Models
YOLOv8 Instance Segmentation vs. Faster R-CNN

YOLOv8 Instance Segmentation vs. Faster R-CNN

Both YOLOv8 Instance Segmentation and Faster R-CNN are commonly used in computer vision projects. Below, we compare and contrast YOLOv8 Instance Segmentation and Faster R-CNN.

Models

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YOLOv8 Instance Segmentation

The state-of-the-art YOLOv8 model comes with support for instance segmentation tasks.
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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.
Model Type
Instance Segmentation
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Object Detection
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Model Features
Item 1 Info
Item 2 Info
Architecture
YOLO
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Frameworks
PyTorch
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TensorFlow 1.5
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
21.1k+
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7.5k+
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License
AGPL-3.0
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MIT
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Training Notebook

Compare YOLOv8 Instance Segmentation and Faster R-CNN with Autodistill

Compare YOLOv8 Instance Segmentation 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.