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deepghs/ai_image_corrupted
ai_image_corrupted is a image classification model from deepghs. Use it when you need a label for an image. It is set up for dghs-imgutils. The card lists the license as openrail.
This is the classifier model for predicting the anime-style stable-diffusion-generated images are corrupted or not.
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Updated Sep 28, 2024
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From the Hugging Face model README
This is the classifier model for predicting the anime-style stable-diffusion-generated images are corrupted or not.
Trained on dataset deepghs/ai_image_corrupted.
| Name | FLOPS | Params | Accuracy | AUC | Confusion | Labels |
|---|---|---|---|---|---|---|
| caformer_s36_v0_focal | 22.10G | 37.21M | 95.65% | 0.9916 | confusion | corrupted, normal |
| caformer_s36_v0_sce | 22.10G | 37.21M | 95.77% | 0.9894 | confusion | corrupted, normal |
| mobilenetv3_v0_focal_dist | 0.63G | 4.18M | 94.02% | 0.9842 | confusion | corrupted, normal |
You can use this model with dghs-imgutils.
from imgutils.generic import classify_predict_score
classify_predict_score(
'sample_image.png',
repo_id='deepghs/ai_image_corrupted',
model_name='mobilenetv3_v0_focal_dist',
)
# {'corrupted': 0.7807788848876953, 'normal': 0.2192210853099823}
This model is trained on SD1.5 images, generated by 8 different base models.
So the better solution for this problem is to use some metrics like artstyle embeddings, this model will be deprecated as soon as the artstyle embedding model for anime images is completed.
@misc{Citation,
title={AI-Corrupt Score for Anime Images},
author={narugo1992},
year={2023},
howpublished={\url{https://huggingface.co/deepghs/ai_image_corrupted}}
}