Instructions to use Emreuludasdemir/teknofest2026-task3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LightGlue
How to use Emreuludasdemir/teknofest2026-task3 with LightGlue:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| from __future__ import annotations | |
| """Probe utility for standalone Task 3 reference modality detection.""" | |
| import argparse | |
| from pathlib import Path | |
| import sys | |
| PROJECT_ROOT = Path(__file__).resolve().parents[1] | |
| if str(PROJECT_ROOT) not in sys.path: | |
| sys.path.insert(0, str(PROJECT_ROOT)) | |
| from src.task3.modality_detection import detect_modality | |
| def main() -> int: | |
| parser = argparse.ArgumentParser(description="Probe Task 3 reference modality detection.") | |
| parser.add_argument("images", nargs="+", type=Path, help="One or more image paths to classify.") | |
| args = parser.parse_args() | |
| for image_path in args.images: | |
| modality, diagnostics = detect_modality(image_path) | |
| exif_signals = ",".join(diagnostics["exif_signals"]) or "-" | |
| pixel_signals = diagnostics["pixel_signals"] | |
| summary = ( | |
| f"sat={pixel_signals.get('mean_saturation', '-')}, " | |
| f"corr={pixel_signals.get('min_channel_correlation', '-')}, " | |
| f"bimodal={pixel_signals.get('histogram_bimodal', '-')}" | |
| ) | |
| print( | |
| f"{image_path.name:<18} {modality.value.upper():<8} {diagnostics['method']:<5} " | |
| f"{diagnostics['confidence']:<6} exif=[{exif_signals}] {summary}" | |
| ) | |
| return 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |