YOLOv4 Tiny vs. Faster R-CNN

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

Models

YOLOv4 Tiny

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

Learn more about YOLOv4 Tiny

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

ResNet-D, YOLO

Annotation Format

Instance Segmentation

Framework

Darknet

TensorFlow 1.5

GitHub

View Repo

GitHub Stars

7.5k+

License

YOLO

MIT

Paper

View Paper

Training Notebook

Train on Colab

Deploy Model

Deploy with Roboflow

Compare YOLOv4 Tiny 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.

YOLOv4 Tiny vs. Faster R-CNN

Models

YOLOv4 Tiny

Faster R-CNN

Compare YOLOv4 Tiny and Faster R-CNN with Autodistill

Compare YOLOv4 Tiny vs. Faster R-CNN