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README.md
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@@ -29,25 +29,16 @@ It seems like using random crops helped the model to generalize better, however,
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## Performance comparison
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We did a small
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| real_1.jpg | ai (99%) | human (99%) | artificial (100%) | REAL (98%) | ai (55%) |
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| real_2.jpg | ai (88%) | human (100%) | artificial (100%) | REAL (100%) | real (85%) |
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| real_3.jpg | ai (95%) | human (96%) | artificial (100%) | REAL (100%) | real (97%) |
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| real_4.jpg | real (90%) | human (100%) | artificial (97%) | REAL (100%) | real (94%) |
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| real_5.jpg | ai (75%) | human (100%) | human (57%) | REAL (100%) | real (100%)|
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| real_6.jpg | ai (89%) | human (98%) | human (100%) | REAL (100%) | real (99%) |
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| **Accuracy:**| 50% | 50% | 58% | **75%** | **75%** |
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## Usage
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## Performance comparison
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We did a small eval test with ~5000 images on the current available AI image detectors. Note that these models were not specificly trained on anime images.
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| Model | Accuracy |
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| mmanikanta/VIT_AI_image_detector | 79,65% |
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| **legekka/AI-Anime-Image-Detector-ViT** | **94,68%** |
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| *legekka/AI-Anime-Image-Detector-HD-ViT WIP* | *94,26%* |
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| Organika/sdxl-detector | 43,29% |
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| umm-maybe/AI-image-detector | 75,45% |
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| Nahrawy/AIorNot | 64,74% |
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## Usage
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