Project Overview

Project Repository in Github

Participaed in a project with Daewoong Foundation to develop a deep learning system for recognizing skin diseases in pets, aiming to assist veterinarians in diagnosis.

Key Contributions

  • Implemented dataset parser for complex data structures
  • Used U-Net based image segmentation model using PyTorch
  • Collaborated on pipeline combining object detection and classification

Technical Highlights

  1. Dataset Handling:

    • Parsed and cleaned ~55,000 images with 6 lesion types
    • Converted annotations to YOLO format

  2. U-Net Segmentation Model:

    • Addressed challenges of small object segmentation (93% of lesions <5% of image area)

  3. Integrated Pipeline:

    • Combined segmentation, object detection, and classification
    • Implemented post-processing for improved accuracy

Challenges Overcome

  • Inconsistent image quality and small object sizes
  • Complex dataset structure with missing data
  • Model overfitting in semantic segmentation

Tools Used

Python, PyTorch, Jupyter Notebooks, Git/GitHub