Instructions to use microsoft/resnet-152 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-152 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-152") 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-152") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-152") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
d670374
1
Parent(s): d2848da
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=1.123e-03; Maximum converted output difference=1.123e-03.
- 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:d6f9bb33d7ed9ba5a7cb3c3d11be2962aed0523373faed4b35896b3fb487ec13
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size 242237176
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