Models
YOLOv3 PyTorch vs. YOLOv4 Tiny

YOLOv3 PyTorch vs. YOLOv4 Tiny

Both YOLOv3 PyTorch and YOLOv4 Tiny are commonly used in computer vision projects. Below, we compare and contrast YOLOv3 PyTorch and YOLOv4 Tiny.

Models

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YOLOv3 PyTorch

Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. PyTorch version.
Learn more about YOLOv3 PyTorch
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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
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
7.2k+
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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 YOLOv3 PyTorch and YOLOv4 Tiny with Autodistill

Compare YOLOv3 PyTorch 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.