Models
YOLOR vs. YOLOv4 Tiny

YOLOR vs. YOLOv4 Tiny

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

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.
Learn more about YOLOR
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YOLOv4 Tiny

The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a feel for your dataset
Learn more about YOLOv4 Tiny
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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ResNet-D, YOLO
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Frameworks
PyTorch
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Darknet
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub
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View Repo
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View Repo
GitHub Stars
2k+
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License
GPL-3.0
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YOLO
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Paper
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View Paper
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View Paper
Training Notebook
Train on Colab
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Train on Colab
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Deploy Model
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Deploy with Roboflow
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Deploy with Roboflow
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Compare YOLOR and YOLOv4 Tiny with Autodistill

Compare YOLOR vs. YOLOv4 Tiny

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.