Instructions to use asusevski/vit-dog-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asusevski/vit-dog-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="asusevski/vit-dog-classifier") 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("asusevski/vit-dog-classifier") model = AutoModelForImageClassification.from_pretrained("asusevski/vit-dog-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb0bcf6e31a91ebdaaa6887e12df97badd1c44333223531eb6fceb13e6b1acce
- Size of remote file:
- 343 MB
- SHA256:
- ed2d43849ce8a490187f5b49d9d31f6f7e74c41d4c0ad8dabc67b5432eec9ba6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.