图像每像素场景标记输出问题（使用FCN-32s语义分段） [英] Image per-pixel Scene labeling output issue (using FCN-32s Semantic Segmentation)

问题描述

我正在寻找一种方法，给定输入图像和神经网络，它将为图像中的每个像素（天空，草地，山脉，人，汽车等）输出标记的类。

我已经设置了Caffe（未来分支机构）并成功运行。

查看 J Long，E Shelhamer，T Darrell 用于语义分割的完全卷积网络，CVPR 2015 （特别是图4）似乎32s模型不是为捕获细分细节而设计的。

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?

解决方案

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.

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