Thank you for uploading your code. It is very helpful to understand PSPNet.
I have two questions about your paper.
- You wrote
we use a pretrained ResNet model with the dilated network strategy to extract the feature map. The final feature map size is 1/8 of the input image.
in the paper. But I think the feature map size is 1/16 when you use ResNet50. Do you use only first 3 blocks of ResNet50?
- You wrote
Then we directly upsample the low-dimension feature maps to get the same size feature as the original feature map via bilinear interpolation. Finally, different levels of features are concatenated as the final pyramid pooling global feature.
in Section 3.2 in the paper. I understand we have to concatenate resized different levels of features and feature map extracted by ResNet 50. But after that, the image size is 1/8 of the input image. How did you resize them to the same image size as input image?
Thank you for uploading your code. It is very helpful to understand PSPNet.
I have two questions about your paper.
in the paper. But I think the feature map size is 1/16 when you use ResNet50. Do you use only first 3 blocks of ResNet50?
in Section 3.2 in the paper. I understand we have to concatenate resized different levels of features and feature map extracted by ResNet 50. But after that, the image size is 1/8 of the input image. How did you resize them to the same image size as input image?