YOLO-World vs. Faster R-CNN

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

Models

YOLO-World

YOLO-World is a zero-shot object detection model.

Learn more about YOLO-World

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

Frameworks

TensorFlow 1.5

Annotation Format

Instance Segmentation

GitHub

View Repo

GitHub Stars

2.9k+

7.5k+

License

GPL-3.0

MIT

Paper

View Paper

Training Notebook

Train on Colab

Deploy Model

Deploy with Roboflow

Compare YOLO-World 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.

YOLO-World vs. Faster R-CNN

Models

YOLO-World

Faster R-CNN

Compare YOLO-World and Faster R-CNN with Autodistill

Compare YOLO-World vs. Faster R-CNN