damo-cv/TransReID

Health v2 · maintenance only

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1048 stars · 205 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of TransReID, an ICCV 2021 paper applying vision transformers to object re-identification. It provides training and evaluation code achieving state-of-the-art results on person re-ID (Market-1501, MSMT17, DukeMTMC) and vehicle re-ID (VehicleID, VeRi-776) benchmarks.

Use cases

• train a transformer model for person re-identification
• implement vehicle re-identification on VeRi-776 or VehicleID
• reproduce ICCV 2021 TransReID paper results
• fine-tune a ViT/DeiT backbone for re-ID tasks
• compare re-ID baselines on Market-1501 and MSMT17
• build a person search or surveillance matching system

When to choose

• you need state-of-the-art transformer-based person or vehicle re-ID models
• you want a research reference implementation with pretrained ViT/DeiT backbones
• you have GPU resources and standard re-ID datasets available

When to avoid

• you need a production-ready re-ID service with an API out of the box
• you lack a CUDA GPU, since training requires 16-32GB VRAM
• you need lightweight real-time inference on edge devices

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning image-processing python re-identification person-reid vehicle-reid vision-transformer pytorch iccv-2021 research-code gpu linux

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For agents

markdown · JSON · MCP: product_card(name="damo-cv/TransReID")