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log20epoch .txt
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log20epoch .txt
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>>>
= RESTART: C:\Car_Object_Detection_and_Classification\TrainCarsBrand_Pytorch_Resnet.py
Warning (from warnings module):
File "C:\Users\Alfonso Blanco\.conda\envs\alfonso1\lib\site-packages\numpy\_distributor_init.py", line 30
warnings.warn("loaded more than 1 DLL from .libs:"
UserWarning: loaded more than 1 DLL from .libs:
C:\Users\Alfonso Blanco\.conda\envs\alfonso1\lib\site-packages\numpy\.libs\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll
C:\Users\Alfonso Blanco\.conda\envs\alfonso1\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll
Warning (from warnings module):
File "C:\Users\Alfonso Blanco\AppData\Roaming\Python\Python39\site-packages\torchvision\models\_utils.py", line 208
warnings.warn(
UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
Warning (from warnings module):
File "C:\Users\Alfonso Blanco\AppData\Roaming\Python\Python39\site-packages\torchvision\models\_utils.py", line 223
warnings.warn(msg)
UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to C:\Users\Alfonso Blanco/.cache\torch\hub\checkpoints\resnet50-0676ba61.pth
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No. epochs: 2, Training Loss: 0.063 Valid Loss: 2.896 Valid Accuracy: 0.238
No. epochs: 3, Training Loss: 0.078 Valid Loss: 1.894 Valid Accuracy: 0.488
No. epochs: 4, Training Loss: 0.077 Valid Loss: 1.634 Valid Accuracy: 0.558
No. epochs: 5, Training Loss: 0.065 Valid Loss: 1.317 Valid Accuracy: 0.625
No. epochs: 6, Training Loss: 0.053 Valid Loss: 1.237 Valid Accuracy: 0.662
No. epochs: 7, Training Loss: 0.046 Valid Loss: 1.337 Valid Accuracy: 0.64
No. epochs: 8, Training Loss: 0.035 Valid Loss: 0.718 Valid Accuracy: 0.806
No. epochs: 9, Training Loss: 0.032 Valid Loss: 0.678 Valid Accuracy: 0.816
No. epochs: 10, Training Loss: 0.03 Valid Loss: 0.654 Valid Accuracy: 0.825
No. epochs: 11, Training Loss: 0.031 Valid Loss: 0.641 Valid Accuracy: 0.823
No. epochs: 12, Training Loss: 0.033 Valid Loss: 0.628 Valid Accuracy: 0.828
No. epochs: 13, Training Loss: 0.037 Valid Loss: 0.638 Valid Accuracy: 0.83
No. epochs: 14, Training Loss: 0.038 Valid Loss: 0.625 Valid Accuracy: 0.833
No. epochs: 15, Training Loss: 0.039 Valid Loss: 0.637 Valid Accuracy: 0.827
No. epochs: 16, Training Loss: 0.043 Valid Loss: 0.635 Valid Accuracy: 0.827
No. epochs: 17, Training Loss: 0.047 Valid Loss: 0.645 Valid Accuracy: 0.826
No. epochs: 18, Training Loss: 0.047 Valid Loss: 0.644 Valid Accuracy: 0.82
No. epochs: 19, Training Loss: 0.053 Valid Loss: 0.63 Valid Accuracy: 0.826
No. epochs: 20, Training Loss: 0.056 Valid Loss: 0.64 Valid Accuracy: 0.824
Test accuracy of model: 80.4%
>>>