Image Classification
Transformers
PyTorch
TensorBoard
Graphcore
vit
vision
Generated from Trainer
Eval Results (legacy)
Instructions to use jimypbr/cifar10_outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jimypbr/cifar10_outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jimypbr/cifar10_outputs") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, PoptorchPipelinedViTForImageClassification processor = AutoImageProcessor.from_pretrained("jimypbr/cifar10_outputs") model = PoptorchPipelinedViTForImageClassification.from_pretrained("jimypbr/cifar10_outputs") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#3 opened over 1 year ago
by
SFconvertbot
Librarian Bot: Add base_model information to model
#2 opened over 2 years ago
by
librarian-bot
Add evaluation results on cifar10 dataset
#1 opened almost 4 years ago
by
autoevaluator