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i really appreciate your github. Thanks a lot for your works on fine-tuning models.
When i tried to run your code locally, i faced a issue and it looks like a multi-threading issue. This is the log:
C:\Users\dk12a7\Desktop\New folder\fine-tuning.pytorch>python main.py --finetune
[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...
[Phase 2] : Model setup
| Downloading ImageNet fine-tuned ResNet-50...
[Phase 3] : Training Model
| Training Epochs = 50
| Initial Learning Rate = 0.001000
| Optimizer = SGD
=> Training Epoch #1, LR=0.001000
[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...
[Phase 2] : Model setup
| Downloading ImageNet fine-tuned ResNet-50...
[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...
[Phase 2] : Model setup
| Downloading ImageNet fine-tuned ResNet-50...
[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...
[Phase 2] : Model setup
| Downloading ImageNet fine-tuned ResNet-50...
[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...
After get into the training phase for the first time, the code keep repeating the phase 1 and phase 2. Do you have any idea about this issue. Thank you
The text was updated successfully, but these errors were encountered:
i really appreciate your github. Thanks a lot for your works on fine-tuning models.
When i tried to run your code locally, i faced a issue and it looks like a multi-threading issue. This is the log:
After get into the training phase for the first time, the code keep repeating the phase 1 and phase 2. Do you have any idea about this issue. Thank you
The text was updated successfully, but these errors were encountered: