Instructions to use Efferbach/mobilevit-small-10k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Efferbach/mobilevit-small-10k-steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Efferbach/mobilevit-small-10k-steps")# Load model directly from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Efferbach/mobilevit-small-10k-steps") model = MobileViTForSemanticSegmentation.from_pretrained("Efferbach/mobilevit-small-10k-steps") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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- image-segmentation
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- vision
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- generated_from_trainer
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model-index:
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- name: mobilevit-small-10k-steps
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results: []
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- image-segmentation
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- vision
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- generated_from_trainer
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base_model: apple/deeplabv3-mobilevit-small
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model-index:
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- name: mobilevit-small-10k-steps
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results: []
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