Instructions to use AhmedSayeem/VIT_Basic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AhmedSayeem/VIT_Basic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AhmedSayeem/VIT_Basic") 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("AhmedSayeem/VIT_Basic") model = AutoModelForImageClassification.from_pretrained("AhmedSayeem/VIT_Basic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AhmedSayeem/VIT_Basic: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/AhmedSayeem/VIT_Basic/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AhmedSayeem/VIT_Basic/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AhmedSayeem/VIT_Basic/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 7859cfdbec9a5321a00f4da5c8bcd391a79f4e937e5e43c3b2d597b4ab43e3d3
- Size of remote file:
- 343 MB
- SHA256:
- 5ced9aad59730e16e3d3c88f0556aa4fe3a5a877dc8b34d72eff771535645692
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