Models
YOLOv3 PyTorch vs. YOLOR

YOLOv3 PyTorch vs. YOLOR

Both YOLOv3 PyTorch and YOLOR are commonly used in computer vision projects. Below, we compare and contrast YOLOv3 PyTorch and YOLOR.

Models

icon-model

YOLOv3 PyTorch

Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. PyTorch version.
Learn more about YOLOv3 PyTorch
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
YOLO
--
CNN, YOLO
--
Annotation Format
Instance Segmentation
Instance Segmentation
Framework
--
PyTorch
--
PyTorch
GitHub
--
View Repo
--
View Repo
GitHub Stars
7.2k+
--
2k+
--
License
GPL-3.0
--
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 YOLOv3 PyTorch and YOLOR with Autodistill

Compare YOLOv3 PyTorch 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.