This project leverages deep learning for flower image classification using the pre-trained MobileNetV2 model. By fine-tuning this efficient Convolutional Neural Network (CNN), the model is capable of accurately identifying different flower species from images. MobileNetV2’s lightweight architecture ensures fast and efficient performance, making it suitable for deployment on resource-limited devices. The model is trained and evaluated using a dataset of flower images, providing real-time, high-accuracy classification for flower recognition tasks.