YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions.
Here is an overview of the
model:
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.
This novel approach propels YOLOR to the state-of-the-art for object detection in the speed/accuracy tradeoff landscape.
Images in Courtesy of Wong-Kin-Yiu
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
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.
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.
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.
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.
Roboflow offers a range of SDKs with which you can deploy your model to production.
YOLOR
uses the
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.
You can automatically label a dataset using
YOLOR
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
YOLOR
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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