Snake Classification (venomous/ non-venomous) 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.