Instructions to use theoberva/UBCO-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theoberva/UBCO-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="theoberva/UBCO-Model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("theoberva/UBCO-Model") model = AutoModelForImageClassification.from_pretrained("theoberva/UBCO-Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from theoberva/UBCO-Model: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/theoberva/UBCO-Model/resolve/main/training_args.bin
- Command line
-
hf download hf://theoberva/UBCO-Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/theoberva/UBCO-Model/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 31762e6dafc8c8b0176670da4fe1a161931f9f02dbb9e42290f4ce1b6df49625
- Size of remote file:
- 4.09 kB
- SHA256:
- fa951de9080ae87e5c5845b9f1670d092b418b5a05abd96c73b150f01ab0d9a9
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