Instructions to use crocutacrocuto/dinov2-large-MEG7-20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crocutacrocuto/dinov2-large-MEG7-20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="crocutacrocuto/dinov2-large-MEG7-20") 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("crocutacrocuto/dinov2-large-MEG7-20") model = AutoModelForImageClassification.from_pretrained("crocutacrocuto/dinov2-large-MEG7-20") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c113d0a5aedd4afcca94020e6934224e057e821fc5cdfee4b80599b50706530c
- Size of remote file:
- 1.22 GB
- SHA256:
- c53a6b33eb59d3758ee1b42a1e883bf1615e71bfd094a589fac45e4a34d97c1f
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