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 training_args.bin from ahishamm/vit-base-PH2-sharpened-patch-32: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/ahishamm/vit-base-PH2-sharpened-patch-32/resolve/main/training_args.bin
- Command line
-
hf download hf://ahishamm/vit-base-PH2-sharpened-patch-32/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ahishamm/vit-base-PH2-sharpened-patch-32/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- c473567027c952c4c434b31b2f89ff3e6ccf7e6fe070761cb794302786b4ad3b
- Size of remote file:
- 3.96 kB
- SHA256:
- 1e37ea36207b79df2caa7c7d7ba9527653dce1c853542292fd68df27e7147f0a
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