Linear evaluation protocols

[donglao]

Greetings,

I have a question regarding linear evaluation. It is my understanding that during evaluation, the model loads weights from both pre-trained encoder and pre-trained Deeplab head (that predicts saliency), then re-initialize and finetunes the last linear layer of Deeplab for semantic segmentation. When comparing with MOCO-V2 pre-trained weights, how do you initialize weights for the Deeplab head?

Thanks, Lao

[wvangansbeke]

Hi @donglao,

I believe the discussion in issue #7 should be helpful (and possibly issue #19). In short, we only trained a 1x1 conv on top of a dilated ResNet backbone since MoCo does not pretrain a decoder. This gives quite good results.

[donglao]

Thank you for clarification

On Fri, Jan 14, 2022, 6:30 AM Wouter Van Gansbeke @.***> wrote:

Hi @donglao https://github.com/donglao,

I believe the discussion in issue #7 https://github.com/wvangansbeke/Unsupervised-Semantic-Segmentation/issues/7 should be helpful (and possibly issue #19 https://github.com/wvangansbeke/Unsupervised-Semantic-Segmentation/issues/19). In short, we only trained a 1x1 conv on top of a dilated ResNet backbone since MoCo does pretrain a decoder. This gives quite good results.

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