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All Projects → zacario-li → Fast-SCNN_pytorch

zacario-li / Fast-SCNN_pytorch

Licence: MIT license
A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network(PyTorch >= 1.4)

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python
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Labels

pytorchsegmentationsemantic-segmentationfast-scnn

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Fast-SCNN

基于如下论文复现该网络

fast-scnn paper

网络架构

network arch

网络结构详情

Input Block t c n s
1024 × 2048 × 3 Conv2D - 32 1 2
512 × 1024 × 32 DSConv - 48 1 2
256 × 512 × 48 DSConv - 64 1 2
128 × 256 × 64 bottleneck 6 64 3 2
64 × 128 × 64 bottleneck 6 96 3 2
32 × 64 × 96 bottleneck 6 128 3 1
32 × 64 × 128 PPM - 128 - -
32 × 64 × 128 FFM - 128 - -
128 × 256 × 128 DSConv - 128 2 1
128 × 256 × 128 Conv2D - nums of classes 1 1

Table 1

Input Operator Output
h × w × c Conv2D 1/1, f h × w × tc
h × w × tc DWConv 3/s, f h/s x w/s x tc
h/s x w/s x tc Conv2D 1/1, − h/s x w/s x c'

Table 2

使用方法

基于540x540分辨率，在voc2012数据集上训练了一个权重，各位可以用这个来初始化，节约一些训练时间
https://pan.baidu.com/s/17_pGbpkI4tx8eOMZFS73fA password:v98k

数据准备

准备原图文件夹 img，准备label图文件夹 label，然后准备好train.txt 和 val.txt，放在同一级目录下，结构如下：
dataset
|train.txt
|val.txt
└--img
| | image1.jpg
| | image2.jpg
└--label
| image1.png
| image2.png

train.txt/val.txt 格式如下：
image1.jpg image1.png
image2.jpg image2.png
......

训练脚本准备

可以按照上述结构准备数据并训练

TODO

  • Training & Validate functions
  • Tensorboard 记录
  • resume training 脚本
  • VOC2012数据集训练脚本
  • 多GPU训练
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