Instructions to use theoberva/UBCO-Model-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theoberva/UBCO-Model-512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="theoberva/UBCO-Model-512") 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("theoberva/UBCO-Model-512") model = AutoModelForImageClassification.from_pretrained("theoberva/UBCO-Model-512") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fdb5bd46e769c270e050140b795920c6e536e0dc01f97cd03cf3621b9ec8b9f7
- Size of remote file:
- 348 MB
- SHA256:
- fb9ee6538394bb1d0dac4a4086ea41e85955c2711334b6b8b3c20dc8a579a83f
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