Fruits and Vegetables Image Classification

This project focuses on classifying images of fruits and vegetables using a Convolutional Neural Network (CNN) implemented in TensorFlow and Keras. The dataset is organized into three parts: training, validation, and test sets, and images are classified into distinct categories such as different fruits and vegetables.

The model is trained using the training dataset, and performance is validated using the validation dataset. Results are visualized using accuracy and loss plots for both training and validation phases.

Model Architecture

The model is a Sequential CNN with the following layers:

1. Rescaling Layer: Normalizes pixel values to the range [0, 1].
2. Convolutional Layers: Three convolutional layers with ReLU activation and max-pooling:
   • Conv2D (16 filters, kernel size 3x3)
   • Conv2D (32 filters, kernel size 3x3)
   • Conv2D (64 filters, kernel size 3x3)
3. Flatten Layer: Converts the feature map into a 1D vector.
4. Dropout Layer: Prevents overfitting by randomly setting 20% of the neurons to zero during training.
5. Dense Layers: Fully connected layers:
   • Dense (128 units)
   • Dense (number of categories)

Deploying the Web App

The trained model has been deployed as a web application using the Streamlit library. Users can upload an image of a fruit or vegetable, and the app will classify the image and display the predicted category along with the prediction Accuracy.

Features of the Web App:

• Upload an image directly (JPG, JPEG, PNG).
• View the predicted fruit or vegetable category.
• See the Accuracy score of the prediction.

How to Run the Web App Locally

1. Clone the Repository

Download or clone the repository from GitHub:

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

2. Install Dependencies

Ensure you have Python 3.8+ installed. Install the required libraries using pip:

pip install tensorflow streamlit numpy matplotlib

3. Run the Web App

Run the Streamlit app using the following command:

streamlit run app.py

Sample Output

Here’s an example output after running the web app: