Bird Species Classifier

This project implements a machine learning model to classify bird species based on images. It uses a high-quality dataset of 20 bird species and is built with Python, TensorFlow, and supporting libraries.

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Features

• Classification of 20 bird species using a deep learning model.
• Preprocessing pipeline for resizing and normalizing images.
• Training and validation workflows with real-time monitoring of metrics.
• Functionality to predict bird species for custom images.

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Dataset

The dataset is sourced from Kaggle: BIRDS 20 SPECIES - IMAGE CLASSIFICATION https://www.kaggle.com/datasets/umairshahpirzada/birds-20-species-image-classification/data

• Training Images: 3208
• Validation Images: 100 (5 per species)
• Test Images: 100 (5 per species)
• Image Format: JPEG, 224x224x3
• Key Feature: Single bird in each image, occupying at least 50% of the pixels.

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Requirements

• Python 3.8+
• macOS (or any OS with compatible Python setup)
• Libraries:
  • numpy
  • pandas
  • tensorflow (>=2.12)
  • opencv-python
  • matplotlib

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Project Structure

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├── bird_classifier.ipynb     # Jupyter Notebook with full implementation
├── README.md                 # Project documentation
└── sample_images/            # Folder for testing your images

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Usage

Training the Model

1. Open the bird_classifier.ipynb notebook in Google Colab.
2. Upload the dataset.
3. Run the notebook cells to:
   • Preprocess the data.
   • Train the model.
   • Save the best-performing model.

Testing the Model

1. Load the saved model using the load_model function.
2. Use the function predict_species to classify new bird images:

predicted_species = predict_species('path_to_image.jpg', model, class_indices)
print(f"Predicted species: {predicted_species}")

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Example Prediction

Input image: sample_images/example_bird.jpg

Output:

Predicted species: Cardinal

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Results

• Achieved X% accuracy on the test dataset.
• Model generalizes well to unseen data.

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Future Improvements

• Implement advanced data augmentation.
• Test with more complex architectures like EfficientNet.
• Expand the dataset with more species and images.

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