Instructions to use OmAlve/vit-base-pets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OmAlve/vit-base-pets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OmAlve/vit-base-pets") 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("OmAlve/vit-base-pets") model = AutoModelForImageClassification.from_pretrained("OmAlve/vit-base-pets", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from OmAlve/vit-base-pets: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/OmAlve/vit-base-pets/resolve/main/training_args.bin
- Command line
-
hf download hf://OmAlve/vit-base-pets/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/OmAlve/vit-base-pets/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- a9de0ee23e2ce32ecc7f2c6c321dfa8f86f6195fd2d302730927e051182cbf6e
- Size of remote file:
- 4.92 kB
- SHA256:
- 33d4c76de48bc1cfcdacd9758735c59ce75dd9bf02657f219a30684fa44e0d63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.