Instructions to use ivensamdh/beitv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivensamdh/beitv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ivensamdh/beitv2") 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("ivensamdh/beitv2") model = AutoModelForImageClassification.from_pretrained("ivensamdh/beitv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f25f6096c49563f06ef20cf212f15f1acf7ec7a7348d0906e020a129fc1987d0
- Size of remote file:
- 347 MB
- SHA256:
- 25c942613ca3e4daaf5ade1d2c85c57fe703a77a7efee4cb9f0f5a44a00066c7
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