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xuebinqin / U-2-Net

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
Apache License 2.0
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Improving results of foreground segmentation by using U2net #337

Open thebeautyofcoding opened 1 year ago

thebeautyofcoding commented 1 year ago

Hey, Thanks for developing U-2-Net. It works super fast and pretty well. I am currently training it with 50k images ( DUTS- + COCO-dataset). My train loss is around 0.26 at the moment. Although the foreground objects get extracted efficiently. The results are far from perfect. Do you think I can increase quality by further training and reducing the train loss? How can I get perfect results, such as the people from remove.bg? I tried using CascadePSP as a post process step. The results are perfect, but CascadePSP is just way too slow. I need faster processing. I wonder how remove.bg can achieve perfect results with super quick processing. Are there other deep learning models that are faster than CascadePSP? Or is it possible to get better results from U-2-Net? Thank you so much for your insane amount of work and your amazing achievements. If you want to see some of my results, please check my post on stackoverflow. Thank you so much :)

rakesh-reddy95 commented 1 year ago

Try training with a combined dataset of Matting and SOD. This works well.