YOLOR vs. YOLOv4 Tiny: Compared and Contrasted

Models

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

The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a feel for your dataset

Model Type

Object Detection

Model Features

Item 1 Info

Item 2 Info

Architecture

CNN, YOLO

ResNet-D, YOLO

Annotation Format

Instance Segmentation

Framework

PyTorch

Darknet

GitHub

GitHub Stars

2k+

License

GPL-3.0

YOLO

Paper

Training Notebook

Deploy Model

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.