Instructions to use rezajebeli97/vit_on_simple_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rezajebeli97/vit_on_simple_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rezajebeli97/vit_on_simple_data") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("rezajebeli97/vit_on_simple_data") model = AutoModelForImageClassification.from_pretrained("rezajebeli97/vit_on_simple_data") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:36fa0f2d50aa381cbc027e37330e49c53523b269a08cc1a45a4d2ad6cc4e7a28
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size 346293856
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