Thank you for your excellent work, I found a problem in reading the paper and the code:
I saw your paper said :" Backbone. Many common CNNs can be taken as the backbone. For better comparisons, we choose two typical CNN architectures: ResNet [25] and HRNet [51]. We only retain the initial several parts of the original ImageNet pretrained CNNs to extract feature from images. We name them ResNet-S and HRNet-S, the parameters numbers of which are only about 5.5% and 25% of the original CNNs."
But, after reading the code, I still couldn't know What parts of the CNN model you have retained. Can you tell me in detail?
Thank you for your excellent work, I found a problem in reading the paper and the code: I saw your paper said :" Backbone. Many common CNNs can be taken as the backbone. For better comparisons, we choose two typical CNN architectures: ResNet [25] and HRNet [51]. We only retain the initial several parts of the original ImageNet pretrained CNNs to extract feature from images. We name them ResNet-S and HRNet-S, the parameters numbers of which are only about 5.5% and 25% of the original CNNs." But, after reading the code, I still couldn't know What parts of the CNN model you have retained. Can you tell me in detail?