QUICKSTART.md

Quick Start Guide

This guide will help you get started with LineamentLearning in 5 minutes.

Installation

# Clone repository
git clone https://github.com/RichardScottOZ/LineamentLearning.git
cd LineamentLearning

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -e .

Your First Training

1. Prepare Your Data

Your data should be in MATLAB .mat format with the following structure:

• I1, I2, ..., I8: Input layers (8 geophysical datasets)
• output: Ground truth lineaments
• mask: Valid data region
• train_mask: Training region
• test_mask: Testing region
• DEGREES: Lineament orientations

2. Create Configuration

Create my_config.json:

{
    "model": {
        "architecture": "RotateNet",
        "window_size": 45,
        "batch_size": 32,
        "epochs": 50,
        "learning_rate": 0.001
    },
    "data": {
        "train_ratio": 0.7,
        "val_ratio": 0.15,
        "test_ratio": 0.15
    }
}

3. Train Model

lineament-train \
    --config my_config.json \
    --data ./Dataset/Australia/Rotations/Australia_strip.mat \
    --output ./my_first_model \
    --tensorboard

Monitor training with TensorBoard:

tensorboard --logdir ./my_first_model/logs

4. Run Prediction

lineament-predict \
    --model ./my_first_model/best_model.h5 \
    --data ./Dataset/test_data.mat \
    --output ./results \
    --visualize

Using Python API

from config import Config
from model_modern import build_model

# Create and configure model
config = Config()
config.model.architecture = 'UNet'
model = build_model(config)

# View model architecture
model.summary()

Trying Different Architectures

U-Net (Better for Spatial Context)

lineament-train \
    --architecture UNet \
    --window-size 64 \
    --data ./data/train.mat \
    --output ./models/unet

ResNet (Deeper Network)

lineament-train \
    --architecture ResNet \
    --window-size 64 \
    --epochs 100 \
    --data ./data/train.mat \
    --output ./models/resnet

Common Issues

Issue: Out of Memory

Solution: Reduce batch size

lineament-train --batch-size 16 ...

Issue: Slow Training

Solutions:

1. Enable GPU: --gpu 0
2. Use mixed precision: Add to config: "use_mixed_precision": true
3. Reduce window size: --window-size 32

Issue: Model Not Learning

Solutions:

1. Check data quality
2. Adjust learning rate: --learning-rate 0.0001
3. Increase epochs: --epochs 200
4. Try different architecture: --architecture UNet

Next Steps

1. Read Full Documentation: See README.md
2. Explore Examples: Check examples/ directory
3. Customize Model: Edit model_modern.py
4. Optimize Hyperparameters: Experiment with config
5. Visualize Results: Use TensorBoard and visualization tools

Tips for Best Results

1. Data Quality: Ensure clean, properly labeled data
2. Data Augmentation: Enable augmentation for small datasets
3. Early Stopping: Use early stopping to prevent overfitting
4. Model Selection: Try multiple architectures
5. Hyperparameter Tuning: Experiment with learning rates and batch sizes
6. Ensemble Methods: Combine multiple models for better results

Getting Help

• Documentation: README.md, CHANGELOG.md
• Issues: https://github.com/RichardScottOZ/LineamentLearning/issues
• Original Thesis: http://hdl.handle.net/2429/68438