fahimfaisal0404/Crop-Disease-in-Uganda

Project 02: Crop-Disease-in-Uganda by WorldQuant University Applied AI course In this project we'll be working with a dataset of crop disease images from Uganda. We'll build and train a convolutional neural network to classify images into five categories. In the project we'll learn how to improve the performance of a computer vision model by using pre-trained models and by optimizing training with techniques like Callbacks. Objectives are: Explore a crop image dataset. Build and training a convolutional neural network to classify images into five classes. Improve the model by using Transfer Learning and adapting a pre-trained image classification model. Indentify model overfitting. Evaluate model performance using k-fold cross-validation. Utilize Callbacks like Learning Rate Scheduling, Checkpointing, and Early Stopping to optimize training.