Instructions to use G-dawg/table_exp_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use G-dawg/table_exp_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="G-dawg/table_exp_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("G-dawg/table_exp_2") model = AutoModelForObjectDetection.from_pretrained("G-dawg/table_exp_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from G-dawg/table_exp_2: direct link, hf CLI and curl.
- Browser
- Download file 116 MB
-
https://huggingface.co/G-dawg/table_exp_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://G-dawg/table_exp_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/G-dawg/table_exp_2/resolve/main/pytorch_model.bin
116 MB
- Xet hash:
- 87947c6c04f517a2c0c49c079d4a8155bd59f2cea9af4d1532c07e45653ddc5b
- Size of remote file:
- 116 MB
- SHA256:
- 18ab9f028d8e5dbb059e29246bbd6fca2c393607d5595a43581f8fa7f0bbd279
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