| --- |
| 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. |
|
|
