Instructions to use bcheung/dinov2-base-finetuned-agreement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bcheung/dinov2-base-finetuned-agreement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bcheung/dinov2-base-finetuned-agreement") 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("bcheung/dinov2-base-finetuned-agreement") model = AutoModelForImageClassification.from_pretrained("bcheung/dinov2-base-finetuned-agreement", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c3c117b3c249761e4ccc36d3b42d59c42e6914d6254c25283c488e346974308
- Size of remote file:
- 346 MB
- SHA256:
- e71b610753a9db94b587ca6a09c92b500f1a9150a34d4f7400d778c9933b9560
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.