Models
MobileNet V2 Classification vs. YOLOR

MobileNet V2 Classification vs. YOLOR

Both MobileNet V2 Classification and YOLOR are commonly used in computer vision projects. Below, we compare and contrast MobileNet V2 Classification and YOLOR.

Models

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MobileNet V2 Classification

MobileNet is a GoogleAI model well-suited for on-device, real-time classification (distinct from MobileNetSSD, Single Shot Detector). This implementation leverages transfer learning from ImageNet to your dataset.
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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.
Model Type
Classification
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Object Detection
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Model Features
Item 1 Info
Item 2 Info
Architecture
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CNN, YOLO
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Frameworks
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
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2k+
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License
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GPL-3.0
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Training Notebook

Compare MobileNet V2 Classification and YOLOR with Autodistill

Compare MobileNet V2 Classification 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.