Instructions to use G-dawg/table_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use G-dawg/table_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="G-dawg/table_final")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("G-dawg/table_final") model = AutoModelForObjectDetection.from_pretrained("G-dawg/table_final") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 00b894541c0122656ea073d2e409aeda48d4c0c9a8e4a7dbd08b5fb44821a1f5
- Size of remote file:
- 116 MB
- SHA256:
- 176120952d366438b536f755d8a2b525b68e0c482c010008f5949f2746a46637
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