README.md

metadata

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

HyperVision

Lightweight anchor-free object detection model. Real-time on mobile devices.

Premium Edition by Myndlabs.tech

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

# 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

# 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:

python tools/train.py CONFIG_FILE_PATH

TensorBoard logs are saved to the directory specified in the config file.

Model Export

# 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:

@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.

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Premium Edition published by Myndlabs.tech — Enterprise-grade object detection solutions.