Solar Panels Yolov8s Seg
Instance segmentation of individual PV arrays from drone and nadir aerial imagery, with tight masks on the panels themselves. Part of the Model Garden collection.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 22,7 MB
- Versión
- vRevision 21d8e9c
- Actualizado
- 10 oct 2026
- Descargas
- 18
- Fuente
- Hugging Face
Descripción de la fuente
Aerial Solar Panels (YOLOv8s-seg)
Instance segmentation of individual PV arrays from drone and nadir aerial imagery, with tight masks on the panels themselves. Part of the Model Garden collection.
Class
solar-panel
Provenance
Weights derive from
finloop/yolov8s-seg-solar-panels,
re-hosted here for the Model Garden showcase. On a Wikimedia solar-farm test
set it locked panels at conf 0.90 to 0.97 on 6 of 8 shots, missing only very
distant obliques.
Training
- Base: YOLOv8s-seg
- Task: instance segmentation
Usage
from ultralytics import YOLO
model = YOLO("finloop_seg.pt")
results = model("aerial.jpg")
results[0].show()
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 10 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- license: agpl-3.0 tags: - ultralytics - yolov8 - instance-segmentation - solar-panel - aerial - remote-sensing library_name: ultralytics pipeline_tag: image-segmentation --- # Aerial Solar Panels (YOLOv8s-seg) Instance segmentation of individual PV arrays from **drone and nadir aerial** imagery, with tight masks on the panels themselves. Part of the [Model Garden](https://github.com/computer-vision-with-marco) collection. ## Class `solar-panel` ## Provenance Weights derive from [`finloop/yolov8s-seg-solar-panels`](https://huggingface.co/finloop/yolov8s-seg-solar-panels), re-hosted here for the Model Garden showcase. On a Wikimedia solar-farm test set it locked panels at conf 0.90 to 0.97 on 6 of 8 shots, missing only very distant obliques. ## Training - **Base:** YOLOv8s-seg - **Task:** instance segmentation ## Usage ```python from ultralytics import YOLO model = YOLO("finloop_seg.pt") results = model("aerial.jpg") results[0].show() ```
Source context: 18 downloads · 1 likes · Pipeline image-segmentation · Library ultralytics · Repo mfranzon/solar-panels-yolov8s-seg
Source context: 25 downloads · 0 likes · Pipeline image-segmentation · Library ultralytics · Repo mfranzon/solar-panels-yolov8s-seg
