Bangladeshi Flower Image Classification

Deep learning–powered web app for classifying 13 types of Bangladeshi flowers.
Transfer learning with MobileNetV2 (fine-tuned last 50 layers) on a custom curated dataset.
Advanced data augmentation pipeline (rotation, shift, shear, zoom, flip) to improve model robustness.
Achieved 99.87% validation accuracy with real-time classification and confidence scores via a Streamlit app.

Tools / Technology: Python, TensorFlow/Keras, Streamlit, OpenCV, PIL, NumPy, Pandas