Models
YOLOv7 Instance Segmentation vs. Faster R-CNN

YOLOv7 Instance Segmentation vs. Faster R-CNN

Both YOLOv7 Instance Segmentation and Faster R-CNN are commonly used in computer vision projects. Below, we compare and contrast YOLOv7 Instance Segmentation and Faster R-CNN.

Models

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YOLOv7 Instance Segmentation

YOLOv7 Instance Segmentation lets you perform segmentation tasks with the YOLOv7 model.
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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
12.5k+
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7.5k+
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License
GPL-3.0
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MIT
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Training Notebook

Compare YOLOv7 Instance Segmentation and Faster R-CNN with Autodistill

Compare YOLOv7 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.