Comments (2)
Hello,
I am facing the same problem. I am trying to finetune my model on Pascal VOC 2007 dataset and the loss is not going down beyond random. It's not clear what is going wrong.
I have tried with default hyperparameter settings as well as my own.
Please advise.
Thanks
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I encountered the same problem as @Maryames . And I solved it with the help of @uahsan3 .
If anyone else encounter this problem, please look at the trainval.prototxt generate by the code (in train_cls.py)and check the data layer. The code will create a right data layer and if your alexnet.prototxt also include a data layer, then the network will have two data layer and the image data will not go through the network. Besides, I attach two screenshots of trainval.prototxt below, the first is wrong and the second is right.
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Related Issues (7)
- Is there a python2.x version of this code? HOT 2
- The code does not work for models/bvlc_alexnet/deploy.prototxt
- Size mismatch in SigmoidCrossEntropyLoss HOT 6
- dimension mismatch in test evaluation HOT 3
- How can I get the state-of-art performance of VOC in the classification task?
- Do you have benchmark results for VOC for image classification?
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