Instructions to use nvidia/mit-b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/mit-b1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nvidia/mit-b1") 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-b1") model = AutoModelForImageClassification.from_pretrained("nvidia/mit-b1") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
17ebbf8
1
Parent(s): 73b65a0
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=2.742e-06; Maximum crossload hidden layer difference=3.713e-05;
Maximum conversion output difference=2.742e-06; Maximum conversion hidden layer difference=3.713e-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 sha256:163057aa0e923a68dda4795e764e74790efa0c863f281f35fb75530a5d70aca7
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size 54919784
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