# RF-DETR Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/rf-detr
> Markdown URL: https://aitinkerers.org/technologies/rf-detr.md
> Technology record last updated: 2026-04-20T20:37:52Z
> Generated: 2026-10-24T17:15:58Z

RF-DETR optimizes end-to-end object detection by utilizing Receptive Field-based feature fusion to eliminate redundant computations in Transformer encoders.

RF-DETR addresses the high computational cost of the DETR architecture by introducing a Receptive Field-augmented encoder. By focusing on multi-scale feature fusion and spatial constraints, this model reduces the complexity of global self-attention mechanisms without sacrificing mAP scores. The implementation leverages a lightweight encoder design that achieves competitive performance on the COCO dataset (reaching 44.6 mAP with a ResNet-50 backbone) while significantly lowering GFLOPs. It is a precise solution for developers needing the accuracy of query-based detection on hardware with constrained processing power.

- Official technology site: https://github.com/SlongLiu/RF-DETR
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Detection was the easy part: re-identification on real store cameras](https://lahore.aitinkerers.org/talks/rsvp_rjgHdviyAUI)

- Event context: AI Tinkerers Lahore: September 19, 2026 — 2026-09-19 — Lahore
- Public talk page: https://lahore.aitinkerers.org/talks/rsvp_rjgHdviyAUI

### [Building Chessboard Recognition App with my daughter](https://toronto.aitinkerers.org/talks/rsvp_zTY6p1BvBBQ)

- Event context: AI Tinkerers Toronto - April 2026 - hosted by Shopify — 2026-04-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_zTY6p1BvBBQ

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