Cat-Vision

AI-Powered Image Insights

Cat-Vision is an AI image analysis tool that leverages self-supervised learning to transform the way you interact with visual data. This tool addresses critical challenges in existing AI frameworks, including model standardization, universal compatibility, specialized medical data processing, and cost barriers. The system aims to empower researchers to train multi-modal models without code experience.

At the foundation of Cat-Vision is DINO-MX, a flexible, modular framework developed to advance self-supervised learning for Vision Transformer architectures. With DINO-MX, vision models can be trained on large-scale unlabeled datasets by solving data-driven pretext tasks, allowing the model to learn generalizable visual representations that transfer well to downstream tasks. Whether the goal is pattern discovery, feature extraction, or large-scale image analysis, Cat-Vision supports a range of tasks including image classification, attention-driven region highlighting, and similarity search.

Models trained with Cat-Vision can easily work with tools like Hugging Face and be combined with language models to create powerful, multimodal systems that understand both images and text. While the self-service web version of this tool is still in development, the framework is complete and accessible. If you’re interested in leveraging this now, please reach out to us to discuss collaboration.

Key Features of Cat-Vision

Standardized models, datasets, and training setups make it easier to reproduce results and share work.
Built-in compatibility across different datasets and model architectures saves time and effort.
Advanced medical data augmentation techniques that improve model performance in clinical contexts.
Low-cost by using efficient training methods like parameter-efficient fine-tuning and LoRA.
Keeps data secure by running on UK-owned, NIST-compliant compute infrastrcuture.