Instructions to use swapnilpote/table-transformer-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swapnilpote/table-transformer-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="swapnilpote/table-transformer-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("swapnilpote/table-transformer-detection") model = AutoModelForObjectDetection.from_pretrained("swapnilpote/table-transformer-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from swapnilpote/table-transformer-detection: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/swapnilpote/table-transformer-detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://swapnilpote/table-transformer-detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/swapnilpote/table-transformer-detection/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- dd2192f8e7be268ed1bebcb827d1f485b7e78d797fa66c1ce8db91bd18e17e26
- Size of remote file:
- 115 MB
- SHA256:
- 17898fffd93d8d3ecf9c82c487943a82a02986510ea1233bf70e7f868fd6b289
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