Instructions to use buddhadeb33/output_dinov2_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buddhadeb33/output_dinov2_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="buddhadeb33/output_dinov2_large") 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("buddhadeb33/output_dinov2_large") model = AutoModelForImageClassification.from_pretrained("buddhadeb33/output_dinov2_large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e344d44544875a3c6896a1c8b2317a0c4e5e16f7344d8900c0cc9e6615f5ee07
- Size of remote file:
- 1.22 GB
- SHA256:
- ad4df401f0ea555eea8a699cdc5a42ab2bcfbce5d3e87a3cae058fc9f954a748
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