Instructions to use prithivMLmods/Multisource-121-DomainNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Multisource-121-DomainNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Multisource-121-DomainNet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Multisource-121-DomainNet") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Multisource-121-DomainNet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adbb3c8f10305f32f8c0e0cb31fabbde682ca14a36ba4c73069170a46c7b4628
- Size of remote file:
- 1.06 kB
- SHA256:
- 7548d314b3526255122b39d5cd6162a5b057112b63c2fc571557a7b4a6d6d36e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.