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Trained using https://github.com/SmilingWolf/JAX-CV. TPUs used for training kindly provided by the TRC program. Last image id: 7220105 Trained on Danbooru images with IDs modulo 0000-0899. Validated on images with IDs modulo 0950-0999. Images with less than 10 general tags were filtered out. Tags with less than 600 images were filtered out. Model v2.0/Dataset v3: Trained for a few more epochs. Used tag frequency-based loss scaling to combat class imbalance.

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Trained using https://github.com/SmilingWolf/JAX-CV. TPUs used for training kindly provided by the TRC program. Last image id: 7220105 Trained on Danbooru images with IDs modulo 0000-0899. Validated on images with IDs modulo 0950-0999. Images with less than 10 general tags were filtered out. Tags with less than 600 images were filtered out. Model v1.0/Dataset v3: More training images, more and up-to-date tags (up to 2024-02-28). Now `timm` compatible! Load it up and give it a spin using the canonical one-liner! ONNX model is compatible with code developed for the v2 series of models. The batch dimension of the ONNX model is not fixed to 1 anymore. Now you can go crazy with batch inference. Switched to Macro-F1 to measure model performance since it gives me a better gauge of overall training progress.

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Trained using https://github.com/SmilingWolf/JAX-CV. TPUs used for training kindly provided by the TRC program. Last image id: 7220105 Trained on Danbooru images with IDs modulo 0000-0899. Validated on images with IDs modulo 0950-0999. Images with less than 10 general tags were filtered out. Tags with less than 600 images were filtered out. `v2.0: P=R: threshold = 0.2653, F1 = 0.4541` `v1.0: P=R: threshold = 0.2521, F1 = 0.4411`

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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wd-vit-tagger-v3

SmilingWolf

Trained using https://github.com/SmilingWolf/JAX-CV. TPUs used for training kindly provided by the TRC program. Last image id: 7220105 Trained on Danbooru images with IDs modulo 0000-0899. Validated on images with IDs modulo 0950-0999. Images with less than 10 general tags were filtered out. Tags with less than 600 images were filtered out. Model v2.0/Dataset v3: Trained for a few more epochs. Used tag frequency-based loss scaling to combat class imbalance.

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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