Update
- 2019/12/13: The pytorch implementation of ACNet has been released for the currently popular EfficientNet and the efficient DNA network families. Jump to Pytorch ACNet.
Adaptively Connected Neural Networks
A re-implementation of our CVPR 2019 paper "Adaptively Connected Neural Networks" (https://arxiv.org/abs/1904.03579).
Guangrun Wang , Keze Wang, Liang Lin
Sun Yat-sen University (SYSU)
Table of Contents
Introduction
This repository contains the training & testing code of Adaptively Connected Neural Networks (ACNet) on ImageNet and Cora.
ImageNet
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Training and testing curve on ImageNet:
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ImageNet accuracy and pretrained model (baidu pan code: ow9z):
Model | Top 5 Error | Top 1 Error | Download |
---|---|---|---|
ResNet50 | 6.9% | 23.6% | ⬇️ |
ResNet50-ACNet | 6.4% | 22.5% | google drive: ⬇️ and baidu pan: ⬇️ |
ResNet50-ACNet-pixel-aware | 6.4% | 22.5% | google drive: ⬇️ and baidu pan: ⬇️ |
- Training script:
cd cnn/dataset-aware/ or cd cnn/pixel-aware/
python imagenet-resnet.py --gpu 0,1,2,3,4,5,6,7 --data [ROOT-OF-IMAGENET-DATASET] --log_dir [ROOT-OF-TRAINING-LOG-AND-MODEL]
- Testing script:
cd cnn/dataset-aware/ or cd cnn/pixel-aware/
python imagenet-resnet.py --gpu 0,1,2,3,4,5,6,7 --data [ROOT-OF-IMAGENET-DATASET] --log_dir [ROOT-OF-TEST-LOG] --load [ROOT-TO-LOAD-MODEL] --eval
Cora
Coming soon ...
Citation
If you use these models in your research, please cite:
@inproceedings{wang2019adaptively,
title={Adaptively Connected Neural Networks},
author={Wang, Guangrun and Wang, Keze and Lin, Liang},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}}
Dependencies
- Python 2.7 or 3
- TensorFlow >= 1.3.0
- Tensorpack
The code depends on Yuxin Wu's Tensorpack. For convenience, we provide a stable version 'tensorpack-installed' in this repository.
# install tensorpack locally: cd tensorpack-installed python setup.py install --user