This dataset contains images of three types of fruits: apple, orange, and banana. There is a separate folder for each fruit category, and different subfolders for fresh and rotten are provided to indicate the freshness state.

The dataset is divided into three parts: training, testing, and validation. The training partition contains labeled images for model training, while the testing partition is used to evaluate the model's performance on unseen data. The validation partition is used to fine-tune model hyperparameters and monitor training progress. Each image is labeled according to its fruit type and freshness state, enabling supervised learning tasks such as classification or object detection. The structure of the dataset facilitates robust model development and evaluation for tasks related to fruit quality assessment or classification.