🛰️ HSI-FastSpectralNet

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🧩 Project Structure

auto_modular_project/
├── data_loader.py      # Handles dataset loading and preprocessing
├── model.py            # Model definitions (CNN / Transformer / classifier)
├── train.py            # Training and evaluation routines
├── utils.py            # Helper utilities (metrics, visualization, etc.)
├── main.py             # Entry point — runs the entire pipeline
├── requirements.txt    # List of dependencies

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⚙️ Installation

Clone this repository and install dependencies:

git clone https://github.com/<your-username>/auto_modular_project.git
cd auto_modular_project
pip install -r requirements.txt

✅ Requires: Python 3.8 or later

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🚀 How to Run

Run the full workflow:

python main.py

This script will:

1. Download and load the Pavia University hyperspectral dataset
2. Perform data preprocessing and summarization
3. Optionally train a model (if training code is defined)
4. Visualize key results (spectral bands, class maps, etc.)

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🧠 Customize

You can extend or modify the pipeline easily:

• data_loader.py → change dataset source or preprocessing
• model.py → define your own architecture (CNN, ViT, etc.)
• train.py → modify training logic, optimizer, or evaluation metrics
• utils.py → add custom metrics or helper functions

Example:

from data_loader import load_pavia_data
from model import MyCustomModel

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📊 Dataset

Dataset used: Pavia University Hyperspectral Image

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🧾 License

MIT License © 2025 Jayant Biradar

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🌟 Acknowledgements

Developed by Jayant Biradar
Converted and modularized using AI-assisted notebook parsing.