https://github.com/zhiqiangdon/cu-net
Code for "Quantized Densely Connected U-Nets for Efficient Landmark Localization" (ECCV 2018) and "CU-Net: Coupled U-Nets" (BMVC 2018 oral)
https://github.com/zhiqiangdon/cu-net
face-align human-pose-estimation quantized-neural-network quantized-training u-net
Last synced: about 1 month ago
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Code for "Quantized Densely Connected U-Nets for Efficient Landmark Localization" (ECCV 2018) and "CU-Net: Coupled U-Nets" (BMVC 2018 oral)
- Host: GitHub
- URL: https://github.com/zhiqiangdon/cu-net
- Owner: zhiqiangdon
- License: apache-2.0
- Created: 2018-07-25T21:07:30.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2021-08-16T03:47:07.000Z (almost 4 years ago)
- Last Synced: 2025-03-27T01:12:56.537Z (about 2 months ago)
- Topics: face-align, human-pose-estimation, quantized-neural-network, quantized-training, u-net
- Language: Python
- Homepage:
- Size: 8.11 MB
- Stars: 228
- Watchers: 14
- Forks: 40
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
#
## **[Quantized Densely Connected U-Nets for Efficient Landmark Localization](https://arxiv.org/abs/1808.02194)**
## **[CU-Net: Coupled U-Nets](https://arxiv.org/abs/1808.06521)**
## Overview
The follwoing figure gives an illustration of naive dense U-Net, stacked U-Nets and coupled U-Nets (CU-Net). The naive dense U-Net and stacked U-Nets have shortcut connections only inside each U-Net. In contrast, the coupled U-Nets also have connections for semantic blocks across U-Nets. The CU-Net is a hybrid of naive dense U-Net and stacked U-Net, integrating the merits of both dense connectivity, intermediate supervisions and multi-stage top-down and bottom-up refinement. The resulted CU-Net could save ~70% parameters of the previous stacked U-Nets but with comparable accuracy.
If we couple each U-Net pair in multiple U-Nets, the coupling connections would have quadratic growth with respect to the U-Net number. To make the model more parameter efficient, we propose the order-K coupling to trim off the long-distance coupling connections.
For simplicity, each dot represents one U-Net. The red and blue lines are the shortcut connections of inside semantic blocks and outside inputs. Order-0 connectivity (Top) strings U-Nets together only by their inputs and outputs, i.e. stacked U-Nets. Order-1 connectivity (Middle) has shortcut connections for adjacent U-Nets. Similarly, order-2 connectivity (Bottom) has shortcut connections for 3 nearby U-Nets.
### Prerequisites
This package has the following requirements:
* `Python 2.7`
* `Pytorch v0.4.0` or `Pytorch v0.1.12`
Note that the script name with string `prev-version` requires `Pytorch v0.1.12`.
### Training
```
python cu-net.py --gpu_id 0 --exp_id cu-net-2 --layer_num 2 --order 1 --loss_num 2 --is_train true --bs 24
```
### Validation
```
python cu-net.py --gpu_id 0 --exp_id cu-net-2 --layer_num 2 --order 1 --loss_num 2 --resume_prefix your_pretrained_model.pth.tar --is_train false --bs 24
```
### Model Options
```
layer_num # number of coupled U-Nets
order # the order of coupling
loss_num # number of losses. Losses are uniformly distributed along the CU-Net. Each U-Net at most has one loss. (loss_num <= layer_num)
```
### Pretrained Models
1. [face-layer-num-8-order-1](https://drive.google.com/file/d/1_znMNl2PDBW5E-bhJeBuDGB7-ExzqAoq/view?usp=sharing)
2. [human-layer-num-16-order-1](https://drive.google.com/file/d/1Y7b6vdBXVfecemykOwvI1U2PrrjnX1TZ/view?usp=sharing)
## Citation
If you find this code useful in your research, please consider citing:
```
@inproceedings{tang2018quantized,
title={Quantized densely connected U-Nets for efficient landmark localization},
author={Tang, Zhiqiang and Peng, Xi and Geng, Shijie and Wu, Lingfei and Zhang, Shaoting and Metaxas, Dimitris},
booktitle={ECCV},
year={2018}
}
@inproceedings{tang2018cu,
title={CU-Net: Coupled U-Nets},
author={Tang, Zhiqiang and Peng, Xi and Geng, Shijie and Zhu, Yizhe and Metaxas, Dimitris},
booktitle={BMVC},
year={2018}
}
```