Models
YOLOv4 Tiny vs. YOLOR

YOLOv4 Tiny vs. YOLOR

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

Models

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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
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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
Model Type
Object Detection
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Object Detection
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Model Features
Item 1 Info
Item 2 Info
Architecture
ResNet-D, YOLO
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CNN, YOLO
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Frameworks
Darknet
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PyTorch
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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
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2k+
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License
YOLO
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GPL-3.0
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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 YOLOv4 Tiny and YOLOR with Autodistill

Compare YOLOv4 Tiny 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.