Models
YOLOv3 PyTorch vs. MobileNet SSD v2

YOLOv3 PyTorch vs. MobileNet SSD v2

Both YOLOv3 PyTorch and MobileNet SSD v2 are commonly used in computer vision projects. Below, we compare and contrast YOLOv3 PyTorch and MobileNet SSD v2.

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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MobileNet SSD v2

This architecture provides good realtime results on limited compute. It's designed to run in realtime (30 frames per second) even on mobile devices.
Learn more about MobileNet SSD v2
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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Frameworks
PyTorch
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TensorFlow 1.5
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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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81+
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License
GPL-3.0
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MIT
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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 MobileNet SSD v2 with Autodistill

Compare YOLOv3 PyTorch vs. MobileNet SSD v2

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.