Instructions to use microsoft/cvt-13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/cvt-13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/cvt-13") 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("microsoft/cvt-13") model = AutoModelForImageClassification.from_pretrained("microsoft/cvt-13") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights (#1)
Browse files- Add TF weights (6f59baab62ffb310c4878738e5f0ce95e191feeb)
Co-authored-by: Mathieu Jouffroy <CCMat@users.noreply.huggingface.co>
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:d782bacd92bd57ab1b7e59e5cbff7f1a102e094cc338ac72186af080ebd5abc1
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size 80698472
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