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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7232e309b332d2d70a12522ee06cc5ec9996c3c7981ffb27e6cfa6102a3140f0
- Size of remote file:
- 25.6 MB
- SHA256:
- cd61a5229fea72ed02cdac9b45f750a1566ce48f8af623bf48333f5244386081
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