Instructions to use AmineAllo/table-transformer-true-bush-63 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/table-transformer-true-bush-63 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/table-transformer-true-bush-63")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/table-transformer-true-bush-63") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/table-transformer-true-bush-63", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AmineAllo/table-transformer-true-bush-63: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/AmineAllo/table-transformer-true-bush-63/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AmineAllo/table-transformer-true-bush-63/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AmineAllo/table-transformer-true-bush-63/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- b212f8a13320a97b51c8c828ba2f32ee0efca3c9f9ec4dcae6a03efb99f5b96e
- Size of remote file:
- 115 MB
- SHA256:
- c4062d89742f2f2549a57fca8d10222cb8b30e9fcee4a14aed97e307c49eac88
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.