Faster R-CNN vs. YOLOv4 Tiny

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

Models

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

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

Model Type

Object Detection

Model Features

Item 1 Info

Item 2 Info

Architecture

ResNet-D, YOLO

Annotation Format

Instance Segmentation

Framework

TensorFlow 1.5

Darknet

GitHub

View Repo

GitHub Stars

7.5k+

License

MIT

YOLO

Paper

View Paper

Training Notebook

Train on Colab

Deploy Model

Deploy with Roboflow

Compare Faster R-CNN vs. YOLOv4 Tiny

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.

Faster R-CNN vs. YOLOv4 Tiny

Models

Faster R-CNN

YOLOv4 Tiny

Compare Faster R-CNN and YOLOv4 Tiny with Autodistill

Compare Faster R-CNN vs. YOLOv4 Tiny