Update README.md

README.md

@@ -11,5 +11,65 @@ tags:
 pipeline_tag: image-classification
 library_name: keras
 ---
-# Garbage Classification use CNN and Transfer Learning with MobileNetV2
-Model Used for transfer learning is MobileNetV2 to improve the model CNN already made
+# TrashNet-Classification
+
+This project focuses on classifying images of garbage into distinct categories using transfer learning with the MobileNetV2 architecture. The goal is to facilitate efficient waste segregation by accurately identifying types of trash, which is essential for effective recycling and waste management.
+
+## Overview
+
+The TrashNet-Classification project utilizes transfer learning with MobileNetV2 to classify images of garbage into categories such as cardboard, glass, paper, metal, and trash. By leveraging a pre-trained model, the project aims to achieve high accuracy with reduced training time and computational resources.
+
+## Project Structure
+
+- `dataset/`: Directory containing the data set used in model training.
+- `model/`: Directory containing the trained model.
+- `01_Preprocessing-Data.ipynb`: Notebook for data preprocessing, including dataset download, extraction, and data augmentation.
+- `02_Train-Model.ipynb`: Notebook for training the MobileNetV2 model with the preprocessed data.
+- `03_Quantization_model.ipynb`: Notebook for model quantization to optimize performance.
+- `requirements.txt`: List of Python dependencies required to run this project.
+
+## Requirements
+
+Ensure you have Python 3.x installed and install the dependencies listed in `requirements.txt`:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+- **Data Preprocessing**: Run the `01_Preprocessing-Data.ipynb` notebook to download and prepare the dataset. This notebook performs data augmentation to enhance model performance.
+- **Model Training**: Once the data is prepared, execute `02_Train-Model.ipynb` to train the MobileNetV2 model with the processed data. This notebook includes steps for training and evaluating the model.
+- **Model Quantization**: To optimize the model, run `03_Quantization_model.ipynb`. This step is optional but recommended for improving model efficiency.
+
+## Dataset
+
+The dataset used is [TrashNet](https://huggingface.co/datasets/garythung/trashnet), which consists of images of trash in six categories: cardboard, glass, metal, paper, plastic, and trash. This dataset is widely used in research for deep learning-based trash classification. 
+
+## Installation
+
+To set up the project environment, follow these steps:
+
+1. Clone the repository:
+
+```bash
+git clone https://github.com/ahmzakif/TrashNet-Classification.git
+cd TrashNet-Classification
+```
+
+2. Create and activate a virtual environment:
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Contributing
+
+Contributions are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request. Please ensure that your contributions align with the project's objectives and adhere to the coding standards.