My Capstone 1 Project at MLZoomcamp: Bird Species Classification with Deep Learning

 Classifying Bird Species: A Deep Learning Approach to Image Classification


Bird species classification can contribute to various ecological and environmental studies, helping researchers identify patterns and protect endangered species. For my Capstone 1 project at MLZoomcamp led by Alexey Grigorev @DataTalks.Club, I took on the challenge of classifying bird species from a dataset of 25 Indian bird species, leveraging deep learning techniques for image classification. Here’s a breakdown of how I approached this problem:


The Challenge

The dataset, sourced from Kaggle, consists of over 22,600 images of 25 different bird species. The key challenges for this project included:

  • Large Dataset: With more than 22,600 images, managing such a large dataset requires efficient    preprocessing and handling techniques.
  • High Image Variability: Different lighting conditions, angles, variations in bird image backgrounds, poses and image resolutions made it difficult for a model to generalize well.
  • Computational Complexity: With a dataset containing over 22,600 images, training a deep learning model from scratch was computationally expensive


The Solution

I decided to implement a Transfer Learning approach using pre-trained models, which allows for better performance with limited data. I chose Xception, a deep convolutional neural network (CNN), as the backbone for my image classification task.

Key Steps Taken

1. Data Preprocessing: I used tf.keras.preprocessing.image_dataset_from_directory to load the images, ensuring they were resized, normalized, and ready for the model.

2. Transfer Learning: Leveraging the pre-trained Xception model, I fine-tuned it by adding custom layers to adapt it to my specific problem. This allowed the model to benefit from the learned features in previous tasks while tailoring it to recognize the bird species.

3.Hyperparameter Tuning: Through careful experimentation, I fine-tuned the following:
  • Learning Rate: Adjusted the learning rate to find the optimal step size for training.
  • Dropout Rate: Implemented dropout layers to reduce overfitting and increase generalization.
  • Inner Layer Addition: Added an extra dense layer with a carefully chosen input size to improve model complexity.
4. Model Evaluation: I used standard metrics such as train accuracy, train loss, validation accuracy and validation loss to evaluate the model’s performance across all 25 classes.



Key Insights

  1. Transfer Learning Power: By leveraging the knowledge embedded in the pre-trained model, I was able to achieve better results than training from scratch.
  2. Hyperparameter Tuning’s Impact: Adjusting the learning rate and dropout rate significantly boosted the model’s performance, striking a balance between bias and variance.
  3. Balancing Model Complexity: Adding an extra inner layer helped capture more complex features, which enhanced the model’s ability to differentiate between the bird species.


Practical Utility

This image classification model could be valuable for a variety of applications:

  • Wildlife Research: It could assist in identifying bird species in field surveys, making the process quicker and more accurate.
  • Educational Tools: The model could be used in educational apps or websites, helping users identify birds based on images.
  • Environmental Monitoring: By classifying bird species in images, this model could aid in biodiversity conservation efforts.


This project was a perfect blend of deep learning, transfer learning, and model optimization, showcasing how machine learning can be applied to real-world problems in image classification. By fine-tuning the model’s hyperparameters and leveraging the power of pre-trained models, I was able to achieve impressive results. You can find the code and methodology on my GitHub repository.

Thanks for reading about my journey through the Capstone 1 project at MLZoomcamp!

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