ibrahimdaud/raw-food-recognition

该数据集是三个公开可用的食物识别数据集的综合汇编，经过合并和整理，用于原始食物识别任务。数据集包含各种原始食品项目的图像，包括水果、蔬菜、乳制品和饮料，旨在用于教育目的和图像识别模型的开发。数据集包含90多个食品类别和约15,000张图像，按照80/20的比例分为训练集和验证集。合并过程包括数据收集、标签标准化、去重、类别整合、分层分割和格式转换。数据集具有多样性，包含现实世界中的变化，并以教育为重点，以Parquet格式存储以提高效率。局限性包括类别不平衡、图像质量不一和标签变体。该数据集仅供教育用途，鼓励用户引用原始源数据集。

This dataset is a comprehensive compilation of three publicly available food recognition datasets, merged and curated for raw food recognition tasks. The dataset contains images of various raw food items including fruits, vegetables, dairy products, and beverages, intended for educational purposes and the development of image recognition models. The dataset contains 90+ food categories and ~15,000+ images, split into training and validation sets with an 80/20 ratio. The merging process involved data collection, label standardization, deduplication, class consolidation, stratified splitting, and format conversion. The dataset is diverse, with real-world variability and educational focus, stored in Parquet format for efficiency. Limitations include class imbalance, varying image quality, and label variants. This dataset is intended for educational use only, and users are encouraged to cite the original source datasets.