Instructions to use nvidia/mit-b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/mit-b2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nvidia/mit-b2") 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("nvidia/mit-b2") model = AutoModelForImageClassification.from_pretrained("nvidia/mit-b2") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
3b0b8dd
1
Parent(s): 4514980
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=5.007e-06; Maximum crossload hidden layer difference=9.346e-05;
Maximum conversion output difference=5.007e-06; Maximum conversion hidden layer difference=9.346e-05;
- 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 sha256:3034758dedb4f3e3436bfabaa3043ca7ccb20c24cc202e6d2670c962af772ede
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size 99336456
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