GitHub Repository: Fruit and Vegetable Classification

Description:
Fruit and Vegetable Classification is a machine learning-based project that classifies fruits and vegetables using image recognition. It demonstrates the use of convolutional neural networks (CNNs) for accurate identification and categorization. Designed as a practical implementation of deep learning techniques, the project provides an engaging exploration of AI in the food industry.

Key Features:

• Image Classification: Recognizes and classifies a variety of fruits and vegetables based on input images.

• Deep Learning Model: Utilizes a CNN architecture to achieve high classification accuracy.

• Preprocessed Dataset: Implements data augmentation and preprocessing for better model performance.

• User-Friendly Interface: Easy-to-follow scripts and commands to run the classification system.

• Customizable Pipeline: Modular codebase allows users to tweak and retrain the model on new datasets.
  

Technologies Used:

• Programming Language: Python

• Libraries: TensorFlow, Keras, NumPy, Pandas, OpenCV

• Dataset: Publicly available fruit and vegetable image datasets (e.g., from Kaggle or custom datasets)
  

Challenges Solved:

• Improved model accuracy using data augmentation techniques like rotation, flipping, and cropping.

• Optimized the training process to handle imbalanced datasets.

• Streamlined image preprocessing for better classification results.

Installation Steps

1. Clone the Repository:
   

   git clone https://github.com/fanik041/Food_detection_Streamlit_project?tab=readme-ov-file

   cd Fruit_Veg_Classification

2. Set Up Virtual Environment (Optional):
   Create and activate a virtual environment to isolate dependencies:
   

   python -m venv env source env/bin/activate # Linux/Mac env\Scripts\activate # Windows
   

3. Install Dependencies:
   Install the required Python libraries using pip:

   pip install -r requirements.txt
   

4. Download the Dataset:
   Ensure the dataset is placed in the appropriate folder as defined in the project (e.g., data/). Use the default dataset or specify your own dataset path in the scripts.

5. Run the Training Script (Optional):
   Train the model with the dataset:

   python train_model.py

6. Run the Classification Script:
   Use the provided script to classify images:

   python classify.py --image <path_to_image>

7. Model Testing and Evaluation:
   Evaluate the model's performance using the test dataset:

   python evaluate_model.py

Note: Ensure all dependencies, including TensorFlow and Keras, are correctly installed and compatible with your system's Python version.

Fruit and Vegetable Classification