ZhengPeng7 commited on
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Add quantitative comparison of this model for dynamic input shapes and previous ones for input with fixed shapes.

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  1. README.md +5 -1
README.md CHANGED
@@ -20,7 +20,11 @@ license: mit
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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 (fixed 1024x1024 resolution) -- greater than BiRefNet-matting and BiRefNet_HR-matting on the reserved validation sets (DIS-VD and TE-P3M-500-NP).
 
 
 
 
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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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+ ![comparison_TE-AM-2k](https://cdn-uploads.huggingface.co/production/uploads/63e4977454f51ea342d54814/Mvkf1AWKIfcnpLC6sIgjd.png)
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+ ![comparison_TE-P3M-500-NP](https://cdn-uploads.huggingface.co/production/uploads/63e4977454f51ea342d54814/CuACZhbWNN18qIzV5Lzce.png)
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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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