NVlabs/DG-Net

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1298 stars · 226 forks observed · 2026-08-28

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

DG-Net is a PyTorch implementation of the CVPR 2019 (Oral) paper 'Joint Discriminative and Generative Learning for Person Re-identification'. It jointly trains discriminative re-id feature learning with generative appearance swapping to improve robustness across camera views.

Use cases

• train a person re-identification model
• search for a person across camera images
• run image retrieval on Market-1501 or DukeMTMC-ReID
• generate person images with swapped clothing appearance
• evaluate re-id models with multi-query and random erasing
• reproduce CVPR 2019 re-id paper results

When to choose

• you need a proven person re-id baseline in PyTorch
• you want to combine generative data augmentation with re-id training
• you work with standard re-id benchmarks like Market-1501, MSMT17, or CUHK-NP
• you need multi-GPU training with optional fp16 via APEX

When to avoid

• you need a commercially licensed model (CC BY-NC-SA 4.0)
• you need a maintained production system rather than research code
• you work outside person re-identification
• you cannot meet the 10-15GB GPU memory requirement

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing search-engine computer-vision machine-learning deep-learning image-processing python person-reidentification pytorch generative-models image-retrieval research-code cvpr-2019 non-commercial-license search linux gpu

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