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Using modified BiSeNet for face parsing in PyTorch
| Date | Stars |
|---|---|
| 2026-07-24 | 2585 |
| 2026-07-25 | 2585 |
| 2026-07-28 | 2585 |
| 2026-07-30 | 2585 |
| 2026-08-06 | 2585 |
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# face-parsing.PyTorch
<p align="center">
<a href="https://github.com/zllrunning/face-parsing.PyTorch">
<img class="page-image" src="https://github.com/zllrunning/face-parsing.PyTorch/blob/master/6.jpg" >
</a>
</p>
### Contents
- [Training](#training)
- [Demo](#Demo)
- [References](#references)
## Training
1. Prepare training data:
-- download [CelebAMask-HQ dataset](https://github.com/switchablenorms/CelebAMask-HQ)
-- change file path in the `prepropess_data.py` and run
```Shell
python prepropess_data.py
```
2. Train the model using CelebAMask-HQ dataset:
Just run the train script:
```
$ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py
```
If you do not wish to train the model, you can download [our pre-trained model](https://drive.google.com/open?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812) and save it in `res/cp`.
## Demo
1. Evaluate the trained model using:
```Shell
# evaluate using GPU
python test.py
```
## Face makeup using parsing maps
[**face-makeup.PyTorch**](https://github.com/zllrunning/face-makeup.PyTorch)
<table>
<tr>
<th> </th>
<th>Hair</th>
<th>Lip</th>
</tr>
<!-- Line 1: Original Input -->
<tr>
<td><em>Original Input</em></td>
<td><img src="makeup/116_ori.png" height="256" width="256" alt="Original Input"></td>
<td><img src="makeup/116_lip_ori.png" height="256" width="256" alt="Original Input"></td>
</tr>
<!-- Line 3: Color -->
<tr>
<td>Color</td>
<td><img src="makeup/116_1.png" height="256" width="256" alt="Color"></td>
<td><img src="makeup/116_3.png" height="256" width="256" alt="Color"></td>
</tr>
</table>
## References
- [BiSeNet](https://github.com/CoinCheung/BiSeNet)Excerpt of 1,686 characters
Read on GitHub
zll · China
11
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c69c95cf0882babd, topic:pytorch