Faster R-CNN vs. YOLOR: Compared and Contrasted

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

YOLOR

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

Learn more about YOLOR

Model Type

Object Detection

Model Features

Item 1 Info

Item 2 Info

Architecture

CNN, YOLO

Frameworks

TensorFlow 1.5

PyTorch

Annotation Format

Instance Segmentation

GitHub

View Repo

GitHub Stars

7.5k+

2k+

License

MIT

GPL-3.0

Paper

Training Notebook

Deploy Model

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Faster R-CNN vs. YOLOR

Both

Faster R-CNN

and

YOLOR

are commonly used in computer vision projects. Below, we compare and contrast

Faster R-CNN

and

YOLOR

	Faster R-CNN	YOLOR
Date of Release		May 10, 2021
Model Type	Object Detection	Object Detection
Architecture		CNN, YOLO
GitHub Stars	7500	2000

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.

How to Augment How to Label How to Plot Predictions How to Filter Predictions How to Create a Confusion Matrix