Instructions to use G-dawg/table_experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use G-dawg/table_experimental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="G-dawg/table_experimental")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("G-dawg/table_experimental") model = AutoModelForObjectDetection.from_pretrained("G-dawg/table_experimental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from G-dawg/table_experimental: direct link, hf CLI and curl.
- Browser
- Download file 116 MB
-
https://huggingface.co/G-dawg/table_experimental/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://G-dawg/table_experimental/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/G-dawg/table_experimental/resolve/main/pytorch_model.bin
116 MB
- Xet hash:
- 669d749c7afa1692f6eeec632b663d2453c3a675dd96326bcf70e2a2e4254286
- Size of remote file:
- 116 MB
- SHA256:
- 11ffe3d6fc190ae063449d909221eee2ed952b878b5dea1e9d55f2eaf91cca80
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