Instructions to use ydshieh/tiny-random-ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ydshieh/tiny-random-ViTForImageClassification") 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("ydshieh/tiny-random-ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("ydshieh/tiny-random-ViTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ydshieh/tiny-random-ViTForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 194 kB
-
https://huggingface.co/ydshieh/tiny-random-ViTForImageClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-ViTForImageClassification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-ViTForImageClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
194 kB
- Xet hash:
- c265d4fa2d5bf8938ffd01bebde3072eb0d97531cec4425718ac04940de82675
- Size of remote file:
- 194 kB
- SHA256:
- aca10c5eb306ab44ca0c941adcf4eb24b6d5b68a8e28acbb9aca978bfc3a2058
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.