Instructions to use microsoft/resnet-101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-101") 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/resnet-101") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-101") - Notebooks
- Google Colab
- Kaggle
Commit ·
8829fd2
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Parent(s): df1df7e
Add TF weights
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py).
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=8.914e-04; Maximum converted output difference=8.914e-04.
- 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 sha256:9040077087c4420a94fd75ddbea596606faa777d1f23cd496dc970cd89f1c5dd
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size 179218872
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