Image per-pixel Scene labeling output issue (using FCN-32s Semantic Segmentation)

问题

I'm looking for a way that, given an input image and a neural network, it will output a labeled class for each pixel in the image (sky, grass, mountain, person, car etc).

I've set up Caffe (the future-branch) and successfully run the FCN-32s Fully Convolutional Semantic Segmentation on PASCAL-Context model. However, I'm unable to produce clear labeled images with it.

Images that visualizes my problem:
Input image

ground truth

And my result:

This might be some resolution issue. Any idea of where I'm going wrong?

回答1:

It seems like the 32s model is making large strides and thus working at a coarse resolution. Can you try the 8s model that seems to perform less resolution reduction.
Looking at J Long, E Shelhamer, T Darrell Fully Convolutional Networks for Semantic Segmentation, CVPR 2015 (especially at figure 4) it seems like the 32s model is not designed for capturing fine details of the segmentation.

来源：https://stackoverflow.com/questions/32451934/image-per-pixel-scene-labeling-output-issue-using-fcn-32s-semantic-segmentation