745a64b342
- VegaModel CNN-FCNN 34.5M params, 82 isotopes, val acc 99.89% - Generation 50k spectres synthetiques 1D (12-24h durees) - Entrainement 100 epochs sur RTX 5060 Ti (CUDA 12.8, Blackwell) - Detection continue avec soustraction du background - Capture background 24h avec gestion deconnexion - Docker Compose : conteneur train (GPU) + detect (CPU/USB) - Modele entraite inclus (vega_best.pt, 395 Mo) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
27 lines
799 B
Python
27 lines
799 B
Python
"""
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Vega Model - CNN-FCNN with Multi-Task Heads for Gamma Spectrum Isotope Identification
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Architecture based on research findings from:
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- Wang et al. (2026): CNN-FCNN achieves 99.8% accuracy
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- Galib et al. (2021): Hybrid CNN outperforms pure architectures
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- Turner et al. (2021): 1D CNN robust to gain shifts and shielding
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Features:
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- 1D CNN backbone for spectral feature extraction
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- Multi-task heads for isotope classification + activity regression
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- Support for 82 isotopes from the synthetic spectra database
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"""
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from .model import VegaModel, VegaConfig
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from .dataset import SpectrumDataset, create_data_loaders
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from .train import train_vega, VegaTrainer
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__all__ = [
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'VegaModel',
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'VegaConfig',
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'SpectrumDataset',
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'create_data_loaders',
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'train_vega',
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'VegaTrainer'
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]
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