Instructions to use Antevolt/Sky-OG-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Antevolt/Sky-OG-Model with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("Antevolt/Sky-OG-Model") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Sky-OG-Model β PV Thermal Anomaly Detection & Module Segmentation
Models for the 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/
βββ README.md
βββ detection/
β βββ yolo26_pv_4class_base.pt # YOLO26 detection, 4-class (base)
β βββ yolo26_pv_4class_ft.pt # + fine-tuned on Sky-OG frames
βββ segmentation/
β βββ yolo11seg_module_autogeo.pt # YOLO11-seg, module auto-geo labels
βββ configs/
βββ data_detection.yaml
βββ data_segmentation.yaml
βββ sahi_inference.yaml
Evaluation
Metrics vs. Sitemark ground truth and per-class confusion matrix to be added once training completes.
- Downloads last month
- 56