Instructions to use shunk031/aesthetics-predictor-v1-vit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shunk031/aesthetics-predictor-v1-vit-base-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="shunk031/aesthetics-predictor-v1-vit-base-patch16", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shunk031/aesthetics-predictor-v1-vit-base-patch16", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4842cdcab3c43f5587175f935c9901a5a202a51939657e87888453f7cc04cc48
- Size of remote file:
- 345 MB
- SHA256:
- bca1e0d8776105777b01015ce2a4dd57342e3ceaad5eb043aaa009a0493dedb1
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