Instructions to use hf-internal-testing/tiny-random-MobileViTForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileViTForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MobileViTForSemanticSegmentation") model = MobileViTForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-MobileViTForSemanticSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f705d6b6cd94a570306a2a14f33b191f3bd5039a6d4015f60d2cf065f190f577
- Size of remote file:
- 25.9 MB
- SHA256:
- 87570e79095687f8aaa32ffe3c715c1f4466d8f4244cb6e37c2eb5a513fd5df3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.