This repository contains the code and dataset for a machine learning project aimed at classifying images of fruits and vegetables. The models used for prediction are VGG16, ResNet50, and Xception.
The dataset contains images of various fruits and vegetables. Each category has its own folder under the train, test, and validation folders. Each of these folders contains 100, 10, and 10 images respectively.
- Banana
- Apple
- Pear
- Grapes
- Orange
- Kiwi
- Watermelon
- Pomegranate
- Pineapple
- Mango
- 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
Three different models were used to classify the images:
- VGG16
- ResNet50
- Xception
Each model was trained on the training set and evaluated on the validation set. The performance of the models was then tested on the test set.
Instructions on how to run the code and use the models for prediction are included in the respective model files.
Contributions are welcome. Please read the CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests.