metadata
license: openrail
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- art
datasets:
- deepghs/ai_image_corrupted
library_name: dghs-imgutils
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.
Models
| 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 |
How to Use
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}
Note
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.
Citation
@misc{Citation,
title={AI-Corrupt Score for Anime Images},
author={narugo1992},
year={2023},
howpublished={\url{https://huggingface.co/deepghs/ai_image_corrupted}}
}