Models
YOLOS vs. YOLOR

YOLOS vs. YOLOR

Both YOLOS and YOLOR are commonly used in computer vision projects. Below, we compare and contrast YOLOS and YOLOR.

Models

icon-model

YOLOS

YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP.
Learn more about YOLOS
icon-model

YOLOR

YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions.
Learn more about YOLOR
Model Type
Object Detection
--
Object Detection
--
Model Features
Item 1 Info
Item 2 Info
Architecture
Transformer, YOLO
--
CNN, YOLO
--
Frameworks
PyTorch
--
PyTorch
--
Annotation Format
Instance Segmentation
Instance Segmentation
GitHub
--
View Repo
--
View Repo
GitHub Stars
812+
--
2k+
--
License
MIT
--
GPL-3.0
--
Paper
--
View Paper
--
View Paper
Training Notebook
Train on Colab
--
Train on Colab
--
Deploy Model
--
rocket icon
Deploy with Roboflow
--
rocket icon
Deploy with Roboflow
Compare Alternatives
--
Compare with...
--
Compare with...

Compare YOLOS and YOLOR with Autodistill

Compare YOLOS vs. YOLOR

Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset.

COCO can detect 80 common objects, including cats, cell phones, and cars.