Instructions to use theoberva/UBCO-Model-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theoberva/UBCO-Model-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="theoberva/UBCO-Model-v2") 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-v2") model = AutoModelForImageClassification.from_pretrained("theoberva/UBCO-Model-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from theoberva/UBCO-Model-v2: direct link, hf CLI and curl.
- Browser
- Download file 196 MB
-
https://huggingface.co/theoberva/UBCO-Model-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://theoberva/UBCO-Model-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/theoberva/UBCO-Model-v2/resolve/main/pytorch_model.bin
196 MB
- Xet hash:
- 0f05c4eb1966b310b5c112745c51953230f5665e486205f2e5af067ab0599dd2
- Size of remote file:
- 196 MB
- SHA256:
- aa3f27664f37d27f9b4b17d09c7c24bb168e87fa322bc867561bfd47a0ab15ac
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