Detection Transformer (DETR) is an end-to-end object detection model implemented using the Transformer architecture. DETR was developed by Facebook Research. The baseline DETR model has an AP score of 42.0% on COCO.

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Model Performance

	name	backbone	schedule	inf_time	box AP	url	size
0	DETR	R50	500	0.036	42.0	model \| logs	159Mb
1	DETR-DC5	R50	500	0.083	43.3	model \| logs	159Mb
2	DETR	R101	500	0.050	43.5	model \| logs	232Mb
3	DETR-DC5	R101	500	0.097	44.9	model \| logs	232Mb

Explore this model on Roboflow

How to Train Object Detection Transformer on Custom Dataset

In this tutorial, we'll show you how to train Object Detection Transformers using DETR.

Deploy DETR to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

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DETR Annotation Format

DETR

uses the

COCO JSON

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
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Convert data between formats

Label data automatically with DETR

You can automatically label a dataset using

DETR

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

DETR

to train a computer vision model.

OWL-ViT and DETR

Grounding DINO and DETR

SAM-CLIP and DETR

Grounded SAM and DETR

DETIC and DETR