Instructions to use ahishamm/vit-base-PH2-sharpened-patch-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahishamm/vit-base-PH2-sharpened-patch-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahishamm/vit-base-PH2-sharpened-patch-32") 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("ahishamm/vit-base-PH2-sharpened-patch-32") model = AutoModelForImageClassification.from_pretrained("ahishamm/vit-base-PH2-sharpened-patch-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ahishamm/vit-base-PH2-sharpened-patch-32: direct link, hf CLI and curl.
- Browser
- Download file 350 MB
-
https://huggingface.co/ahishamm/vit-base-PH2-sharpened-patch-32/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ahishamm/vit-base-PH2-sharpened-patch-32/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ahishamm/vit-base-PH2-sharpened-patch-32/resolve/main/pytorch_model.bin
350 MB
- Xet hash:
- 8a278b689259b6edb70177554c96e3c28c8e9ef4e8325832bcce92f4b75a189a
- Size of remote file:
- 350 MB
- SHA256:
- 3aab33543fd0cef46e48fbfef395d6c0b3b7a7b6ec3efaa86a162c02d36d4bf2
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