# facebookresearch/detr

End-to-End Object Detection with Transformers

Repository: https://github.com/facebookresearch/detr
Canonical: https://ross.abutalabs.com/products/detr
Language: Python
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2024-03-12T15:58:25+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2290, "days_push": 904, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 15354, forks 2669 (observed 2026-08-28T04:11:09.700034+00:00)

## What it is
DETR is Facebook Research's PyTorch implementation of Detection Transformer, an end-to-end object detection model that replaces hand-crafted pipelines with a Transformer encoder-decoder and bipartite matching loss. It ships pretrained models matching Faster R-CNN on COCO with simple training and inference code.

## Use cases
- detect objects in images with a transformer model
- train an end-to-end object detector on custom data
- run pretrained COCO object detection inference in a few lines of PyTorch
- compare DETR with Faster R-CNN baselines
- fine-tune a detection transformer on my dataset

## When to choose
- you want a simple, research-friendly object detection codebase without complex detection libraries
- you need transformer-based set prediction detection with pretrained COCO weights

## When to avoid
- you need production-grade detection tooling with extensive model zoo and deployment support
- you need the latest detection architectures or frequent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, machine-learning, deep-learning
- domain: computer-vision, deep-learning, machine-learning
- platform: python
- tags: object-detection, transformer, pytorch, set-prediction, coco, research, gpu

## Member repositories
- facebookresearch/detr (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:09.700034+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:06:21.351582+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/detr (fetched 2026-08-28T04:11:09.700034+00:00, sha 1a8be4194af3)
- Data as of 2026-08-30T08:39:29.467469+00:00.