Full Description

This project focuses on classifying North American bird species using a robust deep learning approach. By leveraging VGG16, a pre-trained convolutional neural network, as a transfer learning model, the study seeks to achieve high accuracy in identifying bird species from image data.

The dataset includes labeled images of North American bird species, sourced from publicly available repositories such as iNaturalist or specialized bird image datasets curated for machine learning tasks. These datasets provide a diverse range of bird classes, ensuring robust training and testing conditions. Preprocessing steps involve resizing, normalization, and data augmentation to improve model generalization.

Bird species classification is crucial for ecological studies, conservation efforts, and biodiversity tracking. This project aims to simplify and enhance the process by using advanced machine learning techniques, building on the strengths of VGG16’s feature extraction capabilities while fine-tuning the model for the specific task of bird species prediction.

The findings from this project may benefit ornithologists, ecologists, and machine learning practitioners aiming to apply deep learning in environmental and conservation contexts.

This project utilizes two Jupyter notebooks to achieve the goal of accurately predicting North American bird species through transfer learning with the VGG16 model. Each notebook is designed to tackle distinct parts of the workflow, from data preprocessing to training and evaluation. The 2 notebooks are linked below:

Notebook 1: Data Preparation and Preprocessing

The first notebook focuses on preparing the dataset for training the model. Its main objectives and procedures include:

1. Data Acquisition:

   • The dataset is sourced from publicly available repositories such as iNaturalist or other curated bird image collections.
   • It includes labeled images of various North American bird species, ensuring diversity across different classes.

2. Data Exploration:

   • Provides a visual overview of the dataset, including class distributions and representative samples for each bird species.

3. Preprocessing:

   • Images are resized to fit the input dimensions required by VGG16 (typically 224x224 pixels).
   • Normalization is applied to scale pixel values to the [0,1] range.
   • Data augmentation techniques such as rotation, flipping, and zooming are employed to increase dataset variability and improve generalization during training.

4. Dataset Splitting:

   • The dataset is split into training, validation, and testing sets to ensure robust model evaluation.
   • The splits are stratified to maintain class balance across subsets.

5. Output:

   • A preprocessed and augmented dataset ready for use in the training pipeline.
   • Visualizations of augmented samples to verify preprocessing steps.

NA Birds Data Pre-processing Notebook

Notebook 2: Model Training, Fine-Tuning, and Evaluation

The second notebook builds on the prepared dataset to train and evaluate the VGG16-based model. Its main objectives and procedures include:

1. Transfer Learning Setup:

   • The pre-trained VGG16 model is loaded, with its convolutional base used as a feature extractor.
   • The top layers of the model are replaced with a custom classifier tailored to the number of bird species in the dataset.

2. Fine-Tuning:

   • Selected layers in the VGG16 base are unfrozen to allow fine-tuning, enabling the model to adapt to the specific bird classification task.
   • Learning rates are carefully adjusted to prevent overfitting or undertraining.

3. Model Training:

   • The model is trained on the prepared dataset using a combination of loss functions (e.g., categorical cross-entropy) and optimizers (e.g., Adam or SGD with momentum).
   • Training is monitored using metrics such as accuracy and loss on both the training and validation sets.

4. Evaluation:

   • The trained model is tested on the reserved test set to evaluate its accuracy, precision, recall, and F1 score.
   • Confusion matrices and classification reports are generated to analyze performance across different bird species.

5. Predictions:

   • The model's predictions are demonstrated on unseen bird images, showcasing its ability to generalize.

6. Model Saving:

   • The final trained model is saved in a format suitable for deployment (e.g., HDF5 or SavedModel).

NA Birds AI Training & Evaluation Notebook

Expected Outcomes

• Notebook 1 delivers a clean and well-augmented dataset, primed for high-performance training.
• Notebook 2 produces a fine-tuned VGG16 model capable of accurately classifying North American bird species.
• The workflow establishes a reproducible pipeline that can be extended to other image classification tasks or datasets.

This structured approach ensures a seamless transition from raw data to actionable predictions, demonstrating the power of transfer learning in ecological research and conservation efforts.