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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("shadowlilac/aesthetic-shadow") model = AutoModelForImageClassification.from_pretrained("shadowlilac/aesthetic-shadow", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from shadowlilac/aesthetic-shadow: direct link, hf CLI and curl.
- Browser
- Download file 329 Bytes
-
https://huggingface.co/shadowlilac/aesthetic-shadow/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://shadowlilac/aesthetic-shadow@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/shadowlilac/aesthetic-shadow/resolve/refs%2Fpr%2F1/preprocessor_config.json
329 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTFeatureExtractor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 1024, | |
| "width": 1024 | |
| } | |
| } | |