openvinotoolkit/open_model_zoo
Open Model Zoo
Open Model Zoo is a curated collection of pre-trained and optimized deep learning models designed for high-performance inference using OpenVINO. It serves as a model repository and deployment framework that streamlines the integration of neural networks into production environments.
The project utilizes a centralized manifest and a versioned registry to automate the downloading and organization of model weights and metadata. It includes tools for benchmarking inference performance and validating model accuracy by comparing outputs against ground-truth tensors to quantify precision loss.
The ecosystem provides reference implementations and a modular demo architecture to decouple inference engines from application logic. These tools support the implementation of computer vision tasks through standardized wrappers that abstract preprocessing and postprocessing requirements.
Features
- Deep Learning Models - Serves as a comprehensive library of pre-trained neural networks across various domains for immediate deployment.
- Pre-trained Model Zoos - Offers a curated collection of optimized deep learning models and demos designed for high-performance inference.
- Model Deployment - Facilitates the deployment of high-quality pre-trained models into production environments for efficient inference.
- Model Registries - Maintains a versioned catalog of pre-optimized weights tailored for specific hardware acceleration targets.
- Model Asset Managers - Provides automated systems for downloading, verifying, and maintaining libraries of model weights and metadata.
- Model Deployment Frameworks - Provides a framework for automatically downloading and organizing model assets to streamline production integration.
- Model Weight Management - Provides utilities for downloading and organizing pre-trained model weights and metadata via a centralized manifest.
- Model Downloaders - Ships a dedicated tool for fetching and organizing pre-trained model assets from remote sources.
- Inference Benchmarking Tools - Provides tools to validate the accuracy and measure the processing speed of deployed models.
- Accuracy Validation Utilities - Includes utilities for comparing model outputs to verify precision and quality against gold standards.
- Computer Vision Model Integration - Integrates pre-trained visual recognition models into applications using reference implementations and sample notebooks.
- Intermediate Representations - Translates diverse deep learning framework formats into a unified intermediate representation for hardware-independent execution.
- Reference Implementations - Provides standardized wrapper code as reference implementations to abstract model-specific preprocessing and postprocessing logic.
- Inference Precision Validators - Implements precision validation by comparing inference outputs against a fixed set of ground-truth tensors.
- Action recognition - Listed in the “Action recognition” section of the Ailia Models awesome list.
- Age gender estimation - Listed in the “Age gender estimation” section of the Ailia Models awesome list.
- Deep fashion - Listed in the “Deep fashion” section of the Ailia Models awesome list.
- Face detection - Listed in the “Face detection” section of the Ailia Models awesome list.
- Road detection - Listed in the “Road detection” section of the Ailia Models awesome list.
- Vehicle recognition - Listed in the “Vehicle recognition” section of the Ailia Models awesome list.
