TBN

This is the implementation of TBN/TBNv2.

Requirements

• Install PyTorch and torchvision
• Download the ImageNet dataset from http://www.image-net.org/
  • Then, and move validation images to labeled subfolders, using the following shell script

Results

| Arch | top-1 accuracy | top-5 accuracy | | ---- | ----- | ------ | | AlexNet (full) | 61.6 | 82.9 | | AlexNet (TBNv2) | 54.9 | 77.8 | | PreActResNet18 (full) | 70.3 | 89.3 | | PreActResNet18 (TBNv2) | 59.7 | 82.1 | | PreActResNet34 (full) | 73.3 | 76.4 | | PreActResNet34 (TBNv2) | 63.4 | 849 | | PreActResNet50 (full) | 76.4 | 93.2 | | PreActResNet50 (TBNv2) | 66.6 | 86.7 |

Evaluate

1. Download pretrained model
   • alexnet_TBNv2: BaiduPan (password: 3rh2)
   • preact_resnet_18_TBNv2: Coming soon
   • preact_resnet_34_TBNv2: Coming soon
   • preact_resnet_50_TBNv2: BaiduPan (password: cmq7)
2. run command (see scripts/*)

python3 ./imagenet.py --load pretrained/alexnet_TBNv2.pth -e -a alexnet --gpu 0 ~/data/ImageNet

Train

Please refer pytorch/examples/imagenet

Use --ternary-delta=0.5, --ternary-order=2, --ternary-momentum=0.1 and --ternary-no-scale to set the hyper-parameter of TBN/TBNv2.

• For TBN, you need --ternary-no-scale option.
• when --ternary-momentum <= 0, the threshold value of ternary is fixed as --ternary-delta rather than calculating based on inputs.

Citation

@InProceedings{Wan_2018_ECCV,
author = {Wan, Diwen and Shen, Fumin and Liu, Li and Zhu, Fan and Qin, Jie and Shao, Ling and Tao Shen, Heng},
title = {TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}