Instructions to use roylvzn/vit-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use roylvzn/vit-cifar10 with timm:
import timm model = timm.create_model("hf-hub:roylvzn/vit-cifar10", pretrained=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from roylvzn/vit-cifar10: direct link, hf CLI and curl.
- Browser
- Download file 548 Bytes
-
https://huggingface.co/roylvzn/vit-cifar10/resolve/main/README.md
- Command line
-
hf download hf://roylvzn/vit-cifar10/README.md
-
curl -L -o README.md https://huggingface.co/roylvzn/vit-cifar10/resolve/main/README.md
548 Bytes
metadata
library_name: timm
license: apache-2.0
datasets:
- cifar10
tags:
- vision
- image-classification
- cifar10
- vit
model-index:
- name: vit-cifar10
results:
- task:
type: image-classification
dataset:
name: CIFAR-10
type: cifar10
metrics:
- type: accuracy
value: 0.95
ViT Base (patch16, 224) fine-tuned on CIFAR-10
Trained on CIFAR-10 (10 classes). Weights saved as a plain PyTorch state_dict (pytorch_model.bin).
Architecture is defined in vit_model.py (uses timm).