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:
- 978604a2f27a80bde373f4798e47123a25c5d8b511f4ec529e4976d8008f8f07
- Size of remote file:
- 3.71 kB
- SHA256:
- c1e765ec500a37077d23cde786a5cf9ff7c88c595b908417164c6cf91fded741
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