Paper link: AAAI 2023
Updated: 12/20/2023
| Dataset | Dataset | Metadata (train, test) | Description |
|---|---|---|---|
| Enriched 300W | official | google / baidu | The metadata contains both train.tsv and test.tsv, where train.tsv is refined by FreeEnricher and test.tsv is manually labeled. |
| Enriched WFLW | official | google / baidu | The metadata only contains train.tsv, where train.tsv is refined by FreeEnricher. |
| Folder | Description |
|---|---|
| ADNet | The ADNet codebase of training and testing. |
| conf | The enriched version configure files. |
- Step1: Clone and Install ADNet.
- Step2: Replace the conf file of ADNet with enriched version.
- 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 in ADNet.
The framework of FreeEnricher.
Table 1. Performance of our method on enriched 300W.
| Method | Network | NMEpoint | NMEedge |
|---|---|---|---|
| Baseline | ADNet + Line5 | 3.21 | 1.18 |
| Ours | ADNet-FE5 | 3.06 | 0.98 |
Table 2. Comparing with state-of-the-art methods on original 300W and WFLW.
| Method | 300W | WFLW |
|---|---|---|
| LAB | 3.49 | 5.27 |
| HRNet | 3.34 | 4.60 |
| LUVLi | 3.23 | 4.37 |
| ADNet | 2.93 | 4.14 |
| ADNet-FE5 | 2.87 | 4.10 |
TBD
The project is released under the MIT License