Instructions to use abidlabs/cifar10-vit-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abidlabs/cifar10-vit-poc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="abidlabs/cifar10-vit-poc") 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("abidlabs/cifar10-vit-poc") model = AutoModelForImageClassification.from_pretrained("abidlabs/cifar10-vit-poc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6a70fa7ce1323f85cdc4cc13594392206e41e2ddbfb3d88df5f3deeb58343d6
- Size of remote file:
- 5.33 kB
- SHA256:
- 3d84284cb3959ffc8b68db3364083726b1738424083ff2241500028b4b338f2a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.