Instructions to use pphildan/vit-base-patch16-224-v28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pphildan/vit-base-patch16-224-v28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pphildan/vit-base-patch16-224-v28") 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("pphildan/vit-base-patch16-224-v28") model = AutoModelForImageClassification.from_pretrained("pphildan/vit-base-patch16-224-v28", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pphildan/vit-base-patch16-224-v28: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/pphildan/vit-base-patch16-224-v28/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pphildan/vit-base-patch16-224-v28/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pphildan/vit-base-patch16-224-v28/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 251809081633ab899bac1df7a0821fe019aa9508e5fb510ff5878f53aceaf2ff
- Size of remote file:
- 343 MB
- SHA256:
- 35b22207be156ba5fe4eb71a422c922aa5f33852f0f5fa9117981f2bb22c2756
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