Welcome to the Fruit Detector project! This repository contains a machine learning model that can identify different fruits from images. 🥭🍉

Here's an overview of the project directory:

C:\Users\Arman\Desktop\ML\Projects\Fruit-Detector
│
├── Test_File/
│   └── [Test images...]
├── Train_File/
│   └── [Training images...]
│
├── Fruit_Detector.py
├── fruit_detector_model.h5
├── label_encoder.npy
└── predict_fruit.py

Make sure you have the following installed:

• Python 3.x
• Necessary Python packages (use pip install -r requirements.txt)

Clone the repository:

git clone https://github.com/your-username/fruit-detector.git
cd fruit-detector

To train the model, run the Fruit_Detector.py script:

python Fruit_Detector.py

This will load images from the Train_File directory, train the model, and save it as fruit_detector_model.h5. It also saves the label encoder as label_encoder.npy.

To predict the fruit in an image, use the predict_fruit.py script:

python predict_fruit.py <path_to_image>

Replace <path_to_image> with the path to your image file. For example:

python predict_fruit.py ./Test_File/apple_01.jpg

This script handles the entire pipeline from loading data, training the model, and saving the model and label encoder. Key steps include:

• Loading Images: Reads images and their labels from the Train_File and Test_File directories.
• Preprocessing: Normalizes the images and encodes the labels.
• Model Building: Constructs a Convolutional Neural Network (CNN) using Keras.
• Training: Trains the model on the training data and evaluates it on the test data.
• Saving: Saves the trained model and label encoder for later use.

This script loads a saved model and label encoder to make predictions on new images. Key steps include:

• Loading Model: Loads the trained model and label encoder.
• Image Preprocessing: Prepares the image for prediction by resizing and normalizing.
• Prediction: Predicts the class of the fruit and prints the result with confidence score.

After training, the model achieved an accuracy of 97.27% on the test data.

• Expand the dataset with more fruit images.
• Improve the model architecture for better accuracy.
• Implement data augmentation to enhance model robustness.

Contributions are welcome! Feel free to open issues or submit pull requests.

For questions or comments, please reach out to kianianarman1@gmail.com.

Happy Coding! 🎉