Snake Venom Classification using Transfer Learning with MobileNetV2

Overview

This code implements a snake venom classification model using transfer learning with the MobileNetV2 architecture. The model is trained on a dataset of snake images categorized into venomous and non-venomous classes. It evaluates the model's performance, predicts the class for a sample image, and provides a confusion matrix and classification report.

Prerequisites

• Python 3.x
• TensorFlow 2.x
• Matplotlib
• Seaborn
• Scikit-learn

Installation

Ensure you have the required dependencies installed using:

pip install tensorflow matplotlib seaborn scikit-learn

Usage

1. Download the snake venom dataset and organize it into train and test directories.

2. Update the train_dir and test_dir variables with the correct paths to your train and test datasets.

3. Optionally, set the image_path variable to the path of a specific image for prediction.

4. Run the script to train the model, evaluate its performance, and make predictions.

python snake_venom_classification.py

Code Structure

• Dataset Loading:

  • The dataset is loaded and preprocessed using image augmentation for training.

• Model Creation:

  • MobileNetV2 is employed as the base model with additional custom top layers for classification.
  • The model is compiled with categorical cross-entropy loss and the Adam optimizer.

• Training:

  • The model is trained for a specified number of epochs.

• Evaluation:

  • The model's accuracy is evaluated on the test set, and an accuracy graph is plotted.

• Prediction:

  • A sample image is loaded, preprocessed, and the model predicts its class.

• Confusion Matrix:

  • A confusion matrix and classification report are generated for evaluating model performance.

Hyperparameters

• img_width and img_height: Input image dimensions (224x224).
• batch_size: Batch size for training and testing (32).
• num_classes: Number of snake venom classes (2 - venomous and non-venomous).
• epochs: Number of training epochs (10).

Customization

• Adjust the hyperparameters to suit your specific dataset and computing resources.
• Modify the model architecture, learning rate, or image augmentation parameters as needed.

License

This code is licensed under the MIT License.

Feel free to customize and use this code for your snake venom classification tasks. If you find it helpful, consider providing attribution to the original source.