Instructions to use tsushil/vit-base-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsushil/vit-base-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tsushil/vit-base-cifar10") 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("tsushil/vit-base-cifar10") model = AutoModelForImageClassification.from_pretrained("tsushil/vit-base-cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6dfd85cbebf8cc640837e36a77c9319bc169b36adead277664fa90369207462e
- Size of remote file:
- 343 MB
- SHA256:
- fc55aec9a70e5b0b891a0b6a1b9ef2259aeb7b44da892e7ac3b94d3ef9031040
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.