ResNeXt & ResNet Pytorch Implementation

ResNet (Deep Residual Learning for Image Recognition)
Pre-act ResNet (Identity mappings in deep residual networks)
ResNeXt (Aggregated Residual Transformations for Deep Neural Networks)
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

torch (@facebookresearch). (Original) Cifar and Imagenet
MXNet (@dmlc). Imagenet
pytorch (@prlz77). Cifar

Cite

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={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={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={Computer Vision and Pattern Recognition},
  year={2017}
}