Instructions to use nvidia/mit-b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/mit-b3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nvidia/mit-b3") 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-b3") model = AutoModelForImageClassification.from_pretrained("nvidia/mit-b3") - Inference
- Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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tags:
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- vision
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- image-segmentation
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datasets:
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widget:
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- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
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example_title: House
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license: apache-2.0
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tags:
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- vision
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datasets:
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- imagenet_1k
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widget:
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- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
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example_title: House
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