Object Detection
ultralytics
PyTorch
yolosv5
ultralyticsplus
yolov5
yolo
vision
awesome-yolov8-models
indonesia
layout detector
Eval Results (legacy)
Instructions to use hermanshid/yolo-layout-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use hermanshid/yolo-layout-detector with ultralytics:
from ultralytics import YOLOvv5 model = YOLOvv5.from_pretrained("hermanshid/yolo-layout-detector") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c4a170d1f772b35cc40668e60a06e7dc473fab38b6d555fa87fd05662ad01dc
- Size of remote file:
- 14.4 MB
- SHA256:
- c8172e7f7c9898a8ee4855c6b01ed485458a82492fd80547202bd0f96663571a
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