ouomarabdessamade/Image-Classification-for-the-Most-Recognized-Date-Varieties-in-the-Dr-a-Tafilalet-Region-of-Morocco

# Image Classification for the Most Recognized Date Varieties 🌴 Welcome to our Image Classification Project focusing on the most recognized date varieties in the Drâa-Tafilalet Region of Morocco! 🌴 In this project, we leverage Convolutional Neural Networks (CNN) for the classification of images representing different types of date varieties. ## Dataset Our dataset comprises 897 high-quality JPG images, each depicting a unique type of date among the 5 available types (Mejhoul, Rutab, Soukari, Nabtat Ali, and Ajwa). **Source:** Date Fruit Image Dataset in Controlled Environment ## Model Architecture For this classification task, we have designed a powerful Sequential Architecture for our CNN model. ## Data Augmentation using DCGAN Witness the magic of Data Augmentation through the lens of Deep Convolutional Generative Adversarial Networks (DCGAN). For an in-depth exploration, delve into the notebook: Data_Augmentation_Using_DCGAN.ipynb. ## Training Insights Discover the journey of our model through the captivating visualizations of loss functions during training and testing. ## Model Performance Behold the accuracy of our model as it reaches new heights in recognizing date varieties. ## Confusion Matrix Unveil the intricacies of classification with our Confusion Matrix. ## Dive In! Explore the intricacies of our image classification journey in the accompanying notebooks. Your curiosity and feedback are highly valued! 🌴 Happy classifying the diverse world of date varieties! 🌴