Instructions to use TahaDouaji/detr-doc-table-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TahaDouaji/detr-doc-table-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="TahaDouaji/detr-doc-table-detection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("TahaDouaji/detr-doc-table-detection") model = AutoModelForObjectDetection.from_pretrained("TahaDouaji/detr-doc-table-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TahaDouaji/detr-doc-table-detection: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/TahaDouaji/detr-doc-table-detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TahaDouaji/detr-doc-table-detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TahaDouaji/detr-doc-table-detection/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- fd8cc303f94bc4c410f1c3b957a30a216a00c55c42b634e665f72fd3c4ed798a
- Size of remote file:
- 167 MB
- SHA256:
- 7e87cd360a5e13dbf93082b3079d5759fea057d2584462d23217cf684e6af4ec
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