Instructions to use prithivMLmods/Painting-126-DomainNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Painting-126-DomainNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Painting-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/Painting-126-DomainNet") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Painting-126-DomainNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-1502/optimizer.pt from prithivMLmods/Painting-126-DomainNet: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/Painting-126-DomainNet/resolve/main/checkpoint-1502/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Painting-126-DomainNet/checkpoint-1502/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Painting-126-DomainNet/resolve/main/checkpoint-1502/optimizer.pt
687 MB
- Xet hash:
- 41cdaf54a9423fe9e8fbf32c78499714ae705e64785282ab7ae70c26da1524d5
- Size of remote file:
- 687 MB
- SHA256:
- df83bc7fdbecd8febf90d02b2a563e51e0652369d2ace1bb78eb1351d7c9a657
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