open_clip and clip_playground
The highly popular and downloaded open-source CLIP implementation (A) serves as a foundational library that the demonstrative and less-used notebooks (B) would likely utilize to showcase CLIP's zero-shot capabilities; thus, they are complements.
About open_clip
mlfoundations/open_clip
An open source implementation of CLIP.
This project provides pre-trained models that understand both images and text, allowing you to connect what you see with descriptive phrases. You can input an image and a list of text descriptions to get back probabilities of which description best matches the image. This is ideal for researchers or developers building applications that need to categorize images based on natural language or search for images using text.
About clip_playground
kevinzakka/clip_playground
An ever-growing playground of notebooks showcasing CLIP's impressive zero-shot capabilities
This project provides interactive examples for exploring how AI can understand images and text together, even for concepts it hasn't been explicitly trained on. You input images and text descriptions, and it shows you how well the AI can recognize objects or ideas within those images based on your descriptions. Researchers, data scientists, or AI enthusiasts can use this to quickly test and visualize cutting-edge computer vision techniques.
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