SmilingWolf

--- license: apache-2.0 library_name: timm ---

Trained using https://github.com/SmilingWolf/JAX-CV. TPUs used for training kindly provided by the TRC program. Dataset 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. Validation results `v2.0: P=R: threshold = 0.2614, F1 = 0.4402` `v1.0: P=R: threshold = 0.2547, F1 = 0.4278` What's new Model v2.0/Dataset v3: Trained for a few more epochs. Used tag frequency-based loss scaling to combat class imbalance. Model v1.1/Dataset v3: Amended the JAX model config file: add image size. No change to the trained weights. 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. Runtime deps ONNX model requires `onnxruntime >= 1.17.0` Inference code examples For timm: https://github.com/neggles/wdv3-timm For ONNX: https://huggingface.co/spaces/SmilingWolf/wd-tagger For JAX: https://github.com/SmilingWolf/wdv3-jax Final words Subject to change and updates. Downstream users are encouraged to use tagged releases rather than relying on the head of the repo.

Trained using https://github.com/SmilingWolf/JAX-CV. TPUs used for training kindly provided by the TRC program. Dataset Last image id: 7220105 Trained on Danbooru images with IDs modulo 0000-0899. ...

Trained using https://github.com/SmilingWolf/SW-CV-ModelZoo. TPUs used for training kindly provided by the TRC program. Dataset Last image id: 5944504 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. Validation results `P=R: threshold = 0.3710, F1 = 0.6862` Final words Subject to change and updates. Downstream users are encouraged to use tagged releases rather than relying on the head of the repo.

Trained using https://github.com/SmilingWolf/SW-CV-ModelZoo. TPUs used for training kindly provided by the TRC program. Dataset Last image id: 5944504 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. Validation results `P=R: threshold = 0.3771, F1 = 0.6911` Paper `MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models` Final words Subject to change and updates. Downstream users are encouraged to use tagged releases rather than relying on the head of the repo.

Trained using https://github.com/SmilingWolf/SW-CV-ModelZoo. TPUs used for training kindly provided by the TRC program. Dataset Last image id: 5944504 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. Validation results `v2.0: P=R: threshold = 0.3771, F1 = 0.6854` What's new Model v2.1/Dataset v2: Re-exported to work around an ONNXRuntime v1.17.1 bug. Bumped the minimum ONNXRuntime version to `>= 1.17.0`. Now `timm` compatible! Load it up and give it a spin using the canonical one-liner! Exported to `msgpack` for compatibility with the JAX-CV codebase. The batch dimension of the ONNX model is not fixed to 1 anymore. Now you can go crazy with batch inference. No change to the trained weights themselves. There might be small prediction discrepancies across frameworks due to implementation details. Runtime deps ONNX model requires `onnxruntime >= 1.17.0` Final words Subject to change and updates. Downstream users are encouraged to use tagged releases rather than relying on the head of the repo.