Models
Scaled-YOLOv4 vs. OpenAI CLIP

Scaled-YOLOv4 vs. OpenAI CLIP

Both Scaled YOLOv4 and OpenAI CLIP are commonly used in computer vision projects. Below, we compare and contrast Scaled YOLOv4 and OpenAI CLIP.

Models

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Scaled YOLOv4

Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.
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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
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Item 2 Info
Architecture
YOLO
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Frameworks
PyTorch
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
2k+
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21.4k+
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License
GPL-3.0
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MIT
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Training Notebook

Compare Scaled YOLOv4 and OpenAI CLIP with Autodistill

Compare Scaled-YOLOv4 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.