fundamentalvision/Deformable-DETR

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4015 stars · 632 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official PyTorch implementation of Deformable DETR, an efficient end-to-end object detector that uses deformable attention to fix DETR's slow convergence and limited feature resolution. It includes training and evaluation code with COCO benchmark results and pretrained models.

Use cases

• train a fast-converging end-to-end object detector
• detect small objects better than vanilla DETR
• reproduce Deformable DETR results on COCO
• use deformable attention in my own detection model
• compare detector performance against Faster R-CNN and DETR baselines

When to choose

• you need an end-to-end transformer-based object detector with 10x fewer training epochs than DETR
• your dataset has many small objects where DETR underperforms
• you want a research reference implementation of deformable attention

When to avoid

• you need a production-ready, actively maintained detection framework
• you want plug-and-play pretrained detectors for many architectures
• you lack GPU resources for training detection models

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning artificial-intelligence python object-detection transformer detr attention-mechanism coco research-code pytorch linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="fundamentalvision/Deformable-DETR")