Models
OpenAI CLIP vs. MobileNet V2 Classification

OpenAI CLIP vs. MobileNet V2 Classification

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

Models

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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.
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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.
Model Type
Classification
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Classification
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Model Features
Item 1 Info
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Architecture
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Frameworks
PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
21.4k+
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License
MIT
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Training Notebook

Compare OpenAI CLIP and MobileNet V2 Classification with Autodistill

Compare OpenAI CLIP vs. MobileNet V2 Classification

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.