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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from Jiqing/patched_tiny_random_vit_for_image_classification: direct link, hf CLI and curl.
- Browser
- Download file 191 kB
-
https://huggingface.co/Jiqing/patched_tiny_random_vit_for_image_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jiqing/patched_tiny_random_vit_for_image_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jiqing/patched_tiny_random_vit_for_image_classification/resolve/main/pytorch_model.bin
191 kB
- Xet hash:
- 5b14c9558cbab7f718e2356d14c115575992a2561716d8eaff93b5be9cfc41d1
- Size of remote file:
- 191 kB
- SHA256:
- b6e81d7aac2ea3b2955638256891567d90430191b9302da03d69ef08e9d58987
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.