Instructions to use ravindrakapse/drywall-yoloe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use ravindrakapse/drywall-yoloe with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("ravindrakapse/drywall-yoloe", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
drywall-yoloe
YOLOE (open-vocab YOLO11 + text-prompted seg head) fine-tuned via Ultralytics
YOLOEPESegTrainer. yoloe-11s-seg backbone, imgsz 640, 30 epochs, prompt
augmentation (paraphrase per batch).
Test metrics
| Task | Dice | mIoU | Precision | Recall | ms/img |
|---|---|---|---|---|---|
| Crack | 0.681 | 0.547 | 0.690 | 0.757 | ~30 |
| Taping | 0.859 | 0.774 | 0.807 | 0.956 | ~30 |
Best of all three pipelines on Dice + latency.
Load + predict
from huggingface_hub import hf_hub_download
from ultralytics import YOLOE
ckpt = hf_hub_download(repo_id="ravindrakapse/drywall-yoloe", filename="best.pt")
model = YOLOE(ckpt)
results = model.predict("image.jpg", imgsz=640, conf=0.05)
See load_models.py.
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