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 pytorch_model.bin from theoberva/UBCO-Model: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/theoberva/UBCO-Model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://theoberva/UBCO-Model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/theoberva/UBCO-Model/resolve/main/pytorch_model.bin
110 MB
- Xet hash:
- 24a52f4fa27acd8206666df8ce42cb9e7f7a2f9cdd21bb1a2010b2ad8b3e7fa6
- Size of remote file:
- 110 MB
- SHA256:
- 58d0f89740b9508692895b3f64a2ebfd6256f6fe2255c7f7f38d2910271d795e
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