Scaled-YOLOv4 vs. Faster R-CNN

Both Scaled YOLOv4 and Faster R-CNN are commonly used in computer vision projects. Below, we compare and contrast Scaled YOLOv4 and Faster R-CNN.

Models

Scaled YOLOv4

Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.

Learn more about Scaled YOLOv4

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

YOLO

Annotation Format

Instance Segmentation

Framework

PyTorch

TensorFlow 1.5

GitHub

View Repo

GitHub Stars

2k+

7.5k+

License

GPL-3.0

MIT

Paper

View Paper

Training Notebook

Train on Colab

Deploy Model

Deploy with Roboflow

Compare Scaled-YOLOv4 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.

Scaled-YOLOv4 vs. Faster R-CNN

Models

Scaled YOLOv4

Faster R-CNN

Compare Scaled YOLOv4 and Faster R-CNN with Autodistill

Compare Scaled-YOLOv4 vs. Faster R-CNN