Awesome
ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
Paper link: ICCV 2021
Updated: 04/29/2022
Structure
| Folder | Description |
|---|---|
| conf | Configure files of different training tasks. |
| lib | Core implementation of all kinds of backbones, data loaders, losses and optimizers. |
| main.py | Entry script for training, testing or conversion. |
| trainer.py | Entry script for model training. |
| tester.py | Entry script for model testing. |
| evaluate.py | Evaluate script for model NME metrics calculation and visualization. |
| requirements.txt | The dependency list in python. |
Dependencies
Local machine:
- python==3.7.3
- torch==1.6.0
- requirements.txt
- GPU running option requires:
- Installing CUDA 10.1 (Runtime + SDK) and configuring NVCC root directory into ENVIRONMENT PATH
Resources
| Dataset | Dataset | Metadata (train, test) | Model (pytorch, onnx) |
|---|---|---|---|
| COFW | official | google / baidu | google / baidu |
| 300W | official | google / baidu | google / baidu |
| WFLW | official | google / baidu | google / baidu |
Preparation
- Step1: Install dependencies.
- Step2: Clone repo.
- Step3: Download dataset and metadata of each dataset to data/alignment/${dataset} folder.
- Step4: Set the target dataset through configuring the ${data_definition} variable in conf/alignment.py script.
- Step5: Run the scripts below.
Training in local machine
python main.py --mode=train --config_name=alignment --device_ids=0,1,2,3
Testing in local machine
python main.py --mode=test --config_name=alignment --pretrained_weight=${model_path} --device_ids=0
Evaluation in local machine
python evaluate.py --mode=nme --config_name=alignment --model_path=${model_path} --metadata_path==${metadata_path} --image_dir=${image_dir} --device_ids=0
Framework
The framework of ADNet.
Performance
Table 1. Comparing with state-of-the-art methods on COFW by NME<sub>inter-pupils</sub>.
| Method | NME | FR<sub>10%</sub> | AUC<sub>10%</sub> |
|---|---|---|---|
| Human | 5.60 | - | - |
| RCPR | 8.50 | 20.00 | - |
| TCDCN | 8.05 | - | - |
| DAC-CSR | 6.03 | 4.73 | - |
| Wu et al | 5.93 | - | - |
| Wing | 5.44 | 3.75 | - |
| DCFE | 5.27 | 7.29 | 0.3586 |
| Awing | 4.94 | 0.99 | 0.6440 |
| ADNet | 4.68 | 0.59 | 0.5317 |
Table 2. Comparing with state-of-the-art methods on 300W by NME<sub>inter-pupils</sub>.
| Method | Common Subset | Challenging Subset | Fullset |
|---|---|---|---|
| PCD-CNN | 3.67 | 7.62 | 4.44 |
| CPM+SBR | 3.28 | 7.58 | 4.10 |
| SAN | 3.34 | 6.60 | 3.98 |
| LAB | 2.98 | 5.19 | 3.49 |
| DeCaFA | 2.93 | 5.26 | 3.39 |
| DU-Net | 2.90 | 5.15 | 3.35 |
| LUVLi | 2.76 | 5.16 | 3.23 |
| AWing | 2.72 | 4.52 | 3.07 |
| --------- | -------------- | ------------------ | ----------- |
| ADNet | 2.53 | 4.58 | 2.93 |
Table 3. Comparing with state-of-the-art methods on WFLW by NME<sub>inter-ocular</sub>.
| Method | Testset | Pose Subset | Expression Subset | Illumination Subset | Make-up Subeset | Occlusion Subset | Blur Subset |
|---|---|---|---|---|---|---|---|
| ESR | 11.13 | 25.88 | 11.47 | 10.49 | 11.05 | 13.75 | 12.20 |
| SDM | 10.29 | 24.10 | 11.45 | 9.32 | 9.38 | 13.03 | 11.28 |
| CFSS | 9.07 | 21.36 | 10.09 | 8.30 | 8.74 | 11.76 | 9.96 |
| DVLN | 6.08 | 11.54 | 6.78 | 5.73 | 5.98 | 7.33 | 6.88 |
| LAB | 5.27 | 10.24 | 5.51 | 5.23 | 5.15 | 6.79 | 6.12 |
| Wing | 5.11 | 8.75 | 5.36 | 4.93 | 5.41 | 6.37 | 5.81 |
| DeCaFA | 4.62 | 8.11 | 4.65 | 4.41 | 4.63 | 5.74 | 5.38 |
| Awing | 4.36 | 7.38 | 4.58 | 4.32 | 4.27 | 5.19 | 4.96 |
| LUVLi | 4.37 | - | - | - | - | - | - |
| ADNet | 4.14 | 6.96 | 4.38 | 4.09 | 4.05 | 5.06 | 4.79 |
Citation
@inproceedings{huang2021adnet,
title={Adnet: Leveraging error-bias towards normal direction in face alignment},
author={Huang, Yangyu and Yang, Hao and Li, Chong and Kim, Jongyoo and Wei, Fangyun},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={3080--3090},
year={2021}
}
License
The project is released under the MIT License