Add model card with attribution

README.md

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+---
+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.