Object Detection
ultralytics
ONNX
yolo
yolo11
document-layout-analysis
document-understanding
research-papers
Instructions to use darkdwine/yolo11-doc-layout-research-papers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use darkdwine/yolo11-doc-layout-research-papers 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("darkdwine/yolo11-doc-layout-research-papers", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download speed-accuracy.png from darkdwine/yolo11-doc-layout-research-papers: direct link, hf CLI and curl.
- Browser
- Download file 107 kB
-
https://huggingface.co/darkdwine/yolo11-doc-layout-research-papers/resolve/main/speed-accuracy.png
- Command line
-
hf download hf://darkdwine/yolo11-doc-layout-research-papers/speed-accuracy.png
-
curl -L -o speed-accuracy.png https://huggingface.co/darkdwine/yolo11-doc-layout-research-papers/resolve/main/speed-accuracy.png
107 kB

- Xet hash:
- 5c0b05adc0da020b641fb8a940841b855f935284acc15bb611e12c1027dc9c9b
- Size of remote file:
- 107 kB
- SHA256:
- 5b72de15de1a191e99254542e191f7a5c3f88ae8ed8cc3b9b76b688af880db75
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