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jiwoon-ahn / psa

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018
MIT License
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the loss of classification network doesn't decrease #1

Closed kevinlee9 closed 4 years ago

kevinlee9 commented 6 years ago

When I trained the classification network(using both pretrained vgg and resnet weights), the loss didn't decrease succesfully using given hyperparamters. For example, the loss of vgg network vibrated around 0.24 after 1k iters, I also tried the learning rate of 0.01, it also failed. Could you give me some suggestions? Thanks~

jiwoon-ahn commented 6 years ago

Hi kevenlee, I'm not sure what causes the problem, but I recommend you to double-check you have loaded the pretrained weights properly. Instead using vgg16_20M.caffemodel, you may try this file to initialize VGG network.

yangyu12 commented 5 years ago

Hi @jiwoon-ahn I just wonder what's the difference between vgg16_20M.caffemodel and the model you provided in this issue. And where's this model originally from?

jiwoon-ahn commented 5 years ago

I just converted vgg16_20M.caffemodel to PyTorch format. The weights are exactly the same.

yangyu12 commented 5 years ago

Thanks for reply. I've tried both pretrained models. The loss decreases normally in both case. This code really works well.

LeiyuanMa commented 5 years ago

I tried with two gpus, learing rate 0.01 and batch size 6,the code works fine,so May I ask how many gpus are you using?and could you provide the parameter for resnet?

Yinyf0804 commented 4 years ago

Hi @jiwoon-ahn I have met the same problem that the loss of vgg network vibrated around 0.24 after 1k iters, then I try to download the model you provided in this issue, but I don't have the right to download it. Could you give me some suggestions? Thanks!

Yinyf0804 commented 4 years ago

Hi @kevinlee9 How did you finally solve the problem?

kevinlee9 commented 4 years ago

Hi @kevinlee9 How did you finally solve the problem?

I tried the weights file provided by ahn, then loss decreased.

Kobusvdwalt commented 2 years ago

To save the next guy some time:

If you don't want to set up caffe you can do this : 1) Get the caffe weights from https://www.cs.jhu.edu/~alanlab/ccvl/init_models/ 2) Convert the weights to pytorch format with https://github.com/vadimkantorov/caffemodel2pytorch 3) Change the loading script to load the pytorch weights

weights = torch.load('vgg16_20M.caffemodel.pt') weights_dict = {} for key in weights.keys(): if '.bias' in key: weights_dict[key] = torch.squeeze(weights[key]) else: weights_dict[key] = weights[key]