Instructions to use LanluZ/vascular-bundle-yolo26 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use LanluZ/vascular-bundle-yolo26 with ultralytics:
from ultralytics import YOLOvv26 model = YOLOvv26.from_pretrained("LanluZ/vascular-bundle-yolo26") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - YOLOv26
How to use LanluZ/vascular-bundle-yolo26 with YOLOv26:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
YOLO26m bamboo vascular bundle detector
Fine-tuned on the bamboo vascular bundle dataset (7 train / 2 val images), 144 epochs with early stopping (best at epoch 114), ultralytics 8.4.165, AdamW lr0=0.001, AMP. Results: mAP50-95 0.9694 (results.csv best; independent val() 0.970), mAP50 0.9939, P 0.9909, R 0.9758.
Source repo: https://github.com/LanluZ/vascular-bundle-track (branch yolo26-migration).
Download:
hf download LanluZ/vascular-bundle-yolo26 weights/best.pt --local-dir runs/detect/bamboo_yolo26_20260929
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