Instructions to use Jiqing/patched_tiny_random_vit_for_image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jiqing/patched_tiny_random_vit_for_image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Jiqing/patched_tiny_random_vit_for_image_classification") 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("Jiqing/patched_tiny_random_vit_for_image_classification") model = AutoModelForImageClassification.from_pretrained("Jiqing/patched_tiny_random_vit_for_image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b14c9558cbab7f718e2356d14c115575992a2561716d8eaff93b5be9cfc41d1
- Size of remote file:
- 191 kB
- SHA256:
- b6e81d7aac2ea3b2955638256891567d90430191b9302da03d69ef08e9d58987
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