Review: FCN (Semantic Segmentation)

阿新 • • 發佈：2018-12-28

1. From Image Classification to Semantic Segmentation

In classification, conventionally, an input image is downsized and goes through the convolution layers and fully connected (FC) layers, and output one predicted label for the input image, as follows:

Imagine we turn the FC layers into 1×1 convolutional layers:

And the image is not downsized, the output will not a single label. Instead, the output has a size smaller than the input image (due to the max pooling):

If we upsample the output above, then we can calculate the pixelwise output (label map) as below:

Review: FCN (Semantic Segmentation)

1. From Image Classification to Semantic SegmentationIn classification, conventionally, an input image is downsized and goes through the convolution layers

FCN筆記（Fully Convolutional Networks for Semantic Segmentation）

width height training 註意 die str 指標 his repl FCN筆記（Fully Convolutional Networks for Semantic Segmentation）（1）FCN做的主要操作 (a)將之前分類網絡的全連接

語義分割(semantic segmentation) 常用神經網絡介紹對比-FCN SegNet U-net DeconvNet，語義分割,簡單來說就是給定一張圖片,對圖片中的每一個像素點進行分類；目標檢測只有兩類,目標和非目標，就是在一張圖片中找到並用box標註出所有的目標.

avi projects div 般的 ict 中間接受 img dense from：https://blog.csdn.net/u012931582/article/details/70314859 2017年04月21日 14:54:10 閱讀數：4369

Review: FCN (Semantic Segmentation)

1. From Image Classification to Semantic Segmentation

Review: FCN (Semantic Segmentation)

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Review: FCN (Semantic Segmentation)

1. From Image Classification to Semantic Segmentation

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