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Opencv Zoo

OpenCV Zoo is an open-source collection of deep-learning models tuned for OpenCV DNN, accompanied by demo scripts and benchmark results across listed hardware. Its examples cover visual tasks such as detection, recognition, segmentation, tracking and pose estimation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Opencv Zoo — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use OpenCV Zoo when selecting or exploring models for an OpenCV DNN workflow, or when the published benchmark table can inform hardware comparisons. The README points to Hugging Face for current model access and online demos.

Who it is for

  • Developers building computer-vision applications with OpenCV DNN.
  • Teams comparing published model performance across the hardware configurations listed in the benchmarks.

Documented uses

  • Find an OpenCV DNN-oriented model for a visual task such as face detection, object tracking or image segmentation.
  • Use a model's demo script as a starting point for an OpenCV-based application.
  • Compare the repository's reported inference timings for models and listed devices.
Before you choose

Requirements & limits

The repository is public and lists an Apache-2.0 license. The README says the project is now hosted on Hugging Face for models and online demos.

  • The collection is specifically tuned for OpenCV DNN.
  • Benchmark figures describe particular hardware and a batch size of one; they include preprocessing, forward processing and postprocessing and average 10 runs after warm-up.
  • A dash in the benchmark table indicates that a model was not available to run on that device.
  • Some accelerator configurations require a specifically built OpenCV backend, including TIM-VX for the Khadas VIM3 NPU and CANN for the Atlas 200 DK.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the repository-based model collection

Install the stated Python package and download the repository's models and demo scripts.

Before you start

  • Python 3 with pip
  • Git
  • Git LFS

Steps

  1. Install or upgrade the package with the command below.

    Documented command
    python3 -m pip install --upgrade opencv-python

  2. Clone the repository with the command below.

    Documented command
    git clone https://github.com/opencv/opencv_zoo && cd opencv_zoo

  3. Run the command below and then the command below.

    Documented command
    git lfs install
    Documented command
    git lfs pull

Expected outcome

The repository's model files and demo scripts are downloaded locally.

Guide limitations

  • The README directs users to Hugging Face for models and online demos.
  • This setup guide does not cover running a particular model.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Opencv ZooFind an OpenCV DNN-oriented model for a visual task such as face detection, object tracking or image segmentation.Windows, Linux, macOSThe repository is public and lists an Apache-2.0 license. The README says the project is now hosted on Hugging Face for models and online demos.
PolygonRNN++Generate polygon outlines as initial instance annotations for image crops.Linux, Self-hostedThe repository is public and licensed under GPL-3.0.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
4 November 2026
Original website title
opencv/opencv_zoo · Official homepage title · retained in its source language
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