durandtibo / wildcat.pytorch

PyTorch implementation of "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation", CVPR 2017
MIT License
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implementation for the segmentation? #1

Open apple2373 opened 7 years ago

apple2373 commented 7 years ago

Hi,

I could not find the implementation for the segmentation and its evaluation.... Where is it?

Best, Satoshi

ghost commented 7 years ago

@apple2373 this may be helpful for you https://github.com/lucasb-eyer/pydensecrf

KeyKy commented 7 years ago

I also want to learn the segmentation code~

Ferenas commented 4 years ago

Actually you have to implement your seg masks based on the paper by yourself. After trained from datasets, the net can be directluy used for the mask Inference. The output of the class-wise pooling (attention, the code writer links the class-wise and spatial-wise into one pooling module by Sequentail, so you have to split them in the net to get the class-wise pooling, whose outputs are 2077 in VOC12) , then you choose the maxscore in each channel(classes) to generate the final seg masks

liuyue718 commented 4 years ago

Hello, the segmentation mask I got is 1/32 of the size of the input image. To get the segmentation result of the same size as the input image, do I need to directly sample the original size of the image?Isn't that a lot of resolution lost