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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Use this if you need to train high-performing image-text understanding models like CLIP, but are constrained by significant computing resources and high training costs.
Not ideal if you are looking for a pre-trained model to use directly without any further training or fine-tuning, or if you don't work with large-scale vision-language datasets.
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Jun 03, 2024
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