| license: other | |
| tags: | |
| - hyperspectral | |
| - anomaly-detection | |
| - dinomaly | |
| - cuvis-ai | |
| library_name: cuvis-ai | |
| # Dinomaly - bedding (6-channel VIS+SWIR) anomaly detector | |
| Trained Dinomaly (Anomalib) pipeline for foreign-object anomaly detection on the | |
| bedding hyperspectral dataset | |
| ([cubert-gmbh/X4_SWIR_Industrial_Foreign_Object_Detection_Bedding](https://huggingface.co/datasets/cubert-gmbh/X4_SWIR_Industrial_Foreign_Object_Detection_Bedding)). | |
| - 6-band selector (625, 550, 450, 1450, 1200, 1050) nm, input_channels=6 | |
| (duplicate-and-halve patch-embed inflation), image_size=672 (square squash). | |
| - Trained 20 epochs, fp32, on the 1800x4300 center crop of the native 2400x4900 cubes. | |
| ## Files | |
| - dinomaly_bedding_all6.yaml / .pt - serialized cuvis-ai pipeline (load with CuvisPipeline.load_pipeline). | |
| - eval_val/ - validation metrics (report.json, per-class AUROC, Dice). | |
| ## Headline (val, 59 frames) | |
| pixel AUROC 0.976 - image AUROC (filename) 0.984 - Dice@F1 0.615 - mean per-class AUROC ~0.95. | |
| Used by the bedding tutorial notebooks in cuvis-ai-dinomaly/notebooks/bedding_anomaly/. | |
| Requires the cuvis SDK + high-level cuvis-ai to load (pipeline uses cuvis_ai.node.* built-ins). | |