Models
YOLOv4 PyTorch vs. OpenAI CLIP

YOLOv4 PyTorch vs. OpenAI CLIP

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

Models

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YOLOv4 PyTorch

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in PyTorch.
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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
YOLO
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Frameworks
PyTorch
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
4.4k+
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21.4k+
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License
Apache-2.0
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MIT
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Training Notebook

Compare YOLOv4 PyTorch and OpenAI CLIP with Autodistill

Compare YOLOv4 PyTorch 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.