opencv_zoo - Extensive Collection of Optimized Models with Performance Metrics

OpenCV Zoo: A Model Zoo for Deep Learning with Benchmarks

OpenCV Zoo is a comprehensive repository and benchmarking platform for deep learning models fine-tuned for use with OpenCV's Deep Neural Network (DNN) module. It provides a wide range of models and performance benchmarks across various hardware platforms, offering valuable insights for developers and researchers working in computer vision.

Getting Started with OpenCV Zoo

To start exploring OpenCV Zoo, users first need to ensure they have the latest version of OpenCV installed. This can be done using Python's package installer:

python3 -m pip install opencv-python
# Or upgrade to the latest version
python3 -m pip install --upgrade opencv-python

After setting up OpenCV, the next step is to clone the OpenCV Zoo repository, which contains all the models and demo scripts. It is recommended to use Git Large File Storage (LFS) for handling large files:

# Install git-lfs from https://git-lfs.github.com/
git clone https://github.com/opencv/opencv_zoo && cd opencv_zoo
git lfs install
git lfs pull

Detailed instructions for running benchmarks on specific hardware configurations are provided in the repository’s benchmark documentation.

Overview of Models and Benchmark Results

OpenCV Zoo includes a diverse collection of models with pre-configured benchmarks. These benchmarks are executed on a variety of hardware setups, including x86-64, ARM, and RISC-V architectures, encompassing powerful processors, NPUs (Neural Processing Units), and customized chips designed for AI computation.

The repository offers benchmark data that includes the elapsed time of inference processes such as pre-processing, forward pass, and post-processing. This data is averaged over multiple runs for accuracy, with a consistent batch size of one for all results. Models vary in compatibility with different hardware, which is mapped out in the benchmark tables.

Example Models and Demos

OpenCV Zoo hosts a multitude of example models, showcasing capabilities from face detection to text recognition. Some notable examples include:

Face Detection with YuNet: Provides face detection capability with high efficiency.
Face Recognition with SFace: Focuses on reliable face recognition tasks.
Human Segmentation with PP-HumanSeg: Segment people from the background in images or videos.
Object Detection with NanoDet and YOLOX: Detects various objects using state-of-the-art neural networks.
QR Code Detection and Parsing with WeChatQRCode: Scans and decodes QR codes perfectly.

Each model comes with detailed documentation and visual examples that illustrate their respective use cases, making it easy for users to integrate these solutions into their projects.

License and Contributions

OpenCV Zoo is distributed under the Apache 2.0 license, allowing for open collaboration and modification. Users are encouraged to check individual model licenses to comply with specific terms.

In essence, OpenCV Zoo serves as a valuable resource for anyone interested in leveraging DNN models with OpenCV, offering a rich ecosystem of models and tailored benchmarks across numerous computational environments.