477_PGC

477_PGC/LICENSE

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+MIT License
+
+Copyright (c) 2025 Katsuya Hyodo
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

477_PGC/README.md

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+# 477_PGC
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17564899.svg)](https://doi.org/10.5281/zenodo.17564899) ![GitHub License](https://img.shields.io/github/license/pinto0309/pgc) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/PINTO0309/pgc)
+
+Ultrafast pointing gesture classification. Classify whether the finger is pointing near the center of the camera lens.
+
+A model that can only detect slow human gestures is completely worthless. A resolution of 32x32 is sufficient for human hand gesture classification. LSTM and 3DCNN are useless because they are not robust to environmental noise.
+
+https://github.com/user-attachments/assets/19268cf9-767c-441e-abc0-c3abd8dba57a
+
+|Variant|Size|F1|CPU<br>inference<br>latency|ONNX|
+|:-:|:-:|:-:|:-:|:-:|
+|S|494 KB|0.9524|0.43 ms|[Download](https://github.com/PINTO0309/PGC/releases/download/onnx/pgc_s_32x32.onnx)|
+|C|875 KB|0.9626|0.50 ms|[Download](https://github.com/PINTO0309/PGC/releases/download/onnx/pgc_c_32x32.onnx)|
+|M|1.7 MB|0.9714|0.59 ms|[Download](https://github.com/PINTO0309/PGC/releases/download/onnx/pgc_m_32x32.onnx)|
+|L|6.4 MB|0.9782|0.78 ms|[Download](https://github.com/PINTO0309/PGC/releases/download/onnx/pgc_l_32x32.onnx)|
+
+## Setup
+
+```bash
+git clone https://github.com/PINTO0309/PGC.git && cd PGC
+curl -LsSf https://astral.sh/uv/install.sh | sh
+uv sync
+source .venv/bin/activate
+```
+
+## Inference
+
+```bash
+uv run python demo_pgc.py \
+-pm pgc_l_32x32.onnx \
+-v 0 \
+-ep cuda \
+-dlr
+
+uv run python demo_pgc.py \
+-pm pgc_l_32x32.onnx \
+-v 0 \
+-ep tensorrt \
+-dlr
+```
+
+## Arch
+
+<img width="300" alt="pgc_s_32x32" src="https://github.com/user-attachments/assets/f6a6efcc-0b05-4cbe-b578-1c72312c1b61" />
+
+## Citation
+
+If you find this project useful, please consider citing:
+
+```bibtex
+@software{hyodo2025pgc,
+  author    = {Katsuya Hyodo},
+  title     = {PINTO0309/PGC},
+  month     = {11},
+  year      = {2025},
+  publisher = {Zenodo},
+  doi       = {10.5281/zenodo.17564899},
+  url       = {https://github.com/PINTO0309/pgc},
+  abstract  = {Ultrafast pointing gesture classification.},
+}
+```
+
+## Acknowledgements
+- https://gibranbenitez.github.io/IPN_Hand/: CC BY 4.0 License
+  ```bibtex
+  @inproceedings{bega2020IPNhand,
+    title={IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition},
+    author={Benitez-Garcia, Gibran and Olivares-Mercado, Jesus and Sanchez-Perez, Gabriel and Yanai, Keiji},
+    booktitle={25th International Conference on Pattern Recognition, {ICPR 2020}, Milan, Italy, Jan 10--15, 2021},
+    pages={4340--4347},
+    year={2021},
+    organization={IEEE}
+  }
+  ```
+- https://github.com/PINTO0309/PINTO_model_zoo/tree/main/472_DEIMv2-Wholebody34: Apache 2.0 License
+  ```bibtex
+  @software{DEIMv2-Wholebody34,
+    author={Katsuya Hyodo},
+    title={Lightweight human detection models generated on high-quality human data sets. It can detect objects with high accuracy and speed in a total of 28 classes: body, adult, child, male, female, body_with_wheelchair, body_with_crutches, head, front, right-front, right-side, right-back, back, left-back, left-side, left-front, face, eye, nose, mouth, ear, collarbone, shoulder, solar_plexus, elbow, wrist, hand, hand_left, hand_right, abdomen, hip_joint, knee, ankle, foot.},
+    url={https://github.com/PINTO0309/PINTO_model_zoo/tree/main/472_DEIMv2-Wholebody34},
+    year={2025},
+    month={10},
+    doi={10.5281/zenodo.10229410}
+  }
+  ```
+- https://github.com/PINTO0309/bbalg: MIT License
+- https://github.com/PINTO0309/PGC: MIT License