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Update training pipeline to HDF5, add data access scripts, and readme updates
- Updated training pipeline for HDF5-based dataset workflow - Implemented convert_tif_to_npy.py for TIF to NPY conversion with validation - Modified dataset classes and dataloaders to use HDF5 storage format - Fixed classifier code and model architectures for NPY array handling - Updated training scripts to work with new data format - Added user-friendly data access scripts - Created quickstart.py for dataset loading demonstration - Developed download_dataset.py for automated dataset download from Dropbox - Added get_dataloaders.py for simple PyTorch DataLoader generation - Updated README with clear usage instructions - Updated package configuration for UV compatibility - Updated pyproject.toml dependencies and structure - Added proper dependency groups (dev, processing, logging) - Added development infrastructure - GitHub Actions CI pipeline for automated testing - Pre-commit hooks with Black code formatting - Basic test suite with test_imports.py - Added baseline results: RGB 53.5%, HSI 27.3%, LiDAR 11.5%
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‎.github/workflows/ci.yml‎

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name: CI
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on:
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push:
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branches: [ main, develop ]
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pull_request:
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branches: [ main, develop ]
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jobs:
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test:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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os: [ubuntu-latest]
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python-version: ['3.9', '3.10', '3.11', '3.12']
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install uv
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run: pip install uv
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- name: Install dependencies
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run: |
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uv sync --all-extras
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- name: Test imports
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run: |
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uv run python -c "
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import neon_tree_classification
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from neon_tree_classification import NeonCrownDataset
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from scripts.get_dataloaders import get_dataloaders
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print('All imports successful!')
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"
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- name: Run tests
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run: uv run pytest tests/ -v --tb=short
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lint:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.11'
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- name: Install uv
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run: pip install uv
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- name: Install dependencies
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run: |
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uv sync --extra dev
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- name: Check code formatting
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run: uv run black --check neon_tree_classification/ examples/ scripts/ tests/

‎.gitignore‎

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.venv/
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uv.lock
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# Downloaded dataset files
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_neon_tree_classification_dataset_files/
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# IDE/Editor
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.vscode/
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.Spotlight-V100
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.Trashes
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ehthumbs.db
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Thumbs.db
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Thumbs.db

‎.pre-commit-config.yaml‎

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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v6.0.0
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hooks:
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- id: trailing-whitespace
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- id: end-of-file-fixer
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- id: check-yaml
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- id: check-added-large-files
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args: ['--maxkb=1000']
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- repo: https://github.com/psf/black
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rev: 25.1.0
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hooks:
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- id: black
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files: |
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(?x)^(
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neon_tree_classification/.*\.py|
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examples/.*\.py|
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scripts/.*\.py|
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tests/.*\.py
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)$

‎CONTRIBUTING.md‎

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# Contributing to NEON Tree Classification
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Thank you for your interest in contributing! We welcome contributions from researchers at all technical levels.
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## 🚀 Quick Start for Contributors
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### Option 1: UV Setup (Recommended)
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UV is faster and ensures consistent dependencies across all users.
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```bash
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# 1. Fork the repository on GitHub (click the Fork button)
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# 2. Clone your fork
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git clone https://github.com/yourusername/NeonTreeClassification.git
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cd NeonTreeClassification
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# 3. Install UV if you don't have it
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curl -LsSf https://astral.sh/uv/install.sh | sh
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# or: pip install uv
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# 4. Install the package
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uv sync
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```
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### Option 2: Pip Setup (Alternative)
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If you prefer traditional pip or can't install UV:
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```bash
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# After cloning (steps 1-2 above)
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pip install -e .
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```
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> **Note**: UV is preferred because it ensures all contributors have the same dependency versions using the `uv.lock` file.
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## 🤝 How to Contribute
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### 1. Found a Bug?
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- Open an [issue](https://github.com/Ritesh313/NeonTreeClassification/issues)
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- Describe what you expected vs what happened
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- Include any error messages
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### 2. Have a Feature Idea?
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- Open an [issue](https://github.com/Ritesh313/NeonTreeClassification/issues) to discuss it first
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- We can help figure out the best approach
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### 3. Want to Contribute Code?
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1. **Create a branch**: `git checkout -b your-feature-name`
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2. **Make changes**: Edit files, add features, fix bugs
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3. **Test it works**: `uv run python -c "import neon_tree_classification; print('Works!')"`
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4. **Submit**: Create a Pull Request on GitHub
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> **For advanced testing**: Use `uv sync --all-extras` to install development tools, then run `uv run pytest tests/`
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## 💡 Easy Ways to Help
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**No coding required:**
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- Report bugs or issues you encounter
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- Suggest improvements to documentation
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- Share example use cases
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- Test the package with your data and report results
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**Light coding:**
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- Fix typos in documentation
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- Add examples or tutorials
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- Improve error messages
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**More involved:**
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- Add new model architectures
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- Improve data processing tools
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- Add visualization features
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## 🆘 Need Help?
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- **First time with Git/GitHub?** Check out [GitHub's guides](https://guides.github.com/)
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- **Python packaging confusion?** Just ask in an issue - we're happy to help!
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- **Not sure where to start?** Look for issues labeled "good first issue"
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## 📝 Guidelines
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- **Be descriptive** in commit messages and pull requests
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- **Test your changes** - make sure imports still work
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- **Ask questions** - we'd rather help than have you struggle alone
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We appreciate all contributions, whether it's fixing a typo or adding a major feature!

‎LICENSE‎

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MIT License
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Copyright (c) 2024 Ritesh Chowdhry
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

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