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-751/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-751/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Painting-126-DomainNet/checkpoint-751/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Painting-126-DomainNet/resolve/main/checkpoint-751/optimizer.pt
687 MB
- Xet hash:
- 12e745d9027981d81dbcd95c09c4d1de2928d13cbb8cc659549963ac21c29393
- Size of remote file:
- 687 MB
- SHA256:
- 05272cbba407c24c5e9cd346003622cc3b0a7ddd9449a5cbe6f3027e3918412e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.