AI Pill Identifier: RAG & FastSAM with YOLOv8

This project employs cutting-edge artificial intelligence techniques, particularly in natural language processing and computer vision, to create a system that identifies pills from photographs and provides information about them. The process is broken into phases, beginning with image segmentation using FastSAM, a real-time solution based on YOLOv8-seg that efficiently generates segmentation masks. Visual similarity is then computed using ResNet-18, which excels in feature extraction to identify similarities between pill images. Text extraction from the pill surface is achieved using GPT-4 vision, and the entire system is accessible via a user-friendly Gradio interface. Advanced retrieval strategies, including Retrieval-Augmented Generation (RAG) and the Hybrid Search technique, are implemented using LlamaIndex to optimize the retrieval of pill data from a deep learning vector store, Deep Lake, ensuring precise and contextually relevant information is provided. The system's effectiveness is evaluated using metrics like hit rate and Mean Reciprocal Rank (MRR), demonstrating high performance in identifying and cross-referencing medical information, which is crucial for enhancing healthcare outcomes.

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