| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - hypervision |
| - object-detection |
| - computer-vision |
| - lightweight |
| - mobile |
| - myndlabs |
| - ncnn |
| - mnn |
| - openvino |
| - android |
| - pytorch |
| library_name: hypervision |
| datasets: |
| - coco |
| --- |
| |
| <div align="center"> |
|
|
| # HyperVision |
| **Lightweight anchor-free object detection model. Real-time on mobile devices.** |
|
|
| **Premium Edition by [Myndlabs.tech](https://myndlabs.tech)** |
|
|
| </div> |
|
|
| HyperVision is an anchor-free one-stage object detection model based on Generalized Focal Loss. It is designed for efficient on-device inference across CPU, GPU, and mobile NPU backends. |
|
|
| ## Repository Contents |
|
|
| This repository provides: |
|
|
| - **Source code** for training and inference |
| - **Python demo** for image, video, and webcam inference |
| - **Android demo** (`demo_android_ncnn/`) using ncnn |
| - **NCNN C++ demo** (`demo_ncnn/`) |
| - **MNN C++ demo** (`demo_mnn/`) |
| - **OpenVINO C++ demo** (`demo_openvino/`) |
| - **LibTorch C++ demo** (`demo_libtorch/`) |
| - **Jupyter notebook** walkthrough (`demo/demo-inference-with-pytorch.ipynb`) |
| - **Multi-backend model export** tools (ONNX, TorchScript) |
| - **Training pipeline** using PyTorch Lightning |
|
|
| ## Install |
|
|
| ### Requirements |
|
|
| - Linux, macOS, or Windows |
| - Python >= 3.7 |
| - PyTorch >= 1.10.0, < 2.0.0 |
|
|
| ### Quick Start |
|
|
| ```shell script |
| # Clone the repository |
| git clone https://github.com/Yethikrishna/hypervision.git |
| cd hypervision |
| |
| # Install dependencies |
| pip install -r requirements.txt |
| |
| # Setup HyperVision |
| python setup.py develop |
| ``` |
|
|
| ## Demo |
|
|
| ### PyTorch Inference |
|
|
| ```bash |
| # Image inference |
| python demo/demo.py image --config CONFIG_PATH --model MODEL_PATH --path IMAGE_PATH |
| |
| # Video inference |
| python demo/demo.py video --config CONFIG_PATH --model MODEL_PATH --path VIDEO_PATH |
| |
| # Webcam inference |
| python demo/demo.py webcam --config CONFIG_PATH --model MODEL_PATH --camid YOUR_CAMERA_ID |
| ``` |
|
|
| A Jupyter notebook is also available at `demo/demo-inference-with-pytorch.ipynb`. |
|
|
| ### Android |
|
|
| See `demo_android_ncnn/README.md`. |
|
|
| ### NCNN, MNN, OpenVINO, LibTorch |
|
|
| See the respective README files in `demo_ncnn/`, `demo_mnn/`, `demo_openvino/`, and `demo_libtorch/`. |
|
|
| ## Training |
|
|
| 1. Prepare your dataset in COCO, Pascal VOC XML, or YOLO format. |
| 2. Copy and modify a config file from `config/`. |
| 3. Run training: |
|
|
| ```shell script |
| python tools/train.py CONFIG_FILE_PATH |
| ``` |
|
|
| TensorBoard logs are saved to the directory specified in the config file. |
|
|
| ## Model Export |
|
|
| ```shell script |
| # Export to ONNX |
| python tools/export_onnx.py --cfg_path CONFIG_PATH --model_path MODEL_PATH |
| |
| # Export to TorchScript |
| python tools/export_torchscript.py --cfg_path CONFIG_PATH --model_path MODEL_PATH |
| ``` |
|
|
| ## Citation |
|
|
| If you use this project in your research, please cite: |
|
|
| ```BibTeX |
| @misc{hypervision, |
| title={HyperVision: Lightweight anchor-free object detection model}, |
| author={Yethikrishna R}, |
| howpublished = {\url{https://github.com/Yethikrishna/hypervision}}, |
| year={2025}, |
| note={Premium edition published by Myndlabs.tech} |
| } |
| ``` |
|
|
| ## License |
|
|
| Licensed under the Apache License, Version 2.0. See `LICENSE` for details. |
|
|
| --- |
|
|
| **Premium Edition** published by **[Myndlabs.tech](https://myndlabs.tech)** — Enterprise-grade object detection solutions. |
