File size: 5,116 Bytes
05bc5df 056d92f 05bc5df 056d92f 03f229d 056d92f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | ---
license: mit
pipeline_tag: image-segmentation
tags:
- faceocc
- face-segmentation
- face-occlusion
- visible-face-segmentation
- semantic-segmentation
- pytorch
- segmentation-models-pytorch
---
# FaceOcc
Modernized canonical FaceOcc model for visible-face segmentation.
This model is based on **FaceOcc**, introduced by Xiangnan Yin and
Liming Chen, and the accompanying upstream FaceExtraction implementation.
The original FaceOcc work should be cited when using the underlying
method or dataset.
This release provides the canonical model weights from the modernized
FaceOcc implementation available at:
**https://github.com/mertakinstd/FaceOcc**
## Model
| Property | Value |
| --- | --- |
| Architecture | U-Net |
| Encoder | ResNet18 |
| Encoder pretraining | ImageNet |
| Input | RGB, 256 × 256 |
| Input range | `[0, 1]` before normalization |
| Normalization | ImageNet mean/std |
| Output | Single-channel logits |
| Canonical probability threshold | `0.5` |
| Training objective | Global OHEM-BCE |
| Precision | IEEE FP32 |
| Checkpoint format | SafeTensors |
### Input normalization
Images are normalized immediately before the model forward pass using:
```text
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
```
Masks are not normalized.
The released checkpoint contains the complete trained model weights.
ImageNet weights are therefore not required separately when loading the
checkpoint.
## Reference result
The canonical checkpoint was selected by maximum COFW validation face
IoU at a probability threshold of `0.5`.
| Metric | Value |
| --- | ---: |
| COFW face IoU @ p=0.5 | **0.936523** |
| Best epoch | **24 / 30** |
| Dice | **0.966749** |
| Precision | **0.948656** |
| Recall | **0.986324** |
| Boundary IoU | **0.682287** |
| IoU p10 | **0.893731** |
| IoU median | **0.945921** |
| IoU p90 | **0.971353** |
COFW was used for validation and checkpoint selection in this training
protocol and should not be interpreted as an untouched external test set.
The canonical reference run was trained on a single
**NVIDIA GeForce RTX 3060 12 GB** GPU.
## Loading the checkpoint
The model is implemented with
[`segmentation_models_pytorch`](https://github.com/qubvel-org/segmentation_models.pytorch).
A corresponding model can be instantiated and the SafeTensors
checkpoint loaded as follows:
```python
import segmentation_models_pytorch as smp
from safetensors.torch import load_file
model = smp.Unet(
encoder_name="resnet18",
encoder_weights=None,
in_channels=3,
classes=1,
)
state_dict = load_file("model.safetensors")
model.load_state_dict(state_dict, strict=True)
model.eval()
```
`encoder_weights=None` is intentional when loading the released
checkpoint because all trained encoder and decoder parameters are already
contained in `model.safetensors`.
For the complete preprocessing and inference implementation, use the
source repository linked below.
## Intended use
FaceOcc predicts a binary visible-face mask:
- `1`: visible facial surface
- `0`: background or occluding region
The model is intended for visible-face segmentation and downstream
applications that require a facial region of interest.
It is not a face-recognition, identity-verification, or landmark model.
## Limitations
Performance can vary with image alignment, capture conditions,
occlusion type, image quality, and annotation policy.
The canonical `0.5` probability threshold is retained for reproducible
comparison with the released training protocol. Application-specific
threshold tuning should be validated independently.
## Files
- `model.safetensors` — canonical FaceOcc v1.0.0 weights
- `config.json` — model and inference contract
- `training_config.json` — canonical training provenance
- `README.md` — model card
- `LICENSE` — software license
## Links
- **Source code:** https://github.com/mertakinstd/FaceOcc
- **Archived software release:** https://doi.org/10.5281/zenodo.22261117
- **Original FaceExtraction repository:** https://github.com/face3d0725/FaceExtraction
- **Original FaceOcc paper:** https://arxiv.org/abs/2201.08425
## Citation
If you use FaceOcc or the underlying face-extraction method, please cite
the original FaceOcc paper:
```bibtex
@article{yin2022faceocc,
title = {FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction},
author = {Yin, Xiangnan and Chen, Liming},
journal = {arXiv preprint arXiv:2201.08425},
year = {2022}
}
```
If this modernized implementation or the released model weights
contribute to published work, please also consider citing the archived
software release:
**Mert Akın. FaceOcc: Modernized Implementation, v1.0.0.**
https://doi.org/10.5281/zenodo.22261117
## License
Distributed under the MIT License. See `LICENSE`.
Attribution and provenance information for the original FaceOcc work
and subsequent modernization contributions are provided in `NOTICE.md`.
Third-party datasets, pretrained resources, and external artifacts
remain subject to their respective licenses and terms.
|