Bean Disease Classification
Stepwise CNN fine-tuning achieving 98.86% test accuracy — MSDS coursework research
| Research Paper | MSDS Coursework | 2025 |
“Improving Bean Disease Classification with Stepwise Fine-tuning of CNN-based Transfer Learning Model”
Overview
Benchmarked 5 pretrained CNN architectures on a combined bean leaf disease dataset and identified ResNet50 as the optimal backbone through systematic comparative evaluation.
Architectures Benchmarked
- VGG16
- ResNet50 (selected as optimal backbone)
- DenseNet121
- EfficientNet-B0
- MobileNetV2
Stepwise Fine-Tuning Strategy
Designed and implemented a stepwise fine-tuning strategy — progressively unfreezing layers to adapt pretrained ImageNet representations to the plant disease domain — achieving 98.86% test accuracy on the final model.
Pipeline
Conducted an end-to-end pipeline: data loading, preprocessing, multi-model benchmarking, fine-tuning, evaluation, and visualization in a single reproducible Kaggle notebook.
Tech Stack: Python · TensorFlow/Keras · Kaggle GPU · Jupyter