About the performance

[hengshan123]

Thank you for the excellent work, i use your pretrained model gca-dist and test in composition-1k dataset, but i get the sad, mse like bellow, it's a little different from the paper
[03-12 15:15:46] INFO: TEST NUM: 1000
[03-12 15:15:46] INFO: MSE: 0.010313104958540355
[03-12 15:15:46] INFO: SAD: 37.83985428515623

[Yaoyi-Li]

I checked every steps again and it works fine for me.
[03-13 02:22:57] INFO: TEST NUM: 1000
[03-13 02:22:57] INFO: MSE: 0.009076512024573515
[03-13 02:22:57] INFO: SAD: 35.278531444580096

Did you generate merged images by the code Composition_code.py provided in Adobe dataset and follow their image order test_bg_names.txt/test_fg_names.txt?

[xiezhang666]

Thanks for your excelent job. I use your pretrained model gca-dist and test in composition-1k dataset as hengshan123's talk. here is my result:

['model-600238_1920_13.png'] 8.949408203125
[04-08 18:23:52] INFO: TEST NUM: 1000
[04-08 18:23:52] INFO: MSE: 0.010313099013678589
[04-08 18:23:52] INFO: SAD: 37.83984849536125

I merged Adobe dataset following huochaitiantang's sciprt. (https://github.com/huochaitiantang/pytorch-deep-image-matting/blob/master/tools/composite.py)

[Yaoyi-Li]

Thanks for your excelent job. I use your pretrained model gca-dist and test in composition-1k dataset as hengshan123's talk. here is my result:

['model-600238_1920_13.png'] 8.949408203125
[04-08 18:23:52] INFO: TEST NUM: 1000
[04-08 18:23:52] INFO: MSE: 0.010313099013678589
[04-08 18:23:52] INFO: SAD: 37.83984849536125

I merged Adobe dataset following huochaitiantang's sciprt. (https://github.com/huochaitiantang/pytorch-deep-image-matting/blob/master/tools/composite.py)

I tried this script and I can reproduce your result. Maybe hengshan123 also used this one. But it is different from how Adobe merge their Composition-1k testing set. So don't use this code to merge your data for testing.
I have uploaded the composition_code.py and it uses the composition function provided by Adobe. You can try it.