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.
Source du modèle
- Type
- Other
- Taille du fichier
- 22,7 MB
- Version
- vRevision 21d8e9c
- Mis à jour
- 10 oct. 2026
- Téléchargements
- 18
- Source
- Hugging Face
Description de la source
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()
Sources
1 source
Sources
1 sourceVérifié 10 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- 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
