Instructions to use AmineAllo/table-transformer-azure-dust-65 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/table-transformer-azure-dust-65 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/table-transformer-azure-dust-65")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/table-transformer-azure-dust-65") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/table-transformer-azure-dust-65", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AmineAllo/table-transformer-azure-dust-65: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/AmineAllo/table-transformer-azure-dust-65/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AmineAllo/table-transformer-azure-dust-65/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AmineAllo/table-transformer-azure-dust-65/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 1a7f4325b005eb814b9a36405e3567615aa3aabb6d4b9a2c27cc1fff9a53c7bf
- Size of remote file:
- 115 MB
- SHA256:
- c409df5d95e598e13abf00907829a1941c6a58c041293f2a7d66e581b86fdae1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.