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README.md
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@@ -32,6 +32,8 @@ It achieves the following results on the evaluation set:
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- Loss: 0.0937
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- Accuracy: 0.9654
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**Disclaimer**: This model wasn't made with generative images in mind! There is no generated image in the dataset used here, and it performs significantly worse on generative images, which will require another ViT model specifically trained on generative images. Here are the model's actual scores for generative images to give you an idea:
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- Loss: 0.3682 (↑ 292.95%)
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- Accuracy: 0.8600 (↓ 10.91%)
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- Loss: 0.0937
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- Accuracy: 0.9654
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**<u>New [07/30]</u>**: I created a new ViT model specifically to detect NSFW/SFW images for stable diffusion usage (read the disclaimer below for the reason): [**AdamCodd/vit-nsfw-stable-diffusion**](https://huggingface.co/AdamCodd/vit-nsfw-stable-diffusion).
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**Disclaimer**: This model wasn't made with generative images in mind! There is no generated image in the dataset used here, and it performs significantly worse on generative images, which will require another ViT model specifically trained on generative images. Here are the model's actual scores for generative images to give you an idea:
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- Loss: 0.3682 (↑ 292.95%)
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- Accuracy: 0.8600 (↓ 10.91%)
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