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Pytorch Implementation for ResNet, Pre-Activation ResNet, ResNeXt and DenseNet

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ResNeXt & ResNet Pytorch Implementation

  • ResNeXt (Aggregated Residual Transformations for Deep Neural Networks)

  • ResNet (Deep Residual Learning for Image Recognition)

  • DenseNet (Densely Connected Convolutional Networks)

  • Train on Cifar10 and Cifar100 with ResNeXt29-8-64d and ResNeXt29-16-64d

  • Train on Cifar10 and Cifar100 with ResNet20,32,44,56,110

  • Train on Cifar10 and Cifar100 with Pre-Activation ResNet20,32,44,56,110

  • Train on Cifar10 and Cifar100 with DenseNet

  • Train Imagenet

Usage

To train on Cifar-10 using 4 gpu:

python main.py ./data/cifar.python --dataset cifar10 --arch resnext29_8_64 --save_path ./snapshots/cifar10_resnext29_8_64_300 --epochs 300 --learning_rate 0.05 --schedule 150 225 --gammas 0.1 0.1 --batch_size 128 --workers 4 --ngpu 4

Or there are some off-the-shelf scripts can dirrectly be used for training.

CUDA_VISIBLE_DEVICES=0,1,2,3 sh ./shells/train_model.sh resnet20 cifar10

And a simplified caffenet-like model for cifar10, obtaining 89.5 top1 accuracy.

sh ./shells/cifar10_caffe.sh

Configurations

From the original ResNeXt and ResNet papers:

depth cardinality base width parameters error cifar10 error cifar100 architecture
29 8 64 34.4M 3.65 17.77 ResNeXt
29 16 64 68.1M 3.58 17.31 ResNeXt
20 * * 0.27M 8.75 - ResNet
32 * * 0.46M 7.51 - ResNet
44 * * 0.66M 7.17 - ResNet
56 * * 0.85M 6.97 - ResNet
110 * * 1.7M 6.61 - ResNet
1202 * * 19.4M 7.93 - ResNet

My Results {Last Epoch Error (Best Error)}

depth cardinality base width parameters error cifar10 error cifar100 architecture
29 8 64 34.4M 3.67 17.66(17.47) ResNeXt
29 16 64 68.1M 3.59(3.39) 17.31(17.06) ResNeXt
20 * * 0.27M 8.47 32.99 ResNet
32 * * 0.46M 7.67 30.80 ResNet
44 * * 0.66M 7.23 29.45 ResNet
56 * * 0.85M 6.86 28.89 ResNet
110 * * 1.7M 6.62 27.62 ResNet
20 * * 0.27M 8.35 31.79 Pre-Act
32 * * 0.46M 7.57 30.02 Pre-Act
44 * * 0.66M 29.43 Pre-Act

Other frameworks

Cite

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2016}
}
@inproceedings{he2016identity,
  title={Identity mappings in deep residual networks},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={European Conference on Computer Vision},
  year={2016}
}
@inproceedings{xie2017aggregated,
  title={Aggregated residual transformations for deep neural networks},
  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}
@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Weinberger, Kilian Q and van der Maaten, Laurens},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}

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