Instructions to use Acras/Vit-Flower with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Acras/Vit-Flower with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Acras/Vit-Flower") 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("Acras/Vit-Flower") model = AutoModelForImageClassification.from_pretrained("Acras/Vit-Flower") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "image_mean": [0.5, 0.5, 0.5], | |
| "image_std": [0.5, 0.5, 0.5], | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| }, | |
| "resample": 3 | |
| } | |