What is YOLOR?

YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions.

About the model

Here is an overview of the

YOLOR

model:

Date of Release May 10, 2021
Model Type Object Detection
Architecture CNN, YOLO
Framework Used PyTorch
Annotation Format YOLOv5 PyTorch TXT
Stars on GitHub 1800+

What is YOLOR?

You Only Learn One Representation (YOLOR) is a state-of-the-art object detection model. YOLOR pre-trains an implicit knowledge network with all of the tasks present in the COCO dataset, namely object detection, instance segmentation, panoptic segmentation, keypoint detection, stuff segmentation, image caption, multi-label image classification, and long-tail object recognition. When optimizing for the COCO dataset, YOLOR trains another set of parameters that represent explicit knowledge. For prediction, both implicit and explicit knowledge is used.

YOLOR Architecture

YOLOR Architecture

Vision Transformer Performance

This novel approach propels YOLOR to the state-of-the-art for object detection in the speed/accuracy tradeoff landscape.

YOLOR Performance


Images in Courtesy of Wong-Kin-Yiu

Further Reading

Train YOLOR on a Custom Dataset: https://blog.roboflow.com/train-yolor-on-a-custom-dataset/
YOLOR Research Paper: https://arxiv.org/abs/2105.04206

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Model Performance

Explore this model on Roboflow

YOLOR Annotation Format

YOLOR

uses the

YOLOv5 PyTorch TXT

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.

Convert data between formats

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