Models
OpenAI CLIP vs. YOLOR

OpenAI CLIP vs. YOLOR

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

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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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
PyTorch
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
21.4k+
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2k+
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License
MIT
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GPL-3.0
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Training Notebook

Compare OpenAI CLIP and YOLOR with Autodistill

Compare OpenAI CLIP 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.