This project was initiated with the objective of developing a neural network capable of accurately detecting flower types from user-provided images. The primary aim was to create a reliable and efficient system that enables seamless flower type identification through image inputs.

Build and optimize a neural network architecture to efficiently process and classify flower images.

Implement mechanisms for accepting user-provided images, enabling real-time flower type detection.

Focus on enhancing the accuracy and efficiency of the neural network model through rigorous optimization techniques.

Design an intuitive user interface for easy interaction, making the flower type detection system accessible to a wide audience.

By achieving the project goals, we aim to provide a valuable tool for flower enthusiasts, researchers, and enthusiasts alike. The developed neural network system will empower users to effortlessly identify various flower types, fostering learning and appreciation for floral diversity.