IAugmentor - Intelligent Augmentation

Overview

Worked on IAugmentor, a Python package for intelligent data augmentation with intraclass/interclass-aware strategies: implemented ResNet-based embedding extraction, K-means clustering for intraclass diversity analysis, and Anchored Distribution Interpolation (ADI) to balance datasets while preserving meaningful subgroup structure.

Technical Implementation

• Multi-level Augmentation Pipeline: Designed and deployed multi-level augmentation pipeline with concurrent transformation execution (threading), cluster-aware sampling for underrepresented subgroups, and comprehensive pre/post-augmentation analysis.

• Validation & Results: Validated IAugmentor on Oxford-102 Flowers and HAM10000 skin lesion datasets, achieving targeted class balance while maintaining intraclass diversity through intelligent cluster-based sampling, documented in automated comparison reports.

Technologies Used

• Python
• ResNet
• K-means Clustering
• Computer Vision
• Threading/Concurrency
• Data Augmentation