I Turned 149k Messy Images into an Offline Recognition System

A developer shares their journey of building an offline food recognition system capable of identifying, counting, and assessing the quality of food items on mobile devices. Starting with a massive, inconsistent dataset of 149,000 images, the project required rigorous data cleaning, including duplicate removal, blur detection, and label standardization. By using YOLOv8n, the developer successfully trained a model to function without cloud connectivity. The process involved a pilot phase to refine the pipeline, followed by a full training run on approximately 20,000 cleaned images. Key technical challenges included managing GPU memory, handling varied labeling conventions across datasets, and optimizing the model for edge deployment. The author emphasizes the importance of data quality over quantity, noting that systematic cleaning and preprocessing were more critical to the model's success than the training architecture itself, ultimately providing a practical guide for developers working with multi-source computer vision datasets.