README.md

CNNs_PyTorch

cnn training with bag of tricks

Dependencies

• pytorch >= 1.0
• torchvision to load the datasets, perform image transforms
• tensorboardX
• numpy
• CUDA >= 9.0

Experiment Results

ImageNet

AlexNet: acc1 = 56.37

CUDA_VISIBLE_DEVICES=1 python imagenet.py \
	--data /imagenet-dir \
	--arch alexnet \
	--lr 0.01 --lr-mode step --lr-decay-period 40 \
	--epoch 160 --batch-size 256  -j 8 \
	--weight-decay 0.00005 

ResNet-18: acc1 = 71.02

CUDA_VISIBLE_DEVICES=0,1,2,3 python imagenet.py \
	--data /imagenet-dir \
	--arch resnet18 \
	--lr 0.2 --lr-mode cosine --epoch 120 --batch-size 512  -j 32 \
	--warmup-epochs 5  --weight-decay 0.0001 

ResNet-50: acc1 = 77.75

CUDA_VISIBLE_DEVICES=0,1,2,3 python imagenet.py \
	--data /imagenet-dir \
	--arch resnet50 \
	--lr 0.2 --lr-mode cosine --epoch 120 --batch-size 512  -j 60 \
	--warmup-epochs 5  --weight-decay 0.0001 \
	--no-wd --label-smoothing --last-gamma

MobileNet-V2-1.0: acc1 = 71.93

CUDA_VISIBLE_DEVICES=0,1,2,3 python imagenet.py \
	--data /imagenet-dir \
	--arch mobilenet_v2 \
	--lr 0.05 --lr-mode cosine --epoch 150 --batch-size 256  -j 32 \
	--warmup-epochs 5  --weight-decay 0.00004 \
	--no-wd --label-smoothing 

Cifar10

cifar_resnet20: acc = 92.21

CUDA_VISIBLE_DEVICES=0  python cifar10.py \
	--arch cifar_resnet20 \
	--lr 0.1 --epoch 200 --batch-size 128  -j 2 \
	--lr-decay 0.1 --lr-decay-epoch 100,150 \
	--weight-decay 0.0001 

Model Quantization

• Dynamic Fixed-point;
• Block Floating-point;
• Bi-direction Blocking Floating-point;

Cite

Gluoncv model_zoo

@inproceedings{he2019bag,
  title={Bag of tricks for image classification with convolutional neural networks},
  author={He, Tong and Zhang, Zhi and Zhang, Hang and Zhang, Zhongyue and Xie, Junyuan and Li, Mu},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={558--567},
  year={2019}
}