Models
MobileNet V2 Classification vs. OpenAI CLIP

MobileNet V2 Classification vs. OpenAI CLIP

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

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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OpenAI CLIP

CLIP (Contrastive Language-Image Pre-Training) is an impressive multimodal zero-shot image classifier that achieves impressive results in a wide range of domains with no fine-tuning. It applies the recent advancements in large-scale transformers like GPT-3 to the vision arena.
Model Type
Classification
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Classification
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Model Features
Item 1 Info
Item 2 Info
Architecture
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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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21.4k+
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License
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MIT
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Training Notebook

Compare MobileNet V2 Classification and OpenAI CLIP with Autodistill

Compare MobileNet V2 Classification vs. OpenAI CLIP

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.