AIGC-clf

Contest solution for distinguish AIGC images

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Contest Page: https://xihe.mindspore.cn/competition/mindcon23-aigc-img/0/introduction
Team Name: 你这图保真吗
Submission repo: https://xihe.mindspore.cn/projects/kahsolt/AIGC-clf
Final Score/Rank: 71%/7th, no award :( (refer to SOLUTIONS.md for top solutions)

Results

⚪ migrated pretrained apps

method	pAcc	comment
cheaty	99.897224%	detect image h/w==512
AI-generated-art-classifier	88.69476%	resnet18 clf
AI-image-detector	78.82837%	swin clf
sdxl-detector	56.62898%	swin clf
sd-vae-ft-ema	70.7086%	aekl + clf by loss_diff

⚪ finetuned apps

ℹ following apps are based on AI-generated-art-classifier and sd-vae-ft-ema

• run python predict.py --app <app_name>

app	input_size	pAcc	pAcc (vote=5)	pAcc (vote=7)	comment
resnet	224	85.71429%	85.50874%	85.09764%	migrated baseline
resnet_ft	224	98.86948%	99.07503%	99.07503%	finetune
resnet_hf	80	87.97533%	93.01131%	93.62795%	retrain from pretrained
aekl_clf	256	95.67456%	95.88900%	96.60843%	finetune from pretrained

⚪ ensemble app

• run python predict_ensemble.py --votes 7

app	pAcc	pAcc (vote=7)
resnet_ft	96.40288%	97.22508%
resnet_hf	89.31141%	96.71120%
aekl_clf	96.50565%	97.43063%
ensembled	98.15005%	99.38335%

Quickstart

⚪ run pretrained apps

• install PyTorch
• pip install -r requirements.txt
• run python predict.py --app <app_name> and see out/result.txt

⚪ finetune the apps

• link the contest dataset to data
• run the following train scripts
  • python train_resnet_ft.py
  • python train_resnet_hf.py
  • python train_aekl_clf.py

references

• AI-detectors
  • https://huggingface.co/Organika/sdxl-detector
  • https://huggingface.co/umm-maybe/AI-image-detector
  • https://huggingface.co/artfan123/resnet-18-finetuned-ai-art
    • https://huggingface.co/spaces/artfan123/AI-generated-art-classifier
• SD VAEs
  • https://huggingface.co/stabilityai/sdxl-vae
  • https://huggingface.co/stabilityai/sd-vae-ft-mse
  • https://huggingface.co/stabilityai/sd-vae-ft-ema
  • https://huggingface.co/stabilityai/sd-vae-ft-mse-original
• dataset
  • JourneyDB: https://huggingface.co/datasets/JourneyDB/JourneyDB
• 比赛材料仓库
  • 训练样例: https://source-xihe-mindspore.osinfra.cn/champagne11/mindcv_twoclass.git
  • 数据样例: https://source-xihe-mindspore.osinfra.cn/drizzlezyk/ai-real.git
  • mindspore: https://www.mindspore.cn/docs/zh-CN/r2.2/index.html
  • mindcv: https://mindspore-lab.github.io/mindcv/

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by Armit 2024/01/03