Models
Faster R-CNN vs. YOLOR

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.

Models

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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.
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YOLOR

YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions.
Model Type
Object Detection
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Object Detection
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Model Features
Item 1 Info
Item 2 Info
Architecture
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CNN, YOLO
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Frameworks
TensorFlow 1.5
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
7.5k+
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2k+
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License
MIT
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GPL-3.0
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Training Notebook

Compare Faster R-CNN and YOLOR with Autodistill

Compare Faster R-CNN vs. YOLOR

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.