Instructions to use aParadigmP/table-transformer-detection-custom-ale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aParadigmP/table-transformer-detection-custom-ale with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="aParadigmP/table-transformer-detection-custom-ale")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("aParadigmP/table-transformer-detection-custom-ale") model = AutoModelForObjectDetection.from_pretrained("aParadigmP/table-transformer-detection-custom-ale", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aParadigmP/table-transformer-detection-custom-ale: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/aParadigmP/table-transformer-detection-custom-ale/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aParadigmP/table-transformer-detection-custom-ale/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aParadigmP/table-transformer-detection-custom-ale/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 69a4350d6942e00f178dcce488d23b4f604cea8b29189053132a955462b7313a
- Size of remote file:
- 115 MB
- SHA256:
- 1d9babb11711211dd9fea2f477b7ba11f3656623505b07f9e30bfd41f143a9c7
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