This contest challenges participants to develop a model that detects and classifies various types of fruits in images. The task involves training a model to accurately identify fruits by analyzing visual features such as shape, color, and texture. Participants will be provided with a dataset of 8,479 labeled images, featuring 6 different fruits: Apple, Banana, Grapes, Orange, Pineapple and Watermelon. The goal is to achieve high detection accuracy using techniques such as deep learning, convolutional neural networks (CNNs), feature extraction, and data augmentation. The model's performance will be evaluated based on its ability to correctly classify fruits in diverse and unseen images, ensuring robust generalization across different conditions.