Instructions to use Nonabzbssbbsbs/CIFAR10-Kazakh-Fast-Demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nonabzbssbbsbs/CIFAR10-Kazakh-Fast-Demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Nonabzbssbbsbs/CIFAR10-Kazakh-Fast-Demo") 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("Nonabzbssbbsbs/CIFAR10-Kazakh-Fast-Demo") model = AutoModelForImageClassification.from_pretrained("Nonabzbssbbsbs/CIFAR10-Kazakh-Fast-Demo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "vit", | |
| "architectures": [ | |
| "ViTForImageClassification" | |
| ], | |
| "hidden_size": 768, | |
| "num_hidden_layers": 12, | |
| "num_attention_heads": 12 | |
| } |