Archived / discontinued. This resource is no longer actively maintained.
Archived / discontinued. This resource is no longer actively maintained. Historical details are retained for reference; verify current status on the official source before use.
Data checked 2026-08-13. Data may be stale - beyond the review cycle.
Review cycles are documented on the Methodology page. Methodology
facebookresearch/detr
AI Model Tier A perception_ai Apache-2.0
End-to-End Object Detection with Transformers
Engineering Snapshot
Use cases & tasks 4
- Best suited for
-
- academic research
- perception prototyping
- Primary tasks
-
- image object detection
- set prediction of bounding boxes
Stack & ecosystem
- Resource type
- AI Model
- Ecosystem
- perception_ai
License & compliance
- License
-
Apache-2.0(Inferred)
Lifecycle & freshness Show
- Maintenance
- Archived
- Latest version
- Not recorded
- Last activity
- 2026-08-13
- Last checked
- 2026-08-13
- Verification
- Official confirmed
What It Solves
End-to-end object detection without hand-crafted components (NMS/anchors) using transformers.
Primary use cases
- image object detection
- set prediction of bounding boxes
Secondary use cases
- baseline transformer detector
When to Use
Consider when
- anchor-free end-to-end detection needed
- training compute available
Verify before adopting
- PyTorch framework claim not independently verified
- license inferred Apache-2.0
- training data exact COCO subset
Start Here
Adoption Checklist
- Needs verification PyTorch framework claim not independently verified
- Needs verification license inferred Apache-2.0
- Needs verification training data exact COCO subset
Each check stays "needs verification" until an official source confirms it; unconfirmed items are never marked verified.
Known Limitations & Unknowns
Known limitations
- Repository archived; no active maintenance since 2022
- Slow convergence (default 500 epochs on COCO)
- High training compute cost
- Inference slower than optimized CNN detectors
- Struggles with small objects
Not publicly verified
- model size
- runtime
- edge feasibility
Alternatives & Related Tools
Alternatives
Related tools
- bytetrack — Used for
How is it used?
Start from the recorded entry points below, then validate against the technical checklist.
- Official site https://github.com/facebookresearch/detr
- Repository https://github.com/facebookresearch/detr
- Documentation https://github.com/facebookresearch/detr
Technical checklist
- OK License identified Recorded: Apache-2.0
- NEEDS REVIEW Maintenance signal Archived
- OK Verification status Official confirmed
- OK Source evidence attached 1 source record(s)
- NEEDS REVIEW Latest version recorded Not recorded
Official Links
Metadata & Governance
| License | Apache-2.0 — Inferred |
|---|---|
| Commercial model | Unknown |
| Maintenance status | Archived |
| Verification status | Official confirmed — Confirmed via the official repository API responses in SourceRefs below. |
| Latest version | Not recorded |
| Latest release | Not recorded |
| Last activity | 2026-08-13 |
| Last checked | 2026-08-13 |
| First seen | Not recorded |
Dataset facts
Facts above come from the official dataset card only; unconfirmed fields stay unknown.
Related Resources & Dependencies
Recent Activity
New resource: facebookresearch/detr
New repository resource added by the sprint promote pipeline.
Related Knowledge
Collections
- Aerial Perception Models and Datasets — Source-backed aerial dataset and model repositories for detection and tracking research and prototyping.
- Visual Perception Models and Data — Indexed model and dataset repositories for aerial visual perception research and prototyping.