Models
YOLOS vs. OpenAI CLIP

YOLOS vs. OpenAI CLIP

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

Models

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YOLOS

YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP.
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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
Object Detection
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Classification
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Model Features
Item 1 Info
Item 2 Info
Architecture
Transformer, YOLO
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Frameworks
PyTorch
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
812+
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21.4k+
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License
MIT
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MIT
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Training Notebook

Compare YOLOS and OpenAI CLIP with Autodistill

Compare YOLOS 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.