Models
YOLOR vs. YOLOv4 PyTorch

YOLOR vs. YOLOv4 PyTorch

Both YOLOR and YOLOv4 PyTorch are commonly used in computer vision projects. Below, we compare and contrast YOLOR and YOLOv4 PyTorch.

Models

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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.
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YOLOv4 PyTorch

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in PyTorch.
Model Type
Object Detection
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Object Detection
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Model Features
Item 1 Info
Item 2 Info
Architecture
CNN, YOLO
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YOLO
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Frameworks
PyTorch
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
2k+
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4.4k+
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License
GPL-3.0
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Apache-2.0
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Training Notebook

Compare YOLOR and YOLOv4 PyTorch with Autodistill

Compare YOLOR vs. YOLOv4 PyTorch

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.