social-merit/fruitandvegetableclassificationapp

Fruit and Vegetable Classification

What was done

• 1. Loading and preprocessing

• 2. Load the Images with a generator and Data Augmentation

• 3. Train the model

• 4. Visualize the result

• 5. Class activation heatmap for image classification

• 6. Deploy the model saved model with Streamlit App

This dataset contains images of the following food items:

• fruits: banana, apple, pear, grapes, orange, kiwi, watermelon, pomegranate, pineapple, mango

• vegetables: cucumber, carrot, capsicum, onion, potato, lemon, tomato, raddish, beetroot, cabbage, lettuce, spinach, soy bean, cauliflower, bell pepper, chilli pepper, turnip, corn, sweetcorn, sweet potato, paprika, jalepeño, ginger, garlic, peas, eggplant

• Labels: {0: 'apple', 1: 'banana', 2: 'beetroot', 3: 'bell pepper', 4: 'cabbage', 5: 'capsicum', 6: 'carrot', 7: 'cauliflower', 8: 'chilli pepper', 9: 'corn', 10: 'cucumber', 11: 'eggplant', 12: 'garlic', 13: 'ginger', 14: 'grapes', 15: 'jalepeno', 16: 'kiwi', 17: 'lemon', 18: 'lettuce', 19: 'mango', 20: 'onion', 21: 'orange', 22: 'paprika', 23: 'pear', 24: 'peas', 25: 'pineapple', 26: 'pomegranate', 27: 'potato', 28: 'raddish', 29: 'soy beans', 30: 'spinach', 31: 'sweetcorn', 32: 'sweetpotato', 33: 'tomato', 34: 'turnip', 35: 'watermelon'}

This dataset is from Kaggle and it contains three folders:

• train (100 images each)
• test (10 images each)
• validation (10 images each) each of the above folders contains subfolders for different fruits and vegetables wherein the images for respective food items are present