| license: agpl-3.0 | |
| library_name: ultralytics | |
| tags: | |
| - yolo | |
| - object-detection | |
| - image-segmentation | |
| - solar | |
| - photovoltaic | |
| - thermal | |
| pipeline_tag: object-detection | |
| # Sky-OG-Model — PV Thermal Anomaly Detection & Module Segmentation | |
| Models for the [Sky-OG](https://huggingface.co/datasets/merdiofrivia/Sky-OG) | |
| PV thermal inspection pipeline. | |
| > **Status:** scaffold only. Weights and finalized configs are produced in RunPod | |
| > and pushed here. The tree below is the intended layout. | |
| ## Repository structure | |
| ``` | |
| Sky-OG-Model/ | |
| ├── README.md | |
| ├── detection/ | |
| │ ├── yolo26_pv_4class_base.pt # YOLO26 detection, 4-class (base) | |
| │ └── yolo26_pv_4class_ft.pt # + fine-tuned on Sky-OG frames | |
| ├── segmentation/ | |
| │ └── yolo11seg_module_autogeo.pt # YOLO11-seg, module auto-geo labels | |
| └── configs/ | |
| ├── data_detection.yaml | |
| ├── data_segmentation.yaml | |
| └── sahi_inference.yaml | |
| ``` | |
| ## Evaluation | |
| Metrics vs. Sitemark ground truth and per-class confusion matrix to be added | |
| once training completes. | |