Successfully established a deep learning model which can detect and recognize the images of a wide variety of fruits and vegetables.
The 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.
The dataset 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.
- Keras
- Tensorflow
- Matplotlib
The images in this dataset were scraped from Bing Image Search website.
The main idea was to build an application which recognizes the food item(s) from a captured photo and gives its user distinct recipes that can be developed using the food item(s).