0.244 / 0.267 / 0.284 | 0.367 / 0.441 / 0.453 / 0.462 | 0.274 / 0.185 / 0.185 / 0.182 | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 14,790 / 14,790 / 14,790 / 14,790 | 0.129 / 0.143 / 0.154 / 0.163 | 0.065 / 0.081 / 0.092 / 0.094 | 0.134 / 0.137 / 0.138 / 0.139 | 0.143 / 0.145 / 0.147 / 0.147 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,639 / 1,639 / 1,639 / 1,639 | 0.164 / 0.202 / 0.244 / 0.282 | 0.031 / 0.034 / 0.037 / 0.039 | 0.138 / 0.147 / 0.154 / 0.159 | 0.368 / 0.381 / 0.390 / 0.392 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 546 / 546 / 546 / 546 | 0.123 / 0.153 / 0.179 / 0.187 | 0.055 / 0.055 / 0.058 / 0.061 | 0.095 / 0.101 / 0.107 / 0.108 | 0.139 / 0.146 / 0.146 / 0.147 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 273 / 273 / 273 / 273 | 0.145 / 0.157 / 0.158 / 0.160 | 0.080 / 0.082 / 0.082 / 0.082 | 0.203 / 0.206 / 0.206 / 0.206 | 0.575 / 0.575 / 0.575 / 0.575 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 3,201 / 3,201 / 3,201 / 3,201 | 0.109 / 0.116 / 0.131 / 0.156 | 0.468 / 0.470 / 0.474 / 0.479 | 0.240 / 0.241 / 0.241 / 0.242 | 0.252 / 0.252 / 0.252 / 0.254 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,247 / 1,247 / 1,247 / 1,247 | 0.179 / 0.203 / 0.234 / 0.349 | 0.321 / 0.319 / 0.321 / 0.326 | 0.652 / 0.656 / 0.657 / 0.659 | 0.029 / 0.029 / 0.029 / 0.030 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 232 / 232 / 232 / 232 | 0.239 / 0.311 / 0.339 / 0.410 | 0.374 / 0.369 / 0.369 / 0.366 | 0.469 / 0.488 / 0.494 / 0.502 | 0.026 / 0.027 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 55,126 / 55,126 / 55,126 / 55,126 | 0.143 / 0.162 / 0.177 / 0.189 | 0.084 / 0.107 / 0.122 / 0.124 | 0.143 / 0.146 / 0.148 / 0.148 | 0.156 / 0.157 / 0.158 / 0.159 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,109 / 6,109 / 6,109 / 6,109 | 0.178 / 0.227 / 0.284 / 0.336 | 0.038 / 0.041 / 0.044 / 0.047 | 0.148 / 0.158 / 0.167 / 0.172 | 0.391 / 0.406 / 0.414 / 0.417 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 2,037 / 2,037 / 2,037 / 2,037 | 0.139 / 0.180 / 0.208 / 0.220 | 0.054 / 0.056 / 0.059 / 0.065 | 0.103 / 0.110 / 0.115 / 0.117 | 0.162 / 0.167 / 0.166 / 0.167 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,019 / 1,019 / 1,019 / 1,019 | 0.148 / 0.162 / 0.164 / 0.166 | 0.091 / 0.092 / 0.092 / 0.093 | 0.208 / 0.211 / 0.211 / 0.211 | 0.602 / 0.602 / 0.602 / 0.602 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 11,931 / 11,931 / 11,931 / 11,931 | 0.114 / 0.124 / 0.144 / 0.178 | 0.503 / 0.505 / 0.510 / 0.516 | 0.245 / 0.245 / 0.246 / 0.247 | 0.266 / 0.265 / 0.265 / 0.268 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 4,651 / 4,651 / 4,651 / 4,651 | 0.191 / 0.223 / 0.268 / 0.415 | 0.368 / 0.365 / 0.365 / 0.370 | 0.664 / 0.668 / 0.669 / 0.671 | 0.032 / 0.032 / 0.032 / 0.032 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 867 / 867 / 867 | 1.000 / 0.355 / 0.402 / 0.517 | 1.000 / 0.398 / 0.397 / 0.391 | 1.000 / 0.544 / 0.550 / 0.557 | 1.000 / 0.029 / 0.029 / 0.030 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 102,297 / 112,278 / 116,527 / 117,516 | 0.157 / 0.194 / 0.225 / 0.252 | 0.117 / 0.160 / 0.186 / 0.191 | 0.152 / 0.157 / 0.159 / 0.160 | 0.168 / 0.172 / 0.175 / 0.175 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 9,001 / 10,847 / 11,695 / 12,090 | 0.184 / 0.255 / 0.332 / 0.401 | 0.043 / 0.055 / 0.059 / 0.063 | 0.153 / 0.170 / 0.182 / 0.189 | 0.399 / 0.423 / 0.437 / 0.439 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 3,495 / 4,146 / 4,350 / 4,483 | 0.158 / 0.232 / 0.278 / 0.322 | 0.056 / 0.066 / 0.079 / 0.102 | 0.110 / 0.122 / 0.129 / 0.133 | 0.174 / 0.182 / 0.183 / 0.187 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,859 / 1,919 / 1,923 / 1,923 | 0.153 / 0.169 / 0.172 / 0.173 | 0.100 / 0.101 / 0.101 / 0.101 | 0.212 / 0.215 / 0.215 / 0.215 | 0.608 / 0.609 / 0.610 / 0.610 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 21,639 / 21,986 / 21,863 / 22,136 | 0.118 / 0.131 / 0.152 / 0.194 | 0.521 / 0.525 / 0.528 / 0.535 | 0.246 / 0.248 / 0.248 / 0.249 | 0.278 / 0.277 / 0.277 / 0.280 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 5,782 / 6,632 / 6,752 / 7,077 | 0.190 / 0.231 / 0.284 / 0.453 | 0.370 / 0.381 / 0.379 / 0.385 | 0.662 / 0.674 / 0.674 / 0.676 | 0.032 / 0.033 / 0.033 / 0.033 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 602 / 827 / 850 / 872 | 0.245 / 0.329 / 0.356 / 0.433 | 0.378 / 0.383 / 0.380 / 0.377 | 0.478 / 0.506 / 0.509 / 0.515 | 0.026 / 0.027 / 0.028 / 0.028 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+
+ +--- + ### BEVFusion-LiDAR J6Gen2_base/2.7.1
diff --git a/projects/BEVFusion/docs/BEVFusion-L/v2/jpntaxi_base.md b/projects/BEVFusion/docs/BEVFusion-L/v2/jpntaxi_base.md index fc9e2677d..45c5827d1 100644 --- a/projects/BEVFusion/docs/BEVFusion-L/v2/jpntaxi_base.md +++ b/projects/BEVFusion/docs/BEVFusion-L/v2/jpntaxi_base.md @@ -13,7 +13,7 @@ - **Total Frames: 5,179**
- jpntaxi_gen2 (9,975 frames) + jpntaxi_gen2 (10,687 frames) - `db_jpntaxigen2_v1` - `db_jpntaxigen2_v2` @@ -26,41 +26,437 @@
Eval Range: 0.0 - 50.0m - | Model version | mAP | mAPH | car
(42,789) | truck
(17,259) | bus
(3,437) | bicycle
(2,681) | pedestrian
(57,948) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR jpntaxi_base/2.7.1 | 0.8862 | 0.8586 | 0.9397 | 0.8591 | 0.8839 | 0.8264 | 0.9218 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(50,954) | truck
(18,624) | bus
(3,853) | bicycle
(3,768) | pedestrian
(70,699) | traffic_cone
(12,525) | barrier
(2,009) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 0.6765 | 0.6414 | 0.6054 | 0.6011 | 0.5878 | 0.5835 | 0.9267 | 0.8595 | 0.8713 | 0.7844 | 0.9097 | 0.3843 | 0.0000 |
Eval Range: 50.0 - 90.0m - | Model version | mAP | mAPH | car
(35,518) | truck
(22,550) | bus
(2,683) | bicycle
(1,607) | pedestrian
(27,240) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR jpntaxi_base/2.7.1 | 0.7125 | 0.6854 | 0.8453 | 0.6838 | 0.5362 | 0.6969 | 0.8003 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(41,196) | truck
(22,942) | bus
(3,033) | bicycle
(2,310) | pedestrian
(36,881) | traffic_cone
(7,183) | barrier
(1,847) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 0.5673 | 0.5339 | 0.5415 | 0.5344 | 0.5248 | 0.5177 | 0.8485 | 0.7006 | 0.5352 | 0.6574 | 0.8105 | 0.4191 | 0.0000 |
Eval Range: 90.0 - 121.0m - | Model version | mAP | mAPH | car
(16,524) | truck
(14,587) | bus
(2,476) | bicycle
(364) | pedestrian
(14,297) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR jpntaxi_base/2.7.1 | 0.6030 | 0.5762 | 0.6947 | 0.5260 | 0.5030 | 0.5321 | 0.7591 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(17,510) | truck
(14,707) | bus
(2,997) | bicycle
(566) | pedestrian
(16,580) | traffic_cone
(769) | barrier
(566) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 0.4071 | 0.3837 | 0.4400 | 0.4130 | 0.4284 | 0.4014 | 0.7505 | 0.5566 | 0.4693 | 0.3213 | 0.7219 | 0.0302 | 0.0000 |
Eval Range: 0.0 - 121.0m - | Model version | mAP | mAPH | car
(94,831) | truck
(54,396) | bus
(8,596) | bicycle
(4,652) | pedestrian
(99,485) | - | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | - | BEVFusion-LiDAR jpntaxi_base/2.7.1 | 0.7805 | 0.7527 | 0.8730 | 0.7118 | 0.6785 | 0.7655 | 0.8739 | + | Model version | mAP | mAPH | map_based_nds (recall @ 0.10) | map_based_nds (recall @ 0.40) | maph_based_nds (recall @ 0.10) | maph_based_nds (recall 0.40) | car
(109,660) | truck
(56,273) | bus
(9,883) | bicycle
(6,644) | pedestrian
(124,160) | traffic_cone
(20,477) | barrier
(4,422) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 0.6019 | 0.5676 | 0.5626 | 0.5563 | 0.5455 | 0.5392 | 0.8804 | 0.7288 | 0.6567 | 0.7104 | 0.8656 | 0.3711 | 0.0000 |
+### Mean TPError - JPNTaxi_gen2 + +- Recalls: `0.10`, `0.40`, `optimal` + +
+ Eval Range: 0.0 - 50.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 0.2719 | 0.2964 | 0.2975 | 0.4627 | 1.0000 | 0.2842 | 0.3077 | 0.3000 | 0.4803 | 1.0000 | 0.1861 | 0.1991 | 0.1904 | 0.4301 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 50,954) | truck
0.5/1.0/2.0/4.0
(GTs: 18,624) | bus
0.5/1.0/2.0/4.0
(GTs: 3,853) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,768) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 70,699) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 12,525) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,009) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 5,604 / 5,604 / 5,604 / 5,604 | 2,048 / 2,048 / 2,048 / 2,048 | 423 / 423 / 423 / 423 | 414 / 414 / 414 / 414 | 7,776 / 7,776 / 7,776 / 7,776 | 1,377 / 1,377 / 1,377 / 1,377 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 50,954) | truck
0.5/1.0/2.0/4.0
(GTs: 18,624) | bus
0.5/1.0/2.0/4.0
(GTs: 3,853) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,768) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 70,699) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 12,525) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,009) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 20,891 / 20,891 / 20,891 / 20,891 | 7,635 / 7,635 / 7,635 / 7,635 | 1,579 / 1,579 / 1,579 / 1,579 | 1,544 / 1,544 / 1,544 / 1,544 | 28,986 / 28,986 / 28,986 / 28,986 | 5,135 / 5,135 / 5,135 / 5,135 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 50,954) | truck
0.5/1.0/2.0/4.0
(GTs: 18,624) | bus
0.5/1.0/2.0/4.0
(GTs: 3,853) | bicycle
0.5/1.0/2.0/4.0
(GTs: 3,768) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 70,699) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 12,525) | barrier
0.5/1.0/2.0/4.0
(GTs: 2,009) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 44,532 / 46,751 / 47,578 / 47,681 | 14,348 / 16,107 / 17,107 / 17,466 | 3,000 / 3,436 / 3,542 / 3,547 | 2,845 / 2,913 / 2,876 / 2,876 | 60,539 / 61,514 / 62,277 / 62,441 | 6,486 / 6,824 / 7,125 / 7,398 | 0 / 0 / 0 / 0 | + +
+ +
+ Eval Range: 50.0 - 90.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 0.3372 | 0.2750 | 0.3096 | 0.4999 | 1.0000 | 0.3578 | 0.2816 | 0.3131 | 0.5396 | 1.0000 | 0.2676 | 0.1735 | 0.2026 | 0.5014 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 41,196) | truck
0.5/1.0/2.0/4.0
(GTs: 22,942) | bus
0.5/1.0/2.0/4.0
(GTs: 3,033) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,310) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 36,881) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 7,183) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,847) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 4,531 / 4,531 / 4,531 / 4,531 | 2,523 / 2,523 / 2,523 / 2,523 | 333 / 333 / 333 / 333 | 254 / 254 / 254 / 254 | 4,056 / 4,056 / 4,056 / 4,056 | 790 / 790 / 790 / 790 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 41,196) | truck
0.5/1.0/2.0/4.0
(GTs: 22,942) | bus
0.5/1.0/2.0/4.0
(GTs: 3,033) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,310) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 36,881) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 7,183) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,847) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 16,890 / 16,890 / 16,890 / 16,890 | 9,406 / 9,406 / 9,406 / 9,406 | 1,243 / 1,243 / 1,243 / 1,243 | 947 / 947 / 947 / 947 | 15,121 / 15,121 / 15,121 / 15,121 | 2,945 / 2,945 / 2,945 / 2,945 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 41,196) | truck
0.5/1.0/2.0/4.0
(GTs: 22,942) | bus
0.5/1.0/2.0/4.0
(GTs: 3,033) | bicycle
0.5/1.0/2.0/4.0
(GTs: 2,310) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 36,881) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 7,183) | barrier
0.5/1.0/2.0/4.0
(GTs: 1,847) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 31,946 / 34,752 / 35,481 / 35,761 | 13,234 / 15,760 / 17,748 / 18,656 | 1,143 / 1,712 / 1,912 / 1,948 | 1,403 / 1,491 / 1,492 / 1,475 | 28,761 / 29,331 / 29,372 / 29,481 | 3,696 / 3,847 / 3,872 / 4,000 | 0 / 0 / 0 / 0 | + +
+ +
+ Eval Range: 90.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 0.3797 | 0.3240 | 0.3484 | 0.5829 | 1.0000 | 0.4651 | 0.3644 | 0.3942 | 0.6814 | 1.0000 | 0.3149 | 0.2340 | 0.2477 | 0.5670 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 17,510) | truck
0.5/1.0/2.0/4.0
(GTs: 14,707) | bus
0.5/1.0/2.0/4.0
(GTs: 2,997) | bicycle
0.5/1.0/2.0/4.0
(GTs: 566) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 16,580) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 769) | barrier
0.5/1.0/2.0/4.0
(GTs: 566) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 1,926 / 1,926 / 1,926 / 1,926 | 1,617 / 1,617 / 1,617 / 1,617 | 329 / 329 / 329 / 329 | 62 / 62 / 62 / 62 | 1,823 / 1,823 / 1,823 / 1,823 | 84 / 84 / 84 / 84 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 17,510) | truck
0.5/1.0/2.0/4.0
(GTs: 14,707) | bus
0.5/1.0/2.0/4.0
(GTs: 2,997) | bicycle
0.5/1.0/2.0/4.0
(GTs: 566) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 16,580) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 769) | barrier
0.5/1.0/2.0/4.0
(GTs: 566) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 7,179 / 7,179 / 7,179 / 7,179 | 6,029 / 6,029 / 6,029 / 6,029 | 1,228 / 1,228 / 1,228 / 1,228 | 232 / 232 / 232 / 232 | 6,797 / 6,797 / 6,797 / 6,797 | 0 / 0 / 315 / 315 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 17,510) | truck
0.5/1.0/2.0/4.0
(GTs: 14,707) | bus
0.5/1.0/2.0/4.0
(GTs: 2,997) | bicycle
0.5/1.0/2.0/4.0
(GTs: 566) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 16,580) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 769) | barrier
0.5/1.0/2.0/4.0
(GTs: 566) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 11,780 / 13,293 / 14,056 / 14,133 | 5,970 / 8,371 / 10,098 / 11,087 | 1,279 / 1,474 / 1,872 / 1,945 | 222 / 261 / 241 / 246 | 11,842 / 12,158 / 12,274 / 12,415 | 148 / 150 / 155 / 167 | 0 / 0 / 0 / 0 | + +
+ +
+ Eval Range: 0.0 - 121.0m + + | Model version | mATE (recall @ 0.10) | mAOE (recall @ 0.10) | mASE (recall @ 0.10) | mAVE (recall @ 0.10) | mAAE (recall @ 0.10) | mATE (recall @ 0.40) | mAOE (recall @ 0.40) | mASE (recall @ 0.40) | mAVE (recall @ 0.40) | mAAE (recall @ 0.40) | mATE (optimal) | mAOE (optimal) | mASE (optimal) | mAVE (optimal) | mAAE (optimal) | + | :---- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 0.3015 | 0.2921 | 0.3048 | 0.4845 | 1.0000 | 0.3235 | 0.3009 | 0.3096 | 0.5122 | 1.0000 | 0.2333 | 0.1943 | 0.2011 | 0.4683 | 1.0000 | + + Num match summary + + **recall 0.10** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 109,660) | truck
0.5/1.0/2.0/4.0
(GTs: 56,273) | bus
0.5/1.0/2.0/4.0
(GTs: 9,883) | bicycle
0.5/1.0/2.0/4.0
(GTs: 6,644) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 124,160) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,477) | barrier
0.5/1.0/2.0/4.0
(GTs: 4,422) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 12,062 / 12,062 / 12,062 / 12,062 | 6,190 / 6,190 / 6,190 / 6,190 | 1,087 / 1,087 / 1,087 / 1,087 | 730 / 730 / 730 / 730 | 13,657 / 13,657 / 13,657 / 13,657 | 2,252 / 2,252 / 2,252 / 2,252 | 0 / 0 / 0 / 0 | + + **recall 0.40** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 109,660) | truck
0.5/1.0/2.0/4.0
(GTs: 56,273) | bus
0.5/1.0/2.0/4.0
(GTs: 9,883) | bicycle
0.5/1.0/2.0/4.0
(GTs: 6,644) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 124,160) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,477) | barrier
0.5/1.0/2.0/4.0
(GTs: 4,422) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 44,960 / 44,960 / 44,960 / 44,960 | 23,071 / 23,071 / 23,071 / 23,071 | 4,052 / 4,052 / 4,052 / 4,052 | 2,724 / 2,724 / 2,724 / 2,724 | 50,905 / 50,905 / 50,905 / 50,905 | 8,395 / 8,395 / 8,395 / 8,395 | 0 / 0 / 0 / 0 | + + **optimal** + + | Model version | car
0.5/1.0/2.0/4.0
(GTs: 109,660) | truck
0.5/1.0/2.0/4.0
(GTs: 56,273) | bus
0.5/1.0/2.0/4.0
(GTs: 9,883) | bicycle
0.5/1.0/2.0/4.0
(GTs: 6,644) | pedestrian
0.5/1.0/2.0/4.0
(GTs: 124,160) | traffic_cone
0.5/1.0/2.0/4.0
(GTs: 20,477) | barrier
0.5/1.0/2.0/4.0
(GTs: 4,422) | + | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | + | BEVFusion-LiDAR jpntaxi_base/2.8.1 | 87,589 / 94,880 / 97,103 / 97,882 | 33,139 / 40,397 / 44,749 / 47,200 | 4,572 / 6,029 / 7,207 / 7,319 | 4,479 / 4,726 / 4,742 / 4,763 | 100,710 / 102,804 / 103,519 / 104,226 | 10,025 / 10,672 / 11,043 / 11,437 | 0 / 0 / 0 / 0 | + +
+ + ## Release +### BEVFusion-LiDAR JPNTaxi_base/2.8.1 + +
+ Changes + +- Finetune from `BEVFusion-LiDAR base/2.7.0` with JPNTaxi_base dataset and intensity. +
+ +
+ Artifacts + +- Deployed onnx and ROS parameter files (for internal) + - [WebAuto](https://evaluation.ci.tier4.jp/evaluation/mlpackages/46f8188d-e3be-4f2f-b989-fd27002610d7/releases/73d62897-9beb-400f-a2bf-5af234da909d?project_id=zWhWRzei) + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/jpntaxi_base/v2.8.1/deployment.zip) + - [Google drive](https://drive.google.com/file/d/1UaTEqJlKMzLh2e4h5C3wl0ZoGdn9gIn-/view?usp=drive_link) +- Logs (for internal) + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/jpntaxi_base/v2.8.1/logs.zip) + - [Google drive](https://drive.google.com/file/d/1J27_CDEokWZD6s3aPzxuYS7mcdI-HqtW/view?usp=drive_link) +- Pytorch Best checkpoints: + - [model-zoo](https://download.autoware-ml-model-zoo.tier4.jp/autoware-ml/models/bevfusion/bevfusion-l/jpntaxi_base/v2.8.1/best_epoch_30.zip) + - [Google drive](https://drive.google.com/file/d/1Oe6aZq4N_fL7ejhploDm2sm440JDOLvw/view?usp=drive_link) + +
+ +
+ Training configs + +- [Config file path](https://github.com/KSeangTan/AWML/blob/00a0422d09f4a6ff8d5180c2df700883cb212d9f/projects/BEVFusion/configs/t4dataset/BEVFusion-L/bevfusion_lidar_voxel_second_secfpn_30e_8xb16_j6gen2_base_120m_t4metric_v2.py) +- Train time: NVIDIA H200 140GB * 8 * 30 epochs = 20 hours +- Batch size: 16*8 = 128 +- Training Dataset (frames: 58,329): + - jpntaxi: db_jpntaxi_v1 + db_jpntaxi_v2 + db_jpntaxi_v4 (28,161 frames) + jpntaxi_gen2: db_jpntaxigen2_v1 + db_jpntaxigen2_v2 (30,168 frames) + +
+ +
+ Evaluation + +**JPNTaxi_gen2 Datasets (10,687 frames)**: + + - jpntaxi_gen2 (10,687 frames): db_jpntaxigen2_v1 + db_jpntaxigen2_v2 + +**Total BEV Center Distance mAP (eval range = 0.0 - 50.0m): 0.6765** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 50,954 | 46,144 / 48,579 / 49,273 / 49,538 | 0.868 / 0.934 / 0.947 / 0.957 | 0.909 / 0.941 / 0.947 / 0.950 | 0.315 / 0.210 / 0.157 / 0.157 | +| truck | 18,624 | 14,858 / 16,616 / 17,746 / 18,084 | 0.697 / 0.851 / 0.933 / 0.957 | 0.808 / 0.893 / 0.939 / 0.954 | 0.316 / 0.228 / 0.185 / 0.167 | +| bus | 3,853 | 3,238 / 3,521 / 3,636 / 3,647 | 0.787 / 0.875 / 0.911 / 0.912 | 0.808 / 0.872 / 0.897 / 0.899 | 0.056 / 0.033 / 0.033 / 0.033 | +| bicycle | 3,768 | 3,284 / 3,339 / 3,343 / 3,343 | 0.771 / 0.788 / 0.789 / 0.789 | 0.808 / 0.815 / 0.816 / 0.816 | 0.253 / 0.229 / 0.251 / 0.251 | +| pedestrian | 70,699 | 66,395 / 67,135 / 67,375 / 67,695 | 0.893 / 0.907 / 0.918 / 0.921 | 0.884 / 0.893 / 0.900 / 0.905 | 0.159 / 0.153 / 0.147 / 0.151 | +| traffic_cone | 12,525 | 8,566 / 9,087 / 9,426 / 9,627 | 0.338 / 0.367 / 0.402 / 0.430 | 0.517 / 0.534 / 0.558 / 0.579 | 0.133 / 0.127 / 0.127 / 0.127 | +| barrier | 2,009 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 5,604 / 5,604 / 5,604 / 5,604 | 0.104 / 0.112 / 0.114 / 0.120 | 0.023 / 0.024 / 0.025 / 0.025 | 0.104 / 0.105 / 0.106 / 0.106 | 0.326 / 0.331 / 0.333 / 0.334 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,048 / 2,048 / 2,048 / 2,048 | 0.133 / 0.170 / 0.193 / 0.205 | 0.015 / 0.016 / 0.016 / 0.016 | 0.112 / 0.121 / 0.125 / 0.127 | 0.366 / 0.367 / 0.377 / 0.381 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 423 / 423 / 423 / 423 | 0.152 / 0.162 / 0.169 / 0.170 | 0.016 / 0.016 / 0.017 / 0.017 | 0.107 / 0.109 / 0.110 / 0.110 | 0.253 / 0.264 / 0.267 / 0.267 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 414 / 414 / 414 / 414 | 0.125 / 0.128 / 0.128 / 0.129 | 0.089 / 0.089 / 0.089 / 0.090 | 0.194 / 0.195 / 0.195 / 0.195 | 0.740 / 0.744 / 0.745 / 0.746 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 7,776 / 7,776 / 7,776 / 7,776 | 0.106 / 0.108 / 0.113 / 0.120 | 0.239 / 0.240 / 0.242 / 0.244 | 0.231 / 0.231 / 0.231 / 0.231 | 0.425 / 0.425 / 0.426 / 0.427 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 1,377 / 1,377 / 1,377 / 1,377 | 0.133 / 0.148 / 0.227 / 0.344 | 0.684 / 0.693 / 0.693 / 0.679 | 0.322 / 0.322 / 0.321 / 0.319 | 0.103 / 0.103 / 0.102 / 0.102 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 20,891 / 20,891 / 20,891 / 20,891 | 0.111 / 0.122 / 0.125 / 0.131 | 0.025 / 0.027 / 0.028 / 0.028 | 0.107 / 0.109 / 0.109 / 0.109 | 0.353 / 0.358 / 0.360 / 0.361 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 7,635 / 7,635 / 7,635 / 7,635 | 0.142 / 0.181 / 0.213 / 0.230 | 0.016 / 0.017 / 0.017 / 0.018 | 0.116 / 0.125 / 0.131 / 0.133 | 0.423 / 0.423 / 0.434 / 0.438 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,579 / 1,579 / 1,579 / 1,579 | 0.154 / 0.169 / 0.180 / 0.182 | 0.017 / 0.017 / 0.018 / 0.018 | 0.110 / 0.113 / 0.115 / 0.115 | 0.305 / 0.313 / 0.315 / 0.316 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,544 / 1,544 / 1,544 / 1,544 | 0.131 / 0.135 / 0.136 / 0.137 | 0.097 / 0.098 / 0.098 / 0.099 | 0.198 / 0.199 / 0.199 / 0.199 | 0.719 / 0.724 / 0.726 / 0.726 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 28,986 / 28,986 / 28,986 / 28,986 | 0.111 / 0.114 / 0.121 / 0.132 | 0.246 / 0.247 / 0.250 / 0.253 | 0.233 / 0.234 / 0.234 / 0.234 | 0.438 / 0.438 / 0.438 / 0.440 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 5,135 / 5,135 / 5,135 / 5,135 | 0.147 / 0.170 / 0.268 / 0.415 | 0.746 / 0.755 / 0.749 / 0.732 | 0.320 / 0.321 / 0.319 / 0.318 | 0.100 / 0.100 / 0.099 / 0.099 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 44,532 / 46,751 / 47,578 / 47,681 | 0.125 / 0.145 / 0.157 / 0.168 | 0.032 / 0.040 / 0.047 / 0.047 | 0.113 / 0.118 / 0.119 / 0.119 | 0.398 / 0.415 / 0.427 / 0.427 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 14,348 / 16,107 / 17,107 / 17,466 | 0.153 / 0.208 / 0.267 / 0.314 | 0.019 / 0.022 / 0.023 / 0.024 | 0.123 / 0.135 / 0.145 / 0.151 | 0.489 / 0.513 / 0.550 / 0.568 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 3,000 / 3,436 / 3,542 / 3,547 | 0.159 / 0.199 / 0.229 / 0.234 | 0.018 / 0.020 / 0.022 / 0.022 | 0.116 / 0.127 / 0.131 / 0.131 | 0.358 / 0.367 / 0.367 / 0.368 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 2,845 / 2,913 / 2,876 / 2,876 | 0.136 / 0.141 / 0.141 / 0.142 | 0.107 / 0.108 / 0.108 / 0.109 | 0.203 / 0.204 / 0.204 / 0.204 | 0.712 / 0.716 / 0.719 / 0.720 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 60,539 / 61,514 / 62,277 / 62,441 | 0.118 / 0.127 / 0.142 / 0.169 | 0.262 / 0.267 / 0.271 / 0.277 | 0.237 / 0.238 / 0.238 / 0.239 | 0.452 / 0.451 / 0.452 / 0.454 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 6,486 / 6,824 / 7,125 / 7,398 | 0.144 / 0.165 / 0.266 / 0.419 | 0.732 / 0.740 / 0.735 / 0.725 | 0.320 / 0.321 / 0.318 / 0.316 | 0.100 / 0.100 / 0.099 / 0.099 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +**Total BEV Center Distance mAP (eval range = 50.0 - 90.0m): 0.5673** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 41,196 | 34,194 / 37,651 / 39,026 / 39,360 | 0.757 / 0.853 / 0.887 / 0.898 | 0.807 / 0.862 / 0.880 / 0.884 | 0.201 / 0.166 / 0.166 / 0.159 | +| truck | 22,942 | 14,460 / 17,423 / 19,681 / 20,483 | 0.506 / 0.667 / 0.794 / 0.835 | 0.651 / 0.753 / 0.828 / 0.849 | 0.255 / 0.200 / 0.160 / 0.127 | +| bus | 3,033 | 1,394 / 2,043 / 2,373 / 2,438 | 0.234 / 0.531 / 0.676 / 0.700 | 0.434 / 0.642 / 0.726 / 0.740 | 0.094 / 0.086 / 0.094 / 0.094 | +| bicycle | 2,310 | 1,834 / 1,933 / 1,939 / 1,942 | 0.612 / 0.668 / 0.669 / 0.680 | 0.683 / 0.712 / 0.713 / 0.719 | 0.168 / 0.155 / 0.155 / 0.170 | +| pedestrian | 36,881 | 33,944 / 34,428 / 34,559 / 34,739 | 0.793 / 0.808 / 0.815 / 0.826 | 0.794 / 0.803 / 0.808 / 0.814 | 0.162 / 0.156 / 0.159 / 0.161 | +| traffic_cone | 7,183 | 5,066 / 5,255 / 5,320 / 5,413 | 0.375 / 0.405 / 0.434 / 0.463 | 0.522 / 0.542 / 0.561 / 0.582 | 0.127 / 0.126 / 0.134 / 0.135 | +| barrier | 1,847 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 4,531 / 4,531 / 4,531 / 4,531 | 0.139 / 0.152 / 0.160 / 0.167 | 0.035 / 0.042 / 0.045 / 0.047 | 0.136 / 0.139 / 0.140 / 0.140 | 0.298 / 0.322 / 0.334 / 0.339 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 2,523 / 2,523 / 2,523 / 2,523 | 0.185 / 0.224 / 0.277 / 0.302 | 0.023 / 0.026 / 0.028 / 0.031 | 0.150 / 0.157 / 0.165 / 0.169 | 0.434 / 0.474 / 0.511 / 0.529 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 333 / 333 / 333 / 333 | 0.216 / 0.346 / 0.420 / 0.450 | 0.026 / 0.031 / 0.034 / 0.035 | 0.121 / 0.137 / 0.146 / 0.147 | 0.269 / 0.274 / 0.279 / 0.281 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 254 / 254 / 254 / 254 | 0.169 / 0.184 / 0.185 / 0.222 | 0.084 / 0.084 / 0.084 / 0.085 | 0.237 / 0.237 / 0.237 / 0.236 | 0.830 / 0.849 / 0.850 / 0.846 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 4,056 / 4,056 / 4,056 / 4,056 | 0.116 / 0.121 / 0.131 / 0.159 | 0.301 / 0.304 / 0.308 / 0.314 | 0.259 / 0.260 / 0.260 / 0.261 | 0.456 / 0.456 / 0.457 / 0.460 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 790 / 790 / 790 / 790 | 0.169 / 0.185 / 0.298 / 0.468 | 0.433 / 0.436 / 0.436 / 0.431 | 0.233 / 0.233 / 0.234 / 0.234 | 0.113 / 0.112 / 0.112 / 0.112 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 16,890 / 16,890 / 16,890 / 16,890 | 0.150 / 0.168 / 0.180 / 0.190 | 0.043 / 0.053 / 0.057 / 0.059 | 0.141 / 0.144 / 0.145 / 0.145 | 0.353 / 0.381 / 0.398 / 0.403 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 9,406 / 9,406 / 9,406 / 9,406 | 0.191 / 0.244 / 0.319 / 0.355 | 0.027 / 0.030 / 0.033 / 0.037 | 0.156 / 0.164 / 0.175 / 0.180 | 0.529 / 0.572 / 0.610 / 0.632 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,243 / 1,243 / 1,243 / 1,243 | 0.237 / 0.382 / 0.487 / 0.535 | 0.055 / 0.045 / 0.046 / 0.048 | 0.129 / 0.144 / 0.156 / 0.158 | 0.356 / 0.325 / 0.323 / 0.324 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 947 / 947 / 947 / 947 | 0.177 / 0.195 / 0.197 / 0.243 | 0.102 / 0.100 / 0.100 / 0.101 | 0.246 / 0.247 / 0.247 / 0.246 | 0.881 / 0.896 / 0.897 / 0.894 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 15,121 / 15,121 / 15,121 / 15,121 | 0.120 / 0.126 / 0.139 / 0.176 | 0.325 / 0.328 / 0.332 / 0.339 | 0.261 / 0.261 / 0.262 / 0.262 | 0.478 / 0.478 / 0.479 / 0.483 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 2,945 / 2,945 / 2,945 / 2,945 | 0.172 / 0.193 / 0.318 / 0.527 | 0.404 / 0.409 / 0.407 / 0.403 | 0.224 / 0.225 / 0.225 / 0.226 | 0.104 / 0.104 / 0.103 / 0.103 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 31,946 / 34,752 / 35,481 / 35,761 | 0.165 / 0.200 / 0.223 / 0.241 | 0.067 / 0.087 / 0.094 / 0.096 | 0.149 / 0.154 / 0.155 / 0.155 | 0.433 / 0.483 / 0.513 / 0.522 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 13,234 / 15,760 / 17,748 / 18,656 | 0.197 / 0.269 / 0.377 / 0.455 | 0.028 / 0.036 / 0.045 / 0.052 | 0.160 / 0.174 / 0.190 / 0.201 | 0.576 / 0.664 / 0.728 / 0.774 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,143 / 1,712 / 1,912 / 1,948 | 0.237 / 0.393 / 0.503 / 0.559 | 0.042 / 0.055 / 0.055 / 0.057 | 0.127 / 0.147 / 0.159 / 0.161 | 0.311 / 0.346 / 0.328 / 0.326 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 1,403 / 1,491 / 1,492 / 1,475 | 0.176 / 0.196 / 0.197 / 0.242 | 0.101 / 0.106 / 0.106 / 0.105 | 0.247 / 0.247 / 0.247 / 0.246 | 0.895 / 0.915 / 0.915 / 0.902 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 28,761 / 29,331 / 29,372 / 29,481 | 0.124 / 0.134 / 0.150 / 0.193 | 0.345 / 0.350 / 0.355 / 0.362 | 0.262 / 0.263 / 0.263 / 0.263 | 0.495 / 0.496 / 0.497 / 0.501 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 3,696 / 3,847 / 3,872 / 4,000 | 0.169 / 0.190 / 0.317 / 0.517 | 0.405 / 0.408 / 0.405 / 0.399 | 0.224 / 0.224 / 0.223 / 0.223 | 0.105 / 0.104 / 0.103 / 0.102 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +**Total BEV Center Distance mAP (eval range = 90.0 - 121.0m): 0.4071** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 17,510 | 13,505 / 15,174 / 15,882 / 16,111 | 0.639 / 0.753 / 0.798 / 0.812 | 0.718 / 0.784 / 0.807 / 0.812 | 0.219 / 0.182 / 0.156 / 0.156 | +| truck | 14,707 | 6,697 / 9,720 / 11,675 / 13,042 | 0.250 / 0.516 / 0.671 / 0.789 | 0.473 / 0.653 / 0.747 / 0.812 | 0.262 / 0.239 / 0.163 / 0.150 | +| bus | 2,997 | 1,441 / 1,779 / 2,237 / 2,331 | 0.261 / 0.392 / 0.591 / 0.634 | 0.415 / 0.515 / 0.655 / 0.680 | 0.031 / 0.040 / 0.040 / 0.040 | +| bicycle | 566 | 373 / 415 / 430 / 431 | 0.245 / 0.307 / 0.356 / 0.377 | 0.451 / 0.482 / 0.507 / 0.518 | 0.144 / 0.114 / 0.161 / 0.161 | +| pedestrian | 16,580 | 14,776 / 14,991 / 15,087 / 15,224 | 0.704 / 0.718 / 0.725 / 0.741 | 0.731 / 0.738 / 0.744 / 0.752 | 0.146 / 0.138 / 0.137 / 0.137 | +| traffic_cone | 769 | 310 / 315 / 325 / 336 | 0.024 / 0.027 / 0.030 / 0.039 | 0.205 / 0.208 / 0.215 / 0.231 | 0.212 / 0.212 / 0.212 / 0.212 | +| barrier | 566 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 1,926 / 1,926 / 1,926 / 1,926 | 0.188 / 0.209 / 0.225 / 0.236 | 0.051 / 0.054 / 0.058 / 0.060 | 0.178 / 0.181 / 0.184 / 0.184 | 0.513 / 0.543 / 0.555 / 0.561 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 1,617 / 1,617 / 1,617 / 1,617 | 0.232 / 0.330 / 0.406 / 0.540 | 0.025 / 0.028 / 0.031 / 0.036 | 0.171 / 0.188 / 0.197 / 0.215 | 0.564 / 0.574 / 0.600 / 0.604 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 329 / 329 / 329 / 329 | 0.165 / 0.241 / 0.405 / 0.451 | 0.034 / 0.038 / 0.107 / 0.114 | 0.130 / 0.135 / 0.161 / 0.168 | 0.147 / 0.176 / 0.208 / 0.220 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 62 / 62 / 62 / 62 | 0.177 / 0.223 / 0.295 / 0.412 | 0.139 / 0.134 / 0.127 / 0.136 | 0.233 / 0.244 / 0.266 / 0.263 | 0.917 / 1.004 / 1.090 / 1.068 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 1,823 / 1,823 / 1,823 / 1,823 | 0.132 / 0.138 / 0.149 / 0.202 | 0.255 / 0.256 / 0.261 / 0.268 | 0.280 / 0.280 / 0.280 / 0.280 | 0.561 / 0.561 / 0.562 / 0.565 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 84 / 84 / 84 / 84 | 0.176 / 0.200 / 0.317 / 0.583 | 0.707 / 0.719 / 0.715 / 0.718 | 0.384 / 0.386 / 0.385 / 0.383 | 0.181 / 0.181 / 0.182 / 0.185 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 7,179 / 7,179 / 7,179 / 7,179 | 0.201 / 0.229 / 0.251 / 0.267 | 0.066 / 0.069 / 0.074 / 0.077 | 0.183 / 0.187 / 0.189 / 0.189 | 0.566 / 0.601 / 0.616 / 0.623 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,029 / 6,029 / 6,029 / 6,029 | 0.247 / 0.361 / 0.470 / 0.627 | 0.041 / 0.036 / 0.039 / 0.046 | 0.181 / 0.196 / 0.207 / 0.228 | 0.699 / 0.685 / 0.699 / 0.696 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,228 / 1,228 / 1,228 / 1,228 | 0.200 / 0.302 / 0.536 / 0.596 | 0.036 / 0.048 / 0.168 / 0.178 | 0.143 / 0.151 / 0.191 / 0.198 | 0.209 / 0.265 / 0.296 / 0.309 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 232 / 232 / 232 / 232 | 0.189 / 0.238 / 0.279 / 0.376 | 0.211 / 0.191 / 0.181 / 0.181 | 0.223 / 0.234 / 0.248 / 0.248 | 0.956 / 1.039 / 1.085 / 1.071 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 6,797 / 6,797 / 6,797 / 6,797 | 0.137 / 0.145 / 0.161 / 0.226 | 0.289 / 0.290 / 0.295 / 0.306 | 0.276 / 0.276 / 0.276 / 0.277 | 0.570 / 0.570 / 0.571 / 0.575 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 0 / 0 / 315 / 315 | 1.000 / 1.000 / 0.359 / 0.628 | 1.000 / 1.000 / 0.690 / 0.692 | 1.000 / 1.000 / 0.370 / 0.367 | 1.000 / 1.000 / 0.188 / 0.190 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 11,780 / 13,293 / 14,056 / 14,133 | 0.206 / 0.247 / 0.284 / 0.304 | 0.069 / 0.076 / 0.085 / 0.088 | 0.185 / 0.190 / 0.193 / 0.193 | 0.592 / 0.650 / 0.688 / 0.695 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 5,970 / 8,371 / 10,098 / 11,087 | 0.244 / 0.366 / 0.506 / 0.702 | 0.033 / 0.035 / 0.044 / 0.053 | 0.178 / 0.198 / 0.215 / 0.242 | 0.662 / 0.702 / 0.764 / 0.766 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,279 / 1,474 / 1,872 / 1,945 | 0.199 / 0.304 / 0.558 / 0.628 | 0.036 / 0.049 / 0.187 / 0.200 | 0.142 / 0.151 / 0.198 / 0.207 | 0.213 / 0.270 / 0.309 / 0.322 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 222 / 261 / 241 / 246 | 0.178 / 0.228 / 0.268 / 0.379 | 0.122 / 0.135 / 0.114 / 0.124 | 0.230 / 0.236 / 0.256 / 0.252 | 0.899 / 0.973 / 1.038 / 1.020 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 11,842 / 12,158 / 12,274 / 12,415 | 0.139 / 0.149 / 0.169 / 0.244 | 0.302 / 0.307 / 0.315 / 0.326 | 0.275 / 0.275 / 0.275 / 0.275 | 0.575 / 0.577 / 0.578 / 0.582 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 148 / 150 / 155 / 167 | 0.176 / 0.192 / 0.302 / 0.587 | 0.724 / 0.734 / 0.733 / 0.725 | 0.393 / 0.396 / 0.397 / 0.394 | 0.180 / 0.181 / 0.184 / 0.188 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +**Total BEV Center Distance mAP (eval range = 0.0 - 121.0m): 0.6019** + +| class_name | GTs | num_match@0.5/1.0/2.0/4.0 | AP@0.5/1.0/2.0/4.0 | max_f1@0.5/1.0/2.0/4.0 | optimal_conf@0.5/1.0/2.0/4.0 | +| :---- | ---: | :---- | :---- | :---- | :---- | +| car | 109,660 | 94,016 / 101,637 / 104,473 / 105,345 | 0.797 / 0.884 / 0.915 / 0.925 | 0.842 / 0.888 / 0.902 / 0.905 | 0.250 / 0.182 / 0.166 / 0.157 | +| truck | 56,273 | 36,118 / 43,936 / 49,381 / 51,968 | 0.513 / 0.701 / 0.822 / 0.879 | 0.664 / 0.779 / 0.850 / 0.880 | 0.305 / 0.215 / 0.184 / 0.150 | +| bus | 9,883 | 6,087 / 7,377 / 8,290 / 8,461 | 0.467 / 0.636 / 0.751 / 0.773 | 0.582 / 0.694 / 0.770 / 0.782 | 0.256 / 0.083 / 0.053 / 0.053 | +| bicycle | 6,644 | 5,499 / 5,696 / 5,721 / 5,725 | 0.680 / 0.715 / 0.721 / 0.725 | 0.736 / 0.753 / 0.756 / 0.759 | 0.199 / 0.170 / 0.170 / 0.170 | +| pedestrian | 124,160 | 115,223 / 116,646 / 117,116 / 117,764 | 0.848 / 0.862 / 0.873 / 0.879 | 0.837 / 0.846 / 0.852 / 0.858 | 0.161 / 0.153 / 0.153 / 0.153 | +| traffic_cone | 20,477 | 13,955 / 14,674 / 15,090 / 15,403 | 0.329 / 0.355 / 0.385 / 0.416 | 0.499 / 0.515 / 0.536 / 0.558 | 0.142 / 0.132 / 0.134 / 0.135 | +| barrier | 4,422 | 0 / 0 / 0 / 0 | 0.000 / 0.000 / 0.000 / 0.000 | nan / nan / nan / nan | nan / nan / nan / nan | + +
+TP error — default (recall @0.10) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 12,062 / 12,062 / 12,062 / 12,062 | 0.122 / 0.134 / 0.140 / 0.147 | 0.029 / 0.032 / 0.035 / 0.036 | 0.119 / 0.122 / 0.123 / 0.123 | 0.334 / 0.347 / 0.356 / 0.359 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 6,190 / 6,190 / 6,190 / 6,190 | 0.166 / 0.214 / 0.259 / 0.299 | 0.019 / 0.021 / 0.023 / 0.025 | 0.134 / 0.145 / 0.152 / 0.157 | 0.424 / 0.446 / 0.471 / 0.484 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 1,087 / 1,087 / 1,087 / 1,087 | 0.165 / 0.215 / 0.267 / 0.283 | 0.021 / 0.023 / 0.036 / 0.038 | 0.114 / 0.120 / 0.128 / 0.130 | 0.264 / 0.273 / 0.281 / 0.284 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 730 / 730 / 730 / 730 | 0.137 / 0.145 / 0.148 / 0.161 | 0.091 / 0.091 / 0.092 / 0.092 | 0.204 / 0.206 / 0.207 / 0.206 | 0.749 / 0.761 / 0.765 / 0.764 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 13,657 / 13,657 / 13,657 / 13,657 | 0.111 / 0.114 / 0.121 / 0.137 | 0.256 / 0.257 / 0.260 / 0.264 | 0.241 / 0.241 / 0.242 / 0.242 | 0.444 / 0.444 / 0.445 / 0.446 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 2,252 / 2,252 / 2,252 / 2,252 | 0.146 / 0.161 / 0.253 / 0.398 | 0.609 / 0.614 / 0.613 / 0.603 | 0.295 / 0.296 / 0.295 / 0.294 | 0.107 / 0.107 / 0.107 / 0.106 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — medium (recall @0.40) + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 44,960 / 44,960 / 44,960 / 44,960 | 0.132 / 0.148 / 0.157 / 0.166 | 0.033 / 0.039 / 0.042 / 0.043 | 0.125 / 0.128 / 0.129 / 0.130 | 0.371 / 0.388 / 0.399 / 0.402 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 23,071 / 23,071 / 23,071 / 23,071 | 0.178 / 0.237 / 0.299 / 0.356 | 0.022 / 0.025 / 0.027 / 0.030 | 0.141 / 0.153 / 0.162 / 0.170 | 0.505 / 0.528 / 0.552 / 0.566 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 4,052 / 4,052 / 4,052 / 4,052 | 0.179 / 0.252 / 0.332 / 0.355 | 0.027 / 0.029 / 0.049 / 0.052 | 0.120 / 0.129 / 0.140 / 0.143 | 0.309 / 0.312 / 0.316 / 0.320 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 2,724 / 2,724 / 2,724 / 2,724 | 0.147 / 0.158 / 0.163 / 0.182 | 0.101 / 0.101 / 0.101 / 0.102 | 0.215 / 0.216 / 0.217 / 0.217 | 0.766 / 0.778 / 0.785 / 0.783 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 50,905 / 50,905 / 50,905 / 50,905 | 0.116 / 0.121 / 0.131 / 0.155 | 0.269 / 0.271 / 0.275 / 0.280 | 0.245 / 0.246 / 0.246 / 0.246 | 0.462 / 0.462 / 0.463 / 0.465 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 8,395 / 8,395 / 8,395 / 8,395 | 0.157 / 0.180 / 0.289 / 0.470 | 0.626 / 0.633 / 0.630 / 0.620 | 0.288 / 0.289 / 0.288 / 0.287 | 0.103 / 0.103 / 0.103 / 0.102 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | 1.000 / 1.000 / 1.000 / 1.000 | + +
+ +
+TP error — optimal + +| class_name | num_match@0.5/1.0/2.0/4.0 | ATE@0.5/1.0/2.0/4.0 | AOE@0.5/1.0/2.0/4.0 | ASE@0.5/1.0/2.0/4.0 | AVE@0.5/1.0/2.0/4.0 | AEE@0.5/1.0/2.0/4.0 | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| car | 87,589 / 94,880 / 97,103 / 97,882 | 0.149 / 0.180 / 0.199 / 0.215 | 0.048 / 0.062 / 0.069 / 0.071 | 0.135 / 0.141 / 0.143 / 0.143 | 0.434 / 0.474 / 0.496 / 0.503 | 1.000 / 1.000 / 1.000 / 1.000 | +| truck | 33,139 / 40,397 / 44,749 / 47,200 | 0.185 / 0.266 / 0.362 / 0.461 | 0.024 / 0.030 / 0.035 / 0.041 | 0.146 / 0.163 / 0.178 / 0.192 | 0.548 / 0.617 / 0.665 / 0.693 | 1.000 / 1.000 / 1.000 / 1.000 | +| bus | 4,572 / 6,029 / 7,207 / 7,319 | 0.174 / 0.272 / 0.389 / 0.422 | 0.021 / 0.035 / 0.075 / 0.080 | 0.116 / 0.133 / 0.154 / 0.157 | 0.290 / 0.334 / 0.357 / 0.362 | 1.000 / 1.000 / 1.000 / 1.000 | +| bicycle | 4,479 / 4,726 / 4,742 / 4,763 | 0.149 / 0.163 / 0.167 / 0.189 | 0.103 / 0.108 / 0.108 / 0.110 | 0.218 / 0.220 / 0.221 / 0.220 | 0.771 / 0.786 / 0.791 / 0.789 | 1.000 / 1.000 / 1.000 / 1.000 | +| pedestrian | 100,710 / 102,804 / 103,519 / 104,226 | 0.122 / 0.131 / 0.147 / 0.186 | 0.290 / 0.295 / 0.299 / 0.307 | 0.249 / 0.249 / 0.250 / 0.250 | 0.478 / 0.478 / 0.479 / 0.483 | 1.000 / 1.000 / 1.000 / 1.000 | +| traffic_cone | 10,025 / 10,672 / 11,043 / 11,437 | 0.152 / 0.173 / 0.284 / 0.461 | 0.614 / 0.619 / 0.614 / 0.604 | 0.288 / 0.288 / 0.286 / 0.285 | 0.104 / 0.104 / 0.103 / 0.102 | 1.000 / 1.000 / 1.000 / 1.000 | +| barrier | 0 / 0 / 0 / 0 | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | nan / nan / nan / nan | + +
+ +
+ +--- + ### BEVFusion-LiDAR JPNTaxi_base/2.7.1