Vertex Technologies LLC
Leading AI solutions provider transforming businesses through innovation.
Leading AI solutions provider transforming businesses through innovation.
The Bird Species Image Classification Prototype Project is a deep learning-based system designed to identify bird species from images using the Caltech-UCSD Birds 200 dataset. The project leverages transfer learning with ResNet50 to extract complex visual features and classify images into 200 bird species categories. It provides a robust pipeline from data preprocessing to model training and evaluation, along with visual performance metrics and deployment-ready code.
Handling class imbalance across the 200 species, improving model generalization with limited dataset size, and optimizing hyperparameters for training time and accuracy posed the primary challenges. Efficiently training a deep model without overfitting also required careful use of regularization and data augmentation.
Used transfer learning with pretrained ResNet50 weights (ImageNet) and fine-tuned the model on bird species data. Hyperparameter tuning was done using HyperOpt to optimize performance. Applied data augmentation techniques like rotation, flipping, and zooming to expand the dataset virtually. Visualization techniques helped interpret model performance and validate results.
Successfully trained a high-accuracy image classification model for 200 bird species with 97% training accuracy. The project demonstrated practical use in ecological research, biodiversity studies, and bird species cataloging, with a ready-to-deploy script for real-time image classification tasks.
