MobileNetV2-Project

MobileNetV2 Strawberry Disease Detection

This project applies MobileNetV2 for detecting strawberry diseases using a deep learning model. The original code has been modularized for professional deployment, including Dockerization.

Project Structure

• models/ contains the MobileNetV2 model code.
• scripts/ contains training, evaluation, and utility scripts.
• notebooks/ contains the original Jupyter notebook.
• main.py runs the primary functionality for making predictions.
• Dockerfile and .dockerignore are used for containerization.

Getting Started

Prerequisites

• Docker
• Python 3.9

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/MobileNetV2-Project.git
   cd MobileNetV2-Project

2. Install dependencies:

   pip install -r requirements.txt

3. Build and run the Docker container:

   docker build -t mobilenetv2-project .
   docker run -p 8080:8080 mobilenetv2-project

Usage

Training the Model

To train the model on your dataset, use the following command:

python scripts/train.py --data data/dataset_path --epochs 300 --batch_size 16 --learning_rate 0.001

• --data: Path to the training dataset.
• --epochs: Number of training epochs (default is 300).
• --batch_size: Batch size for training (default is 16).
• --learning_rate: Learning rate for the optimizer (default is 0.001).

Evaluating the Model

To evaluate the trained model on a test dataset, use the following command:

python scripts/evaluate.py --data data/test_dataset --model models/mobilenetv2.pth

• --data: Path to the evaluation dataset.
• --model: Path to the trained model file (mobilenetv2.pth).

This script will print a classification report, display a confusion matrix, and calculate accuracy to help assess the model's performance.

Making Predictions

To use the trained model to make predictions on a new image:

python main.py --image path/to/sample_image.jpg

• --image: Path to the image for which you want to make a prediction.

Project Components

Dockerfile

The Dockerfile is used to create a Docker container for the project. It sets up the Python environment, installs dependencies, and runs main.py.

.dockerignore and .gitignore

• .dockerignore: Specifies which files and directories should be ignored when building the Docker image (e.g., cache files, notebook checkpoints).
• .gitignore: Specifies which files should be ignored by Git (e.g., compiled Python files, environment directories).

Evaluation Metrics

The evaluation script (evaluate.py) provides the following metrics:

• Accuracy: The ratio of correctly predicted samples to the total samples.
• Classification Report: Precision, recall, F1-score, and support for each class.
• Confusion Matrix: A visualization of how well the model is classifying each class, helping identify misclassifications.