open_clip and CLIPA
The two tools are ecosystem siblings, as CLIPA builds upon OpenCLIP by investigating and providing an official implementation for an inverse scaling law observed during CLIP training, making OpenCLIP a foundational component for CLIPA's research and development.
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 CLIPA
UCSC-VLAA/CLIPA
[NeurIPS 2023] This repository includes the official implementation of our paper "An Inverse Scaling Law for CLIP Training"
This project offers a way to train advanced image and text recognition models, known as CLIP, much more efficiently and at a lower cost. It takes large datasets of images and their corresponding text descriptions as input, and outputs highly accurate CLIP models that can understand and connect visual and linguistic information. This is for machine learning researchers and practitioners who build and deploy AI models for tasks like image search or content moderation.
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