Instructions to use apple/deeplabv3-mobilevit-x-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/deeplabv3-mobilevit-x-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="apple/deeplabv3-mobilevit-x-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-x-small") model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-x-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from apple/deeplabv3-mobilevit-x-small: direct link, hf CLI and curl.
- Browser
- Download file 11.9 MB
-
https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://apple/deeplabv3-mobilevit-x-small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/pytorch_model.bin
11.9 MB
- Xet hash:
- 0671a010c6e37ce9c58bb15c9971a309c9ec9a7cd12495d544b4b4de4e979760
- Size of remote file:
- 11.9 MB
- SHA256:
- 9450e85de944b10ba3461be3469c4b24bc9140edcc024075af6ab28b46471985
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