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BADBADBADBOY
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Description

基于pytorch的ocr算法库,包括 psenet, pan, dbnet, sast , crnn

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基于pytorch的OCR库

这里会有这个项目的代码详解和我的一些ocr经验和心得,我会慢慢更新,有兴趣可以看看,希望可以帮到新接触ocr的童鞋CSDN博客


最近跟新: - 2020.12.22 更新CRNN+CTCLoss+CenterLoss训练 - 2020.09.18 更新文本检测说明文档 - 2020.09.12 更新DB,pse,pan,sast,crnn训练测试代码和预训练模型


目前已完成:

  • [x] DBnet 论文链接
  • [x] PSEnet 论文链接
  • [x] PANnet 论文链接
  • [x] SASTnet 论文链接
  • [x] CRNN 论文链接


    接下来计划:

  • [x] 模型转onnx及调用测试

  • [x] 模型压缩(剪枝)

  • [ ] 模型压缩(量化)

  • [x] 模型蒸馏

  • [x] tensorrt部署

  • [ ] 训练通用化ocr模型

  • [ ] 结合chinese_lite进行部署

  • [ ] 手机端部署


    检测模型效果(实验中)

训练只在ICDAR2015文本检测公开数据集上,算法效果如下: |模型|骨干网络|precision|recall|Hmean|下载链接| |-|-|-|-|-|-| |DB|ResNet507*7|85.88%|79.10%|82.35%|下载链接(code:fxw6)| |DB|ResNet5033|86.51%|80.59%|83.44%|下载链接(code:fxw6)| |DB|MobileNetV3|82.89%|75.83%|79.20%|下载链接(code:fxw6)| |SAST|ResNet50_77|85.72%|78.38%|81.89%|下载链接(code:fxw6)| |SAST|ResNet503*3|86.67%|76.74%|81.40%|下载链接(code:fxw6)| |PSE|ResNet5077|84.10%|80.01%|82.01%|下载链接(code:fxw6)| |PSE|ResNet50_33|82.56%|78.91%|80.69%|下载链接(code:fxw6)| |PAN|ResNet187*7|81.80%|77.08%|79.37%|下载链接(code:fxw6)| |PAN|ResNet183*3|83.78%|75.15%|79.23%|下载链接(code:fxw6)|


模型压缩剪枝效果

这里使用mobilev3作为backbone,在icdar2015上测试结果,未压缩模型初始大小为2.4M.

  1. 对backbone进行压缩

|模型|pruned method|ratio|model size(M)|precision|recall|Hmean |-|-|-|-|-|-|-| |DB|no|0|2.4|84.04%|75.34%|79.46%|
|DB|backbone|0.5|1.9|83.74%|73.18%|78.10%| |DB|backbone|0.6|1.58|84.46%|69.90%|76.50%|

  1. 对整个模型进行压缩

|模型|pruned method|ratio|model size(M)|precision|recall|Hmean| |-|-|-|-|-|-|-| |DB|no|0|2.4|85.70%|74.77%|79.86%| |DB|total|0.6|1.42|82.97%|75.10%|78.84%| |DB|total|0.65|1.15|85.14%|72.84%|78.51%|


模型蒸馏

|模型|teacher|student|model size(M)|precision|recall|Hmean|improve(%)| |-|-|-|-|-|-|-|-| |DB|no|mobilev3|2.4|85.70%|74.77%|79.86%|-| |DB|resnet50|mobilev3|2.4|86.37%|77.22%|81.54%|1.68| |DB|no|mobilev3|1.42|82.97%|75.10%|78.84%|-| |DB|resnet50|mobilev3|1.42|85.88%|76.16%|80.73%|1.89| |DB|no|mobilev3|1.15|85.14%|72.84%|78.51%|-| |DB|resnet50|mobilev3|1.15|85.60%|74.72%|79.79%|1.28|


文档教程


文本检测效果



有问题及交流加微信

微信号:-fxwispig-


参考

  • https://github.com/PaddlePaddle/PaddleOCR
  • https://github.com/whai362/PSENet
  • https://github.com/whai362/pan_pp.pytorch
  • https://github.com/WenmuZhou/PAN.pytorch
  • https://github.com/xiaolai-sqlai/mobilenetv3
  • https://github.com/BADBADBADBOY/DBnet-lite.pytorch
  • https://github.com/BADBADBADBOY/Psenet_v2
  • https://github.com/BADBADBADBOY/pse-lite.pytorch

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