Instructions to use microsoft/table-transformer-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/table-transformer-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="microsoft/table-transformer-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection") model = AutoModelForObjectDetection.from_pretrained("microsoft/table-transformer-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/table-transformer-detection: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/microsoft/table-transformer-detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/table-transformer-detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/table-transformer-detection/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- ba4c16ffad763c86a2cf77f212f54690ce4a3a5e8e1cfffabd846a9532949a1f
- Size of remote file:
- 115 MB
- SHA256:
- 1d9babb11711211dd9fea2f477b7ba11f3656623505b07f9e30bfd41f143a9c7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.