Instructions to use tjpel/SignModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tjpel/SignModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tjpel/SignModel") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("tjpel/SignModel") model = AutoModelForImageClassification.from_pretrained("tjpel/SignModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08f2fcf2766875779d7748c533f181642406f0e92f583bdbe15d4434cba63c00
- Size of remote file:
- 343 MB
- SHA256:
- 866e7a9f0619c10a7266a0d7f181a6f494d9bcdc8b8d167b6810ff04fc7dfb4d
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