new sorting alg

Author: Koldim2001

README.md

@@ -130,9 +130,11 @@ Class implementing combining masks/boxes from multiple crops + NMS (Non-Maximum
 | **Argument**         | **Type**          | **Default** | **Description**                                                                                                         |
 |----------------------|-------------------|-------------|-------------------------------------------------------------------------------------------------------------------------|
 | element_crops        |MakeCropsDetectThem|             | Object containing crop information.                                                                                     |
-| nms_threshold        | float             | 0.3         | IoU/IoS threshold for non-maximum suppression.                                                                          |
+| nms_threshold        | float             | 0.3         | IoU/IoS threshold for non-maximum suppression. The lower the value, the fewer objects remain after suppression.                                                                          |
 | match_metric         | str               | IOS         | Matching metric, either 'IOU' or 'IOS'.                                                                                 |
 | intelligent_sorter   | bool              | True        | Enable sorting by area and rounded confidence parameter. If False, sorting will be done only by confidence (usual nms). |
+| sorter_bins          | int               | 10          | Number of bins to use for intelligent_sorter. A smaller number of bins makes the NMS more reliant on object sizes rather than confidence scores. |
+
 
 
 ---
@@ -198,7 +200,7 @@ visualize_results(
 
 4. **Enhancing Detection Within Patches**: To detect more objects within a single crop, increase the `imgsz` parameter and lower the confidence threshold (`conf`). All parameters available for configuring Ultralytics model inference are also accessible during the initialization of the `MakeCropsDetectThem` element.
 
-5. **Handling Duplicate Suppression Issues**: If you encounter issues with duplicate suppression from overlapping patches, consider adjusting the `nms_threshold` parameter in `CombineDetections` or modifying the overlap and size parameters of the patches themselves.
+5. **Handling Duplicate Suppression Issues**: If you encounter issues with duplicate suppression from overlapping patches, consider adjusting the `nms_threshold` and `sorter_bins` parameters in `CombineDetections` or modifying the overlap and size parameters of the patches themselves. (PS: often lowering `sorter_bins` to 5 or 4 can help).
 
 6. **High-Quality Instance Segmentation**: For tasks requiring high-quality results in instance segmentation, detailed guidance is provided in the next section of the README.
 ---

patched_yolo_infer/README.md

@@ -1,8 +1,10 @@
 # YOLO-Patch-Based-Inference
 
-This library facilitates various visualizations of inference results from ultralytics segmentation/detection models, including cropping with overlays, as well as **a patch-based inference algorithm enabling the detection/instance segmentation of small objects in images**. 
+This Python library simplifies SAHI-like inference for instance segmentation tasks, enabling the detection of small objects in images. It caters to both object detection and instance segmentation tasks, supporting a wide range of Ultralytics models. 
 
-**Model Support**: The library offers support for multiple ultralytics deep learning models, such as YOLOv8, YOLOv9, SAM, and RTDETR. Users can select from pre-trained options or utilize custom-trained models to best meet their task requirements.
+The library also provides a sleek customization of the visualization of the inference results for all models, both in the standard approach (direct network run) and the unique patch-based variant.
+
+**Model Support**: The library offers support for multiple ultralytics deep learning [models](https://docs.ultralytics.com/models/), such as YOLOv8, YOLOv8-seg, YOLOv9, YOLOv9-seg, YOLOv10, FastSAM, and RTDETR. Users can select from pre-trained options or utilize custom-trained models to best meet their task requirements.
 
 
 ## Installation
@@ -25,7 +27,7 @@ __Check this Colab examples:__
 
 Patch-Based-Inference Example - [Open in Colab](https://colab.research.google.com/drive/1XCpIYLMFEmGSO0XCOkSD7CcD9SFHSJPA?usp=sharing)
 
-Example of using various functions for visualizing basic YOLOv8/v9 inference results and handling overlapping crops - [Open in Colab](https://colab.research.google.com/drive/1eM4o1e0AUQrS1mLDpcgK9HKInWEvnaMn?usp=sharing)
+Example of using various functions for visualizing basic YOLOv8/v9 inference results - [Open in Colab](https://colab.research.google.com/drive/1eM4o1e0AUQrS1mLDpcgK9HKInWEvnaMn?usp=sharing)
 
 
 ## Usage
@@ -67,7 +69,7 @@ element_crops = MakeCropsDetectThem(
     iou=0.7,
     resize_initial_size=True,
 )
-result = CombineDetections(element_crops, nms_threshold=0.25, match_metric='IOS')  
+result = CombineDetections(element_crops, nms_threshold=0.25)  
 
 # Final Results:
 img=result.image
@@ -108,7 +110,7 @@ Class implementing combining masks/boxes from multiple crops + NMS (Non-Maximum
 - **nms_threshold** (*float*): IoU/IoS threshold for non-maximum suppression.
 - **match_metric** (*str*): Matching metric, either 'IOU' or 'IOS'.
 - **intelligent_sorter** (*bool*): Enable sorting by area and rounded confidence parameter. If False, sorting will be done only by confidence (usual nms). (Dafault is True)
-
+- **sorter_bins** (*int*): Number of bins to use for intelligent_sorter. A smaller number of bins makes the NMS more reliant on object sizes rather than confidence scores. (Defaults to 10)
 
 
 ---

setup.py

@@ -8,7 +8,7 @@
     long_description = "\n" + fh.read()
 
 
-VERSION = '1.2.7'
+VERSION = '1.2.8'
 DESCRIPTION = '''Patch-Based-Inference for detection/segmentation of small objects in images.'''
 
 setup(