Instructions to use lms7127/table with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lms7127/table with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="lms7127/table")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("lms7127/table") model = AutoModelForObjectDetection.from_pretrained("lms7127/table", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lms7127/table: direct link, hf CLI and curl.
- Browser
- Download file 166 MB
-
https://huggingface.co/lms7127/table/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lms7127/table/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lms7127/table/resolve/main/pytorch_model.bin
166 MB
- Xet hash:
- 42dbddc4965709b6dad8dbf1fb8a02e4db671645515f0124d8db3702de271d8d
- Size of remote file:
- 166 MB
- SHA256:
- a55f60e5c9fd5c2288ef572fdef158340b5108d44f0c12303ea4efb629c95a55
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