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ResNeXt (Aggregated Residual Transformations for Deep Neural Networks)
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ResNet (Deep Residual Learning for Image Recognition)
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DenseNet (Densely Connected Convolutional Networks)
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Train on Cifar10 and Cifar100 with ResNeXt29-8-64d and ResNeXt29-16-64d
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Train on Cifar10 and Cifar100 with ResNet20,32,44,56,110
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Train on Cifar10 and Cifar100 with Pre-Activation ResNet20,32,44,56,110
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Train on Cifar10 and Cifar100 with DenseNet
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Train Imagenet
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
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 |
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 |
- torch (@facebookresearch). (Original) Cifar and Imagenet
- MXNet (@dmlc). Imagenet
- pytorch (@prlz77). Cifar
@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}
}