---
library_name: py-feat
pipeline_tag: image-classification
tags:
- gaze-estimation
license: mit
---

# L2CS-Net (Gaze Estimation)

## Model Description

L2CS-Net regresses gaze direction (pitch, yaw) from a face crop. It
formulates gaze estimation as a 90-bin classification problem over each
axis (4°/bin resolution covering [-180°, +180°]), then computes the
expected value across bins for a continuous angle output. ResNet50
backbone, two parallel FC heads.

Reported accuracy (from the original L2CS-Net paper):
- Gaze360 test split: ~3.92° MAE
- MPIIFaceGaze leave-one-out: ~4.16° MAE

These are state-of-the-art numbers for gaze-from-face-crop estimation
(2022); for context, geometric iris-eye approaches typically land at
8-15° MAE on the same benchmarks.

## Model Details

- **Architecture**: ResNet50 + dual classification heads (2 × 90-bin)
- **Input**: 224 × 224 RGB face crop, ImageNet-normalized
- **Output**: pitch, yaw in radians (head-centric frame)
- **Bin resolution**: 4° per bin, covering [-180°, +180°]
- **Backbones available**: ResNet50 (default), ResNet18 (lighter)
- **Framework**: PyTorch (port of upstream MIT code)

## Training data (upstream)

- **Gaze360** (Kellnhofer et al., 2019): in-the-wild gaze annotations
  with 360° head pose coverage. ~127k images.
- **MPIIFaceGaze** (Zhang et al., 2017): unconstrained office captures
  with screen-targeted gaze ground truth. ~213k images.

The upstream maintainer trains separate checkpoints for each dataset.
Py-Feat exposes the **Gaze360** weights as the default since they
generalize better to in-the-wild input.

## Model Sources

- **Original repository (MIT)**: [Ahmednull/L2CS-Net](https://github.com/Ahmednull/L2CS-Net)
- **Paper**: [arXiv:2203.03339](https://arxiv.org/abs/2203.03339)
- **Pretrained weights (upstream)**: Google Drive folder linked from the
  upstream README (`L2CSNet_gaze360.pkl`). Py-Feat hosts a re-packaged
  `.safetensors` version on this repo to avoid the pickle (`.pkl`)
  deserialization path on user machines. The conversion is documented
  at `scripts/convert_l2cs_pickle_to_safetensors.py` in the py-feat
  repo; no architecture or weight values are modified.

## Acknowledgements

This distribution is a **re-host** of the official L2CS-Net weights
trained by Abdelrahman, Hempel, Khalifa, and Al-Hamadi (Otto-von-Guericke
University Magdeburg). All training, hyperparameter selection, and
benchmark numbers are credited to the original authors. The py-feat
project provides only:
- A PyTorch port of the inference code at `feat/gaze_detectors/l2cs/l2cs_model.py`
- This re-packaged `.safetensors` artifact for downstream safety
- Integration with `feat.Detector` and `feat.MPDetector`'s pipelines

Training data are credited to:
- Gaze360 — Kellnhofer, Recasens, Stent, Matusik, Torralba (MIT)
- MPIIFaceGaze — Zhang, Sugano, Fritz, Bulling (MPI Saarbrücken)

## Citation

```bibtex
@article{l2csnet2022,
  title={L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments},
  author={Abdelrahman, Ahmed and Hempel, Thorsten and Khalifa, Aly and
          Al-Hamadi, Ayoub},
  journal={arXiv preprint arXiv:2203.03339},
  year={2022}
}
```

## License

MIT — both the original implementation and the converted weights. See
[upstream LICENSE](https://github.com/Ahmednull/L2CS-Net/blob/main/LICENSE).
Training data licenses (Gaze360, MPIIFaceGaze) are research-use only;
commercial deployment may require separate validation.