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Nyun Adapt

Adapters-for-All: Nyun Zero enables building high-performant AI models through cost-effective fine-tuning

Background

With the growing size of AI models, full fine-tuning of pre-trained models has become increasingly expensive and infeasible. Multiple efficient fine-tuning methods have been proposed in the literature like LoRA which substantially bring down the cost of fine-tuning large models. Recent research has shown that not just large models but even smaller AI models like Resnets, Vision transformers, Yolos have better performance when fine-tuned using efficient fine-tuning methods. With this motivation, Nyun Zero has a brand new plugin that can help users save massive costs in fine-tuning any AI model while surpassing full-finetuning performance - Nyun Adapt!

No more Trade-offs on image resolution! Nyun Zero lets build AI models at gigapixel scale.

Background

In the ever-evolving field of computer vision, deep learning models have established themselves as the cornerstone of advanced feature extraction, surpassing traditional algorithms. However, as technology pushes the boundaries of data acquisition, AI practitioners are faced with a growing challenge: how to train deep learning models effectively on very large images. Large images are everywhere now, be it the medical imaging domain or the remote sensing survey. Thus, a solution is needed where the large deep learning images can be processed seamlessly.