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FastReID

FastReID is an open-source Python and PyTorch research toolbox for implementing, training and evaluating instance re-identification models, including person and vehicle re-identification. It also supports image retrieval and face recognition, and provides model-zoo baselines and examples for converting models to Caffe, ONNX and TensorRT.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

FastReID — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use FastReID when you need a code-based PyTorch framework for re-identification research or development, from model training and evaluation through documented model-conversion examples.

Who it is for

  • Computer-vision researchers experimenting with re-identification methods.
  • Developers building image retrieval or person and vehicle re-identification workflows.
  • Teams evaluating models and preparing conversion paths for downstream inference.

Documented uses

  • Implement and train instance re-identification models.
  • Evaluate model results using the toolbox’s metrics and baselines.
  • Use pretrained model-zoo entries as starting points for dataset-specific experiments.
  • Convert trained models using documented Caffe, ONNX or TensorRT examples.
Before you choose

Requirements & limits

The repository is public, open source and licensed under Apache 2.0.

  • The primary workflow is through Python/PyTorch code, with installation and getting-started instructions in the repository.
  • The model zoo offers baselines and trained models that can serve as experiment starting points.
  • Conversion examples cover Caffe, ONNX and TensorRT; downstream runtime and model compatibility need separate testing.
  • The repository documents distributed training across multiple GPUs.

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

Find FastReID installation and getting-started instructions

Use the repository’s documented entry points to reach setup and quick-start guidance.

Before you start

  • Access to the FastReID repository.

Steps

  1. Open the repository README.

  2. Follow the README’s Installation link to INSTALL.md.

  3. For the quick start, follow the README’s link to GETTING_STARTED.md.

Expected outcome

The repository’s installation and quick-start instructions are available to follow.

Guide limitations

  • The README points to these instructions but does not include the setup or execution steps themselves.
Documentation 1 ↗
Source-based practical guide

Locate FastReID model conversion examples

Find the repository examples and scripts for converting FastReID models to deployment formats.

Before you start

  • Access to the FastReID repository.

Steps

  1. Open the repository README.

  2. Go to the FastReID deploy directory referenced under Deployment.

  3. Review the available examples and scripts for conversion to Caffe, ONNX, or TensorRT.

Expected outcome

The repository’s model-conversion examples and scripts are located.

Guide limitations

  • The README does not provide conversion commands or establish compatibility with a particular model or runtime.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
JDAI-CV/fast-reid · Official homepage title · retained in its source language
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