Instructions to use ThermalVariations/tv-thermal-yolov8n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use ThermalVariations/tv-thermal-yolov8n with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("ThermalVariations/tv-thermal-yolov8n") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
tv-thermal-yolov8n
YOLOv8n thermal solar-PV fault detector. Bootstrap model for ThermalVariations (Track B) until labelled in-house fault data is available.
- Classes: hotspot, diode, misc (renamed from the source Hotspot/Diode/Anomaly to match the app taxonomy)
- Training data: public "Solar thermal image defects" (Roboflow Universe), 1685 train / 100 val; DJI Mavic 3T thermal frames.
- Val metrics (best.pt): mAP50 all 0.52 - hotspot 0.75, diode 0.45, misc 0.35.
- Intended use: demo/bootstrap only. Single small site; under-fires on other palettes (e.g. white-hot). Not production-accurate.
- App wiring: worker loads via hf_hub_download. Set TV_INFERENCE_BACKEND=yolo, TV_INFERENCE_MODEL_REPO=ThermalVariations/tv-thermal-yolov8n, TV_INFERENCE_MODEL_FILENAME=best.pt.
- Downloads last month
- 56