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ResNet

ResNet re-implementation using PyTorch

Steps

  • Configure imagenet path by changing data_dir in main.py
  • bash ./main.sh $ --train for training model, $ is number of GPUs
  • python main.py --test for testing

Results

Version Top@1 Top@5 Params FLOPS Pretrained weights
Resnet 18 71.5 89.9 11.7M 1.8G model
Resnet 34 76.3 92.8 21.8M 3.6G model
Resnet 50 80.1 94.5 25.5M 4.1G model
Resnet 101 81.2 95.1 44.5M 7.8G model
Resnet 152 81.7 95.1 60.1M 11.6G model

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ResNet re-implementation using PyTorch

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