YOLOv5 vs. Faster R-CNN: Compared and Contrasted

Models

YOLOv5

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.

Faster R-CNN

One of the most accurate object detection algorithms but requires a lot of power at inference time. A good choice if you can do processing asynchronously on a server.

Learn more about Faster R-CNN

Model Type

Object Detection

Model Features

Item 1 Info

Item 2 Info

Architecture

CNN, YOLO

Annotation Format

Instance Segmentation

Framework

PyTorch

TensorFlow 1.5

GitHub

View Repo

GitHub Stars

46k+

7.5k+

License

AGPL-3.0

MIT

Paper

View Paper

Training Notebook

Train on Colab

Deploy Model

Deploy with Roboflow

Compare YOLOv5 vs. Faster R-CNN

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.

YOLOv5 vs. Faster R-CNN

Models

YOLOv5

Faster R-CNN

Compare YOLOv5 and Faster R-CNN with Autodistill

Compare YOLOv5 vs. Faster R-CNN