Instructions to use hf-internal-testing/tiny-random-DeiTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DeiTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-DeiTForImageClassification") 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("hf-internal-testing/tiny-random-DeiTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-DeiTForImageClassification") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
#2
by Xenova HF Staff - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:4463a62f0fb69426bfb7e7449fe2313559e56e4589fe5515872c38548b3d0a42
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size 280467
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