Instructions to use prithivMLmods/Sketch-126-DomainNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Sketch-126-DomainNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Sketch-126-DomainNet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Sketch-126-DomainNet") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Sketch-126-DomainNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-604/optimizer.pt from prithivMLmods/Sketch-126-DomainNet: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/Sketch-126-DomainNet/resolve/main/checkpoint-604/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Sketch-126-DomainNet/checkpoint-604/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Sketch-126-DomainNet/resolve/main/checkpoint-604/optimizer.pt
687 MB
- Xet hash:
- 8ba21f1d9db1807bcf6300de812ff6939cc76a41f8c8cbab7a79b506d763a426
- Size of remote file:
- 687 MB
- SHA256:
- 446f34abd516c4d33e91d6635b33b48ef2e744cb1f7d246b3dfeecd1aedff1f4
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