Instructions to use hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, MobileViTV2ForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation") model = MobileViTV2ForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c6d6f5211d83281987fbe3d7fa01670fc6f62a5389f831049729e833e3d0a8d
- Size of remote file:
- 14.2 MB
- SHA256:
- 15e3bbd68ec604e5f074ecc9d23b7475dbe57c8e2d154d2847f4aed252bfad8e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.