Instructions to use Histochemichael/so101-rack-handle-seg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Histochemichael/so101-rack-handle-seg with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("Histochemichael/so101-rack-handle-seg") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
SO101 rack-handle segmentation
Gripper-camera segmentation model trained from 177 manually reviewed positive masks and 72 reviewed negatives.
Held-out test metrics
- Mask mAP50-95: 0.3145
- Mask mAP50: 0.8930
- Mask precision: 0.9274
- Mask recall: 0.8523
The split is grouped by source episode. The 20-image test split was not used for optimization or early stopping.
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