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--- |
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license: apache-2.0 |
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library_name: timm |
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--- |
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# WD SwinV2 Tagger v3 |
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Supports ratings, characters and general tags. |
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Trained using https://github.com/SmilingWolf/JAX-CV. |
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TPUs used for training kindly provided by the [TRC program](https://sites.research.google/trc/about/). |
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## Dataset |
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Last image id: 7220105 |
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Trained on Danbooru images with IDs modulo 0000-0899. |
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Validated on images with IDs modulo 0950-0999. |
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Images with less than 10 general tags were filtered out. |
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Tags with less than 600 images were filtered out. |
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## Validation results |
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`v2.0: P=R: threshold = 0.2653, F1 = 0.4541` |
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`v1.0: P=R: threshold = 0.2521, F1 = 0.4411` |
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## What's new |
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Model v2.0/Dataset v3: |
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Trained for a few more epochs. |
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Used tag frequency-based loss scaling to combat class imbalance. |
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Model v1.1/Dataset v3: |
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Amended the JAX model config file: add image size. |
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No change to the trained weights. |
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Model v1.0/Dataset v3: |
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More training images, more and up-to-date tags (up to 2024-02-28). |
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Now `timm` compatible! Load it up and give it a spin using the canonical one-liner! |
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ONNX model is compatible with code developed for the v2 series of models. |
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The batch dimension of the ONNX model is not fixed to 1 anymore. Now you can go crazy with batch inference. |
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Switched to Macro-F1 to measure model performance since it gives me a better gauge of overall training progress. |
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# Runtime deps |
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ONNX model requires `onnxruntime >= 1.17.0` |
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# Inference code examples |
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For timm: https://github.com/neggles/wdv3-timm |
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For ONNX: https://huggingface.co/spaces/SmilingWolf/wd-tagger |
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For JAX: https://github.com/SmilingWolf/wdv3-jax |
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## Final words |
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Subject to change and updates. |
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Downstream users are encouraged to use tagged releases rather than relying on the head of the repo. |
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