Add quantitative comparison of this model for dynamic input shapes and previous ones for input with fixed shapes.
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README.md
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> This model was trained on arbitrary shapes (256x256 ~ 2304x2304) and shows great robustness on inputs with any shape.
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### Performance
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> How it looks when compared with BiRefNet-matting
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For performance of different epochs, check the [eval_results-xxx folder for it](https://drive.google.com/drive/u/0/folders/1wSOe0m98YJBRnOefQrC6iefFmeUPtVhn) on my google drive.
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> This model was trained on arbitrary shapes (256x256 ~ 2304x2304) and shows great robustness on inputs with any shape.
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### Performance
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> How it looks when compared with BiRefNet-matting and BiRefNet_HR-matting (fixed resolution, e.g., 1024x1024, 2048x2048).
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For performance of different epochs, check the [eval_results-xxx folder for it](https://drive.google.com/drive/u/0/folders/1wSOe0m98YJBRnOefQrC6iefFmeUPtVhn) on my google drive.
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