Instructions to use pk3388/vit-base-patch16-224-Rado_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pk3388/vit-base-patch16-224-Rado_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pk3388/vit-base-patch16-224-Rado_5") 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("pk3388/vit-base-patch16-224-Rado_5") model = AutoModelForImageClassification.from_pretrained("pk3388/vit-base-patch16-224-Rado_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| datasets: | |
| - pk3388/watch | |
| - pk3388/test_watches | |
| - pk3388/watches | |
| language: | |
| - en | |
| base_model: | |
| - google/vit-base-patch16-224 | |
| new_version: pk3388/vit-base-patch16-224-Rado_5 | |
| library_name: transformers | |