Instructions to use vikp/layout_segmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/layout_segmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vikp/layout_segmenter")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("vikp/layout_segmenter") model = AutoModelForTokenClassification.from_pretrained("vikp/layout_segmenter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vikp/layout_segmenter: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/vikp/layout_segmenter/resolve/refs%2Fpr%2F4/pytorch_model.bin
- Command line
-
hf download hf://vikp/layout_segmenter@refs/pr/4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vikp/layout_segmenter/resolve/refs%2Fpr%2F4/pytorch_model.bin
504 MB
- Xet hash:
- 0d7e6cf6a98ccbe9bb0a1e951aa5e251f860b653b67e38849b5cfcd88d3f7db2
- Size of remote file:
- 504 MB
- SHA256:
- 5d522d4812009bb5b1f9ba6344b709f3b31873e97f5aa2c368dfce10d386b222
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