Instructions to use alicelouis/DinoV2_grading_Drawing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alicelouis/DinoV2_grading_Drawing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="alicelouis/DinoV2_grading_Drawing") 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("alicelouis/DinoV2_grading_Drawing") model = AutoModelForImageClassification.from_pretrained("alicelouis/DinoV2_grading_Drawing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from alicelouis/DinoV2_grading_Drawing: direct link, hf CLI and curl.
- Browser
- Download file 699 MB
-
https://huggingface.co/alicelouis/DinoV2_grading_Drawing/resolve/main/optimizer.pt
- Command line
-
hf download hf://alicelouis/DinoV2_grading_Drawing/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/alicelouis/DinoV2_grading_Drawing/resolve/main/optimizer.pt
699 MB
- Xet hash:
- d3e2663052e3afb05f2caa16096b322d2cd75e003380322a5088aa2ddcf0ab76
- Size of remote file:
- 699 MB
- SHA256:
- c0c7d28d9c9edb2f1f48a6affb6d414fcd0a8ac4930e43c8671a98a560b5d3d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.