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Train several classical classification networks in cifar10 dataset by PyTorch

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Classification-cifar10-pytorch

I am testing several classical classification networks performance on cifar10 dataset by PyTorch! Chinese blog

Requirements

  • pytorch
  • torchsummary
  • python3.x

Training

  • Single-GPU
    # Start training with: 
    python main.py
    
    # You can manually resume the training with: 
    python main.py --resume --lr=0.01
  • Muiti-GPU
    # Start training with(assume you have 2 GPUs): 
    python -m torch.distributed.launch --nproc_per_node=2 main.py
    # You can manually resume the training with:
    python -m torch.distributed.launch --nproc_per_node=2 main.py --resume

Results

Model My Acc. Total params Estimated Total Size (MB) Trainable params Params size (MB) Saved model size (MB) GPU memory usage(MB)
MobileNetV2 92.64% 2,296,922 36.14 2,296,922 8.76 8.96 3107
VGG16 94.27% 14,728,266 62.77 14,728,266 56.18 59.0 1229
PreActResNet18 94.70% 11,171,146 53.38 11,171,146 42.61 44.7 1665
ResNeXt29(2x64d) 95.09% 9,128,778 99.84 9,128,778 34.82 36.7 5779
ResNet50 95.22% 23,520,842 155.86 23,520,842 89.72 94.4 5723
DPN92 95.42% 34,236,634 243.50 34,236,634 130.60 137.5 10535
ResNeXt29(32x4d) 95.49% 4,774,218 83.22 4,774,218 18.21 19.2 5817
DenseNet121 95.55% 6,956,298 105.05 6,956,298 26.54 28.3 8203
ResNet18 95.59% 11,173,962 53.89 11,173,962 42.63 44.8 1615
ResNet101 95.62% 42,512,970 262.31 42,512,970 162.17 170.6 8857

Note:

  1. Above GPU memory usage(MB) was observed with batch size=128.
  2. For PreActResNet18, I set initial learning rate=0.1, but it can't converge, so I set it's initial lr=0.01.
  3. I firstly train VGG16, ResNet18 and ResNet50 with total epochs=400. But I want to get results earlier, so for remaining networks, I set total epochs=300 (besides, afterwards it just improve a little).
  4. Run the scripts: python main.py --resume --lr 0.1 --trainbs 128 --testbs 100

Pre-trained models

You can obtain pre-traind models(as above list) from here: [Baidu Drive] [Google Drive]

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Train several classical classification networks in cifar10 dataset by PyTorch

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