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            High quality 5000 images from danbooru. They were shuffled and split into train:eval at 4500:500. (Same as p1atdev/siglip-tagger-test-2)
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            ## Training procedure
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            ### Training hyperparameters
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            The following hyperparameters were used during training:
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            High quality 5000 images from danbooru. They were shuffled and split into train:eval at 4500:500. (Same as p1atdev/siglip-tagger-test-2)
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            |Name|Description|
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            |-|-|
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            |Images count|5000|
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            |Supported tags|9517 general tags. Character and rating tags are not included. See all labels in [config.json](config.json)|
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            |Image rating|4000 for `general` and 1000 for `sensitive,questionable,explicit`|
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            |Copyright tags|`original` only|
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            |Image score range (on search)|min:10, max150|
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            ## Training procedure
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            - Loss function: AsymmetricLossOptimized ([Asymmetric Loss](https://github.com/Alibaba-MIIL/ASL))
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              - `gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False`
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            ### Training hyperparameters
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            The following hyperparameters were used during training:
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