Image Classification
Transformers
PyTorch
Safetensors
vit
anime
quality assurance
dataset maintenance
Instructions to use shadowlilac/aesthetic-shadow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shadowlilac/aesthetic-shadow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="shadowlilac/aesthetic-shadow") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("shadowlilac/aesthetic-shadow") model = AutoModelForImageClassification.from_pretrained("shadowlilac/aesthetic-shadow") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d8eea5309050b8635f733c3703727b92655440c2781397b4c2114d21d856832
- Size of remote file:
- 4.37 GB
- SHA256:
- 7eac1cb6aa06d1a82fa162e124bfbcd6aaaa47dcfbcb8d1a628618e3c1d6f581
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