Repository for Scene Text Detection with Supervised Pyramid Context Network with tensorflow.

Overview

Scene-Text-Detection-with-SPCNET

Unofficial repository for [Scene Text Detection with Supervised Pyramid Context Network][https://arxiv.org/abs/1811.08605] with tensorflow.

参考代码

网络实现主要借鉴Keras版本的Mask-RCNN,训练数据接口参考了argman/EAST.论文作者在知乎的文章介绍SPCNet.

训练

1、训练数据准备

训练数据放在data/下,训练数据准备在data/icdar.py:

data

icdar2017

Annotaions //image_1.txt
JPEGImages //image_1.jpg
train.txt //存储训练图片的名称,例如:image_1

2、参数修改

修改./train.py中的学习率、batch、模型存储路径等参数,如果需要调整网络参数,在nets/config.py中修改。

3、执行训练

python train.py

代码运行环境:Python2.7 tensorflow-gpu1.13 单张1080Ti

测试

修改demo.py中的模型文件夹路径、测试图片路径,然后执行python demo.py

测试结果:论文中还有一些地方我也不确定,因此目前没有在公开数据集测试。值得注意的是,按照原文中的训练说明,最好在多卡上训练,请加大你的batch size.

值得注意的地方

1、global text segmentation(gts)的训练

计算gts训练时损失函数时,我采用的方法是将feature pyramid的各个level产生的gts分别与全局mask gt计算softmax loss,然后取平均作为Loss_gts。因为没找到与原文关于这一块的描述,因此可能是其他的计算方法:每个level准备不同的mask_gt、将多个level的gts预测融合计算loss等等。感兴趣的可以去问问作者或者自己试试。

2、实现Rescore 时gts的选取

计算predict box对应的pyramid level,然后选取对应的gts计算。还有一种思路是:融合P2,P3,P4,P5的gts,然后计算box rescore.

3、Bounding Box的生成

MASK RCNN中是先对输出的box进行阈值过滤以及NMS,然后将剩余的回归之后的box对应的rois送入mask branch计算mask,目的是减少计算量同时获得更准确的mask。SPCNet为了减小FP与FN,对Inference流程做了修改:先对模型输出的box与mask进行Rescore,然后经过threshold filter,再对剩下的mask求Bounding Box,然后利用Poly NMS减少重叠,输出剩下的。

在目前代码(nets/models.py utils.py)里:是先对模型输出的box与mask进行Rescore,然后经过threshold filter与NMS,再对剩下的mask求Bounding Box,然后直接输出。

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