Skip to content

P
PaddleOCR

PaddlePaddle / PaddleOCR
大约 1 年 前同步成功

通知 1528
Star 32962
Fork 6643
P
PaddleOCR
提交 37f5ff96 编写于 作者: Y yukavio
浏览文件
操作

Merge remote-tracking branch 'upstream/develop' into develop 

update
上级 dc0aea4e edd4c33f
展开全部 隐藏空白更改
内联 并排
Showing with 9747 addition and 340 deletion
README.md
浏览文件 @ 37f5ff96
......@@ -4,6 +4,7 @@
PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
**近期更新**
- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型，媲美商业效果。[模型下载](#模型下载)
- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./doc/doc_ch/FAQ.md)
- 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./doc/doc_ch/whl.md)
- 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
......@@ -14,51 +15,69 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
## 特性
- 超轻量级中文OCR模型，总模型仅8.6M
- 单模型支持中英文数字组合识别、竖排文本识别、长文本识别
- 检测模型DB（4.1M）+识别模型CRNN（4.5M）
- 实用通用中文OCR模型
- 多种预测推理部署方案，包括服务部署和端侧部署
- 多种文本检测训练算法，EAST、DB、SAST
- 多种文本识别训练算法，Rosetta、CRNN、STAR-Net、RARE、SRN
- PPOCR系列高质量预训练模型，媲美商业效果
- 超轻量ppocr_mobile系列：检测（2.6M）+方向分类器（0.9M
）+ 识别（4.6M）= 8.1M
- 通用ppocr_server系列：检测（47.2M）+方向分类器（0.9M）+ 识别（107M）= 155.1M
- 超轻量压缩ppocr_mobile_slim系列：(coming soon)
- 支持中英文数字组合识别、竖排文本识别、长文本识别
- 支持多语言识别：韩语、日语、德语、法语 (coming soon)
- 支持用户自定义训练，提供丰富的预测推理部署方案
- 支持PIP快速安装使用
- 可运行于Linux、Windows、MacOS等多种系统
## 快速体验
## 效果展示
<div align="center">
<img src="doc/imgs_results/11.jpg" width="800">
<img src="doc/imgs_results/1101.jpg" width="800">
<img src="doc/imgs_results/1103.jpg" width="800">
</div>
上图是超轻量级中文OCR模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
上图是通用ppocr_server模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
## 快速体验
- PC端：超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
- 超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
- 移动端DEMO体验(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)：[安装包二维码获取地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
- 移动端：[安装包DEMO下载地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)，Android手机也可以直接扫描下面二维码安装体验。
Android手机也可以扫描下面二维码安装体验。
<div align="center">
<img src="./doc/ocr-android-easyedge.png" width = "200" height = "200" />
</div>
- 代码体验：可以直接进入[快速安装](./doc/doc_ch/installation.md)
<a name="模型下载"></a>
## PP-OCR 1.1系列模型列表（9月17日更新）
| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 | |
| ------------ | --------------- | ----------------|---- | ---------- | -------- | ---- |
| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_train.tar) |[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) | |
| 中英文通用OCR模型（155.1M） |ch_ppocr_server_v1.1_xx|服务器端 |[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) |[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_train.tar) |[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) | |
| 中英文超轻量压缩OCR模型 | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |即将开源 |即将开源|即将开源| | ||
更多V1.1版本模型下载，可以参考[OCR1.1模型列表](./doc/doc_ch/models_list.md)
## PP-OCR 1.0系列模型列表（7月16日更新）
## 中文OCR模型列表
| 模型简介 | 模型名称 | 检测模型 | 识别模型 | 支持空格的识别模型 | |
| ------------ | ---------------------- | -------- | ---------- | -------- | ---- |
| 超轻量中英文OCR模型（8.6M） | chinese_db_crnn_mobile_xx |[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar) | |
|通用中文OCR模型（212M）|chinese_db_crnn_server_xx|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)| |
|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址|
|-|-|-|-|-|
|chinese_db_crnn_mobile|超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
## 文档教程
- [快速安装](./doc/doc_ch/installation.md)
- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
- 算法介绍
- [文本检测](#文本检测算法)
- [文本识别](#文本识别算法)
- [文本检测](./doc/doc_ch/algorithm_overview.md)
- [文本识别](./doc/doc_ch/algorithm_overview.md)
- PP-OCR (coming soon)
- 模型训练/评估
- [文本检测](./doc/doc_ch/detection.md)
- [文本识别](./doc/doc_ch/recognition.md)
- [yml参数配置文件介绍](./doc/doc_ch/config.md)
- [中文OCR训练预测技巧](./doc/doc_ch/tricks.md)
- 预测部署
- [基于Python预测引擎推理](./doc/doc_ch/inference.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
......@@ -72,10 +91,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
- [常用数据标注工具](./doc/doc_ch/data_annotation.md)
- [常用数据合成工具](./doc/doc_ch/data_synthesis.md)
- 效果展示
- [超轻量级中文OCR效果展示](#超轻量级中文OCR效果展示)
- [通用中文OCR效果展示](#通用中文OCR效果展示)
- [支持空格的中文OCR效果展示](#支持空格的中文OCR效果展示)
- [效果展示](#效果展示)
- FAQ
- [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用21个问题](./doc/doc_ch/FAQ.md)
......@@ -85,104 +101,20 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [许可证书](#许可证书)
- [贡献代码](#贡献代码)
<a name="算法介绍"></a>
## 算法介绍
<a name="文本检测算法"></a>
### 1.文本检测算法
PaddleOCR开源的文本检测算法列表：
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] DB([paper](https://arxiv.org/abs/1911.08947))
- [x] SAST([paper](https://arxiv.org/abs/1908.05498))(百度自研)
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|
|-|-|-|-|-|-|
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)|
在Total-text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|
|-|-|-|-|-|-|
|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)|
**说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集共3w张数据，训练中文检测模型的相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
* 注： 上述DB模型的训练和评估，需设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化
PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./doc/doc_ch/detection.md)。
<a name="文本识别算法"></a>
### 2.文本识别算法
PaddleOCR开源的文本识别算法列表：
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研)
参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
|-|-|-|-|-|
|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
**说明：** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广，增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载，提取码: y3ry。
原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./doc/doc_ch/recognition.md)。
## 效果展示
<a name="超轻量级中文OCR效果展示"></a>
### 1.超轻量级中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<a name="效果展示"></a>
## 效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/1.jpg" width="800">
<img src="./doc/imgs_results/1102.jpg" width="800">
<img src="./doc/imgs_results/1104.jpg" width="800">
<img src="./doc/imgs_results/1105.jpg" width="800">
<img src="./doc/imgs_results/1110.jpg" width="800">
<img src="./doc/imgs_results/1112.jpg" width="800">
</div>
<a name="通用中文OCR效果展示"></a>
### 2.通用中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
</div>
<a name="支持空格的中文OCR效果展示"></a>
### 3.支持空格的中文OCR效果展示 [more](./doc/doc_ch/visualization.md)
<div align="center">
<img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
</div>
<a name="欢迎加入PaddleOCR技术交流群"></a>
## 欢迎加入PaddleOCR技术交流群
......
configs/det/det_mv3_db.yml
浏览文件 @ 37f5ff96
......@@ -24,6 +24,7 @@ Backbone:
function: ppocr.modeling.backbones.det_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: large
disable_se: true
Head:
function: ppocr.modeling.heads.det_db_head,DBHead
......
configs/det/det_mv3_db_v1.1.yml 已删除 100755 → 0
浏览文件 @ dc0aea4e
Global:
algorithm: DB
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/det_db/
save_epoch_step: 200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000]
train_batch_size_per_card: 16
test_batch_size_per_card: 16
image_shape: [3, 640, 640]
reader_yml: ./configs/det/det_db_icdar15_reader.yml
pretrain_weights: ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
checkpoints:
save_res_path: ./output/det_db/predicts_db.txt
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.det_model,DetModel
Backbone:
function: ppocr.modeling.backbones.det_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: large
disable_se: true
Head:
function: ppocr.modeling.heads.det_db_head,DBHead
model_name: large
k: 50
inner_channels: 96
out_channels: 2
Loss:
function: ppocr.modeling.losses.det_db_loss,DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
function: ppocr.optimizer,AdamDecay
base_lr: 0.001
beta1: 0.9
beta2: 0.999
PostProcess:
function: ppocr.postprocess.db_postprocess,DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
configs/rec/multi_languages/rec_en_lite_train.yml 0 → 100644
浏览文件 @ 37f5ff96
Global:
algorithm: CRNN
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/en_number
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
image_shape: [3, 32, 320]
max_text_length: 30
character_type: ch
character_dict_path: ./ppocr/utils/ic15_dict.txt
loss_type: ctc
distort: false
use_space_char: false
reader_yml: ./configs/rec/multi_languages/rec_en_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
Backbone:
function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: small
small_stride: [1, 2, 2, 2]
Head:
function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
encoder_type: rnn
SeqRNN:
hidden_size: 48
Loss:
function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
Optimizer:
function: ppocr.optimizer,AdamDecay
l2_decay: 0.00001
base_lr: 0.001
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay_warmup
warmup_minibatch: 1000
step_each_epoch: 6530
total_epoch: 500
configs/rec/multi_languages/rec_en_reader.yml 0 → 100755
浏览文件 @ 37f5ff96
TrainReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
num_workers: 8
img_set_dir: ./train_data
label_file_path: ./train_data/en_train.txt
EvalReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
img_set_dir: ./train_data
label_file_path: ./train_data/en_eval.txt
TestReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
configs/rec/multi_languages/rec_french_lite_train.yml 0 → 100755
浏览文件 @ 37f5ff96
Global:
algorithm: CRNN
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_french
save_epoch_step: 1
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
image_shape: [3, 32, 320]
max_text_length: 25
character_type: french
character_dict_path: ./ppocr/utils/french_dict.txt
loss_type: ctc
distort: true
use_space_char: false
reader_yml: ./configs/rec/multi_languages/rec_french_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
Backbone:
function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: small
small_stride: [1, 2, 2, 2]
Head:
function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
encoder_type: rnn
SeqRNN:
hidden_size: 48
Loss:
function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
Optimizer:
function: ppocr.optimizer,AdamDecay
l2_decay: 0.00001
base_lr: 0.001
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay
step_each_epoch: 254
total_epoch: 500
configs/rec/multi_languages/rec_french_reader.yml 0 → 100755
浏览文件 @ 37f5ff96
TrainReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
num_workers: 8
img_set_dir: ./train_data
label_file_path: ./train_data/french_train.txt
EvalReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
img_set_dir: ./train_data
label_file_path: ./train_data/french_eval.txt
TestReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
configs/rec/multi_languages/rec_ger_lite_train.yml 0 → 100755
浏览文件 @ 37f5ff96
Global:
algorithm: CRNN
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_german
save_epoch_step: 1
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
image_shape: [3, 32, 320]
max_text_length: 25
character_type: german
character_dict_path: ./ppocr/utils/german_dict.txt
loss_type: ctc
distort: true
use_space_char: false
reader_yml: ./configs/rec/multi_languages/rec_ger_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
Backbone:
function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: small
small_stride: [1, 2, 2, 2]
Head:
function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
encoder_type: rnn
SeqRNN:
hidden_size: 48
Loss:
function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
Optimizer:
function: ppocr.optimizer,AdamDecay
l2_decay: 0.00001
base_lr: 0.001
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay
step_each_epoch: 254
total_epoch: 500
configs/rec/multi_languages/rec_ger_reader.yml 0 → 100755
浏览文件 @ 37f5ff96
TrainReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
num_workers: 8
img_set_dir: ./train_data
label_file_path: ./train_data/de_train.txt
EvalReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
img_set_dir: ./train_data
label_file_path: ./train_data/de_eval.txt
TestReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
configs/rec/multi_languages/rec_japan_lite_train.yml 0 → 100755
浏览文件 @ 37f5ff96
Global:
algorithm: CRNN
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_japan
save_epoch_step: 1
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
image_shape: [3, 32, 320]
max_text_length: 25
character_type: japan
character_dict_path: ./ppocr/utils/japan_dict.txt
loss_type: ctc
distort: true
use_space_char: false
reader_yml: ./configs/rec/multi_languages/rec_japan_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
Backbone:
function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: small
small_stride: [1, 2, 2, 2]
Head:
function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
encoder_type: rnn
SeqRNN:
hidden_size: 48
Loss:
function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
Optimizer:
function: ppocr.optimizer,AdamDecay
l2_decay: 0.00001
base_lr: 0.001
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay
step_each_epoch: 254
total_epoch: 500
configs/rec/multi_languages/rec_japan_reader.yml 0 → 100755
浏览文件 @ 37f5ff96
TrainReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
num_workers: 8
img_set_dir: ./train_data
label_file_path: ./train_data/japan_train.txt
EvalReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
img_set_dir: ./train_data
label_file_path: ./train_data/japan_eval.txt
TestReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
configs/rec/multi_languages/rec_korean_lite_train.yml 0 → 100755
浏览文件 @ 37f5ff96
Global:
algorithm: CRNN
use_gpu: true
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_korean
save_epoch_step: 1
eval_batch_step: 2000
train_batch_size_per_card: 256
test_batch_size_per_card: 256
image_shape: [3, 32, 320]
max_text_length: 25
character_type: korean
character_dict_path: ./ppocr/utils/korean_dict.txt
loss_type: ctc
distort: true
use_space_char: false
reader_yml: ./configs/rec/multi_languages/rec_korean_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
Backbone:
function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
scale: 0.5
model_name: small
small_stride: [1, 2, 2, 2]
Head:
function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
encoder_type: rnn
SeqRNN:
hidden_size: 48
Loss:
function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
Optimizer:
function: ppocr.optimizer,AdamDecay
l2_decay: 0.00001
base_lr: 0.001
beta1: 0.9
beta2: 0.999
decay:
function: cosine_decay
step_each_epoch: 254
total_epoch: 500
configs/rec/multi_languages/rec_korean_reader.yml 0 → 100755
浏览文件 @ 37f5ff96
TrainReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
num_workers: 8
img_set_dir: ./train_data
label_file_path: ./train_data/korean_train.txt
EvalReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
img_set_dir: ./train_data
label_file_path: ./train_data/korean_eval.txt
TestReader:
reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
deploy/cpp_infer/readme.md
浏览文件 @ 37f5ff96
......@@ -193,6 +193,9 @@ make -j
sh tools/run.sh
```
* 若需要使用方向分类器，则需要将`tools/config.txt`中的`use_angle_cls`参数修改为1，表示开启方向分类器的预测。
最终屏幕上会输出检测结果如下。
<div align="center">
......
deploy/cpp_infer/readme_en.md
浏览文件 @ 37f5ff96
......@@ -162,7 +162,7 @@ inference/
sh tools/build.sh
```
具体地，`tools/build.sh`中内容如下。
Specifically, the content in `tools/build.sh` is as follows.
```shell
OPENCV_DIR=your_opencv_dir
......@@ -201,6 +201,8 @@ make -j
sh tools/run.sh
```
* If you want to orientation classifier to correct the detected boxes, you can set `use_angle_cls` in the file `tools/config.txt` as 1 to enable the function.
The detection results will be shown on the screen, which is as follows.
<div align="center">
......
deploy/cpp_infer/tools/config.txt
浏览文件 @ 37f5ff96
......@@ -15,7 +15,7 @@ det_model_dir ./inference/det_db
# cls config
use_angle_cls 0
cls_model_dir ../inference/cls
cls_model_dir ./inference/cls
cls_thresh 0.9
# rec config
......
deploy/hubserving/ocr_det/params.py
浏览文件 @ 37f5ff96
......@@ -13,7 +13,7 @@ def read_params():
#params for text detector
cfg.det_algorithm = "DB"
cfg.det_model_dir = "./inference/ch_det_mv3_db/"
cfg.det_model_dir = "./inference/ch_ppocr_mobile_v1.1_det_infer/"
cfg.det_max_side_len = 960
#DB parmas
......
deploy/hubserving/ocr_rec/params.py
浏览文件 @ 37f5ff96
......@@ -28,7 +28,7 @@ def read_params():
#params for text recognizer
cfg.rec_algorithm = "CRNN"
cfg.rec_model_dir = "./inference/ch_rec_mv3_crnn/"
cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v1.1_rec_infer/"
cfg.rec_image_shape = "3, 32, 320"
cfg.rec_char_type = 'ch'
......
deploy/hubserving/ocr_system/params.py
浏览文件 @ 37f5ff96
......@@ -13,7 +13,7 @@ def read_params():
#params for text detector
cfg.det_algorithm = "DB"
cfg.det_model_dir = "./inference/ch_det_mv3_db/"
cfg.det_model_dir = "./inference/ch_ppocr_mobile_v1.1_det_infer/"
cfg.det_max_side_len = 960
#DB parmas
......@@ -28,7 +28,7 @@ def read_params():
#params for text recognizer
cfg.rec_algorithm = "CRNN"
cfg.rec_model_dir = "./inference/ch_rec_mv3_crnn/"
cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v1.1_rec_infer/"
cfg.rec_image_shape = "3, 32, 320"
cfg.rec_char_type = 'ch'
......
doc/doc_ch/serving.md → deploy/hubserving/readme.md
浏览文件 @ 37f5ff96
# 服务部署
[English](readme_en.md) | 简体中文
PaddleOCR提供2种服务部署方式：
- 基于HubServing的部署：已集成到PaddleOCR中（[code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/hubserving)），按照本教程使用；
- 基于PaddleServing的部署：详见PaddleServing官网[demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)，后续也将集成到PaddleOCR。
- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，按照本教程使用；
- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，使用方法参考[文档](../pdserving/readme.md)。
服务部署目录下包括检测、识别、2阶段串联三种服务包，根据需求选择相应的服务包进行安装和启动。目录如下：
# 基于PaddleHub Serving的服务部署
hubserving服务部署目录下包括检测、识别、2阶段串联三种服务包，请根据需求选择相应的服务包进行安装和启动。目录结构如下：
```
deploy/hubserving/
└─ ocr_det 检测模块服务包
......@@ -30,11 +32,18 @@ pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
# 在Linux下设置环境变量
export PYTHONPATH=.
# 在Windows下设置环境变量
# 或者，在Windows下设置环境变量
SET PYTHONPATH=.
```
### 2. 安装服务模块
### 2. 下载推理模型
安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是v1.1版的超轻量模型，默认检测模型路径为：
`./inference/ch_ppocr_mobile_v1.1_det_infer/`，识别模型路径为：`./inference/ch_ppocr_mobile_v1.1_rec_infer/`。
**模型路径可在`params.py`中查看和修改。** 更多模型可以从PaddleOCR提供的[模型库](../../doc/doc_ch/models_list.md)下载，也可以替换成自己训练转换好的模型。
### 3. 安装服务模块
PaddleOCR提供3种服务模块，根据需要安装所需模块。
* 在Linux环境下，安装示例如下：
......@@ -61,15 +70,7 @@ hub install deploy\hubserving\ocr_rec\
hub install deploy\hubserving\ocr_system\
```
#### 安装模型
安装服务模块前，需要将训练好的模型放到对应的文件夹内。默认使用的是：
./inference/ch_det_mv3_db/
和
./inference/ch_rec_mv3_crnn/
这两个模型可以在https://github.com/PaddlePaddle/PaddleOCR 下载
可以在./deploy/hubserving/ocr_system/params.py 里面修改成自己的模型
### 3. 启动服务
### 4. 启动服务
#### 方式1. 命令行命令启动（仅支持CPU）
**启动命令：**
```shell
......@@ -172,7 +173,7 @@ hub serving start -c deploy/hubserving/ocr_system/config.json
```hub serving stop --port/-p XXXX```
- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。
例如，如果需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`det_model_dir`和`rec_model_dir`，当然，同时可能还需要修改其他相关参数，请根据实际情况修改调试。 建议修改后先直接运行`module.py`调试，能正确运行预测后再启动服务测试。
例如，如果需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`det_model_dir`和`rec_model_dir`，当然，同时可能还需要修改其他相关参数，请根据实际情况修改调试。 **强烈建议修改后先直接运行`module.py`调试，能正确运行预测后再启动服务测试。**
- 3、 卸载旧服务包
```hub uninstall ocr_system```
......
doc/doc_en/serving_en.md → deploy/hubserving/readme_en.md
浏览文件 @ 37f5ff96
# Service deployment
English | [简体中文](readme.md)
PaddleOCR provides 2 service deployment methods:：
- Based on **HubServing**：Has been integrated into PaddleOCR ([code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/hubserving)). Please follow this tutorial.
- Based on **PaddleServing**：See PaddleServing official website for details ([demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)). Follow-up will also be integrated into PaddleOCR.
PaddleOCR provides 2 service deployment methods:
- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial.
- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../pdserving/readme_en.md) for usage.
The service deployment directory includes three service packages: detection, recognition, and two-stage series connection. Select the corresponding service package to install and start service according to your needs. The directory is as follows:
# Service deployment based on PaddleHub Serving
The hubserving service deployment directory includes three service packages: detection, recognition, and two-stage series connection. Please select the corresponding service package to install and start service according to your needs. The directory is as follows:
```
deploy/hubserving/
└─ ocr_det detection module service package
......@@ -31,11 +33,17 @@ pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
# Set environment variables on Linux
export PYTHONPATH=.
# Set environment variables on Windows
SET PYTHONPATH=.
```
### 2. Install Service Module
### 2. Download inference model
Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v1.1 is used, and the default detection model path is: `./inference/ch_ppocr_mobile_v1.1_det_infer/`, the default recognition model path is: `./inference/ch_ppocr_mobile_v1.1_rec_infer/`.
**The model path can be found and modified in `params.py`.** More models provided by PaddleOCR can be obtained from the [model library](../../doc/doc_en/models_list_en.md). You can also use models trained by yourself.
### 3. Install Service Module
PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs.
* On Linux platform, the examples are as follows.
......@@ -62,7 +70,7 @@ hub install deploy\hubserving\ocr_rec\
hub install deploy\hubserving\ocr_system\
```
### 3. Start service
### 4. Start service
#### Way 1. Start with command line parameters (CPU only)
**start command：**
......
deploy/pdserving/readme.md
浏览文件 @ 37f5ff96
# Paddle Serving 服务部署
[English](readme_en.md) | 简体中文
PaddleOCR提供2种服务部署方式：
- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，使用方法参考[文档](../hubserving/readme.md)。
- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，按照本教程使用。
# Paddle Serving 服务部署
本教程将介绍基于[Paddle Serving](https://github.com/PaddlePaddle/Serving)部署PaddleOCR在线预测服务的详细步骤。
## 快速启动服务
......
deploy/pdserving/readme_en.md 0 → 100644
浏览文件 @ 37f5ff96
English | [简体中文](readme.md)
PaddleOCR provides 2 service deployment methods:
- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please refer to the [tutorial](../hubserving/readme_en.md) for usage.
- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please follow this tutorial.
# Service deployment based on Paddle Serving
This tutorial will introduce the detail steps of deploying PaddleOCR online prediction service based on [Paddle Serving](https://github.com/PaddlePaddle/Serving).
## Quick start service
### 1. Prepare the environment
Let's first install the relevant components of Paddle Serving. GPU is recommended for service deployment with Paddle Serving.
**Requirements:**
- **CUDA version: 9.0**
- **CUDNN version: 7.0**
- **Operating system version: >= CentOS 6**
- **Python version： 2.7/3.6/3.7**
**Installation：**
```
# install GPU server
python -m pip install paddle_serving_server_gpu
# or, install CPU server
python -m pip install paddle_serving_server
# install client and App package (CPU/GPU)
python -m pip install paddle_serving_app paddle_serving_client
```
### 2. Model transformation
You can directly use converted model provided by `paddle_serving_app` for convenience. Execute the following command to obtain:
```
python -m paddle_serving_app.package --get_model ocr_rec
tar -xzvf ocr_rec.tar.gz
python -m paddle_serving_app.package --get_model ocr_det
tar -xzvf ocr_det.tar.gz
```
Executing the above command will download the `db_crnn_mobile` model, which is in different format with inference model. If you want to use other models for deployment, you can refer to the [tutorial](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md) to convert your inference model to a model which is deployable for Paddle Serving.
We take `ch_rec_r34_vd_crnn` model as example. Download the inference model by executing the following command:
```
wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar
tar xf ch_rec_r34_vd_crnn_infer.tar
```
Convert the downloaded model by executing the following python script:
```
from paddle_serving_client.io import inference_model_to_serving
inference_model_dir = "ch_rec_r34_vd_crnn"
serving_client_dir = "serving_client_dir"
serving_server_dir = "serving_server_dir"
feed_var_names, fetch_var_names = inference_model_to_serving(
inference_model_dir, serving_client_dir, serving_server_dir, model_filename="model", params_filename="params")
```
Finally, model configuration of client and server will be generated in `serving_client_dir` and `serving_server_dir`.
### 3. Start service
Start the standard version or the fast version service according to your actual needs. The comparison of the two versions is shown in the table below:
|version|characteristics|recommended scenarios|
|-|-|-|
|standard version|High stability, suitable for distributed deployment|Large throughput and cross regional deployment|
|fast version|Easy to deploy and fast to predict|Suitable for scenarios which requires high prediction speed and fast iteration speed|
#### Mode 1. Start the standard mode service
```
# start with CPU
python -m paddle_serving_server.serve --model ocr_det_model --port 9293
python ocr_web_server.py cpu
# or, with GPU
python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0
python ocr_web_server.py gpu
```
#### Mode 2. Start the fast mode service
```
# start with CPU
python ocr_local_server.py cpu
# or, with GPU
python ocr_local_server.py gpu
```
## Send prediction requests
```
python ocr_web_client.py
```
## Returned result format
The returned result is a JSON string, eg.
```
{u'result': {u'res': [u'\u571f\u5730\u6574\u6cbb\u4e0e\u571f\u58e4\u4fee\u590d\u7814\u7a76\u4e2d\u5fc3', u'\u534e\u5357\u519c\u4e1a\u5927\u5b661\u7d20\u56fe']}}
```
You can also print the readable result in `res`:
```
土地整治与土壤修复研究中心
华南农业大学1素图
```
## User defined service module modification
The pre-processing and post-processing process, can be found in the `preprocess` and `postprocess` function in `ocr_web_server.py` or `ocr_local_server.py`. The pre-processing/post-processing library for common CV models provided by `paddle_serving_app` is called.
You can modify the corresponding code as actual needs.
If you only want to start the detection service or the recognition service, execute the corresponding script reffering to the following table. Indicate the CPU or GPU is used in the start command parameters.
| task | standard | fast |
| ---- | ----------------- | ------------------- |
| detection | det_web_server.py | det_local_server.py |
| recognition | rec_web_server.py | rec_local_server.py |
More info can be found in [Paddle Serving](https://github.com/PaddlePaddle/Serving).
deploy/slim/quantization/README.md
浏览文件 @ 37f5ff96
> 运行示例前请先安装1.2.0或更高版本PaddleSlim
# 模型量化压缩教程
压缩结果：
<table>
<thead>
<tr>
<th>序号</th>
<th>任务</th>
<th>模型</th>
<th>压缩策略</th>
<th>精度(自建中文数据集)</th>
<th>耗时(ms)</th>
<th>整体耗时(ms)</th>
<th>加速比</th>
<th>整体模型大小(M)</th>
<th>压缩比例</th>
<th>下载链接</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">0</td>
<td>检测</td>
<td>MobileNetV3_DB</td>
<td>无</td>
<td>61.7</td>
<td>224</td>
<td rowspan="2">375</td>
<td rowspan="2">-</td>
<td rowspan="2">8.6</td>
<td rowspan="2">-</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>MobileNetV3_CRNN</td>
<td>无</td>
<td>62.0</td>
<td>9.52</td>
<td></td>
</tr>
<tr>
<td rowspan="2">1</td>
<td>检测</td>
<td>SlimTextDet</td>
<td>PACT量化训练</td>
<td>62.1</td>
<td>195</td>
<td rowspan="2">348</td>
<td rowspan="2">8%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">2</td>
<td>检测</td>
<td>SlimTextDet_quat_pruning</td>
<td>剪裁+PACT量化训练</td>
<td>60.86</td>
<td>142</td>
<td rowspan="2">288</td>
<td rowspan="2">30%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">3</td>
<td>检测</td>
<td>SlimTextDet_pruning</td>
<td>剪裁</td>
<td>61.57</td>
<td>138</td>
<td rowspan="2">295</td>
<td rowspan="2">27%</td>
<td rowspan="2">2.9</td>
<td rowspan="2">66.28%</td>
<td></td>
</tr>
<tr>
<td>识别</td>
<td>SlimTextRec</td>
<td>PACT量化训练</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
</tbody>
</table>
## 概述
复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型量化将全精度缩减到定点数减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
该示例使用PaddleSlim提供的[量化压缩API](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)对OCR模型进行压缩。
在阅读该示例前，建议您先了解以下内容：
- [OCR模型的常规训练方法](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
- [PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)
- [PaddleSlim使用文档](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
## 安装PaddleSlim
可按照[PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)中的步骤安装PaddleSlim。
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
python setup.py install
```
## 获取预训练模型
[识别预训练模型下载地址]()
[检测预训练模型下载地址]()
## 量化训练
加载预训练模型后，在定义好量化策略后即可对模型进行量化。量化相关功能的使用具体细节见：[模型量化](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/quantization_api.html)
进入PaddleOCR根目录，通过以下命令对模型进行量化：
......@@ -25,10 +152,11 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
## 导出模型
在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
```bash
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_model
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
```
deploy/slim/quantization/README_en.md 0 → 100755
浏览文件 @ 37f5ff96
\> PaddleSlim 1.2.0 or higher version should be installed before runing this example.
# Model compress tutorial (Quantization)
Compress results：
<table>
<thead>
<tr>
<th>ID</th>
<th>Task</th>
<th>Model</th>
<th>Compress Strategy</th>
<th>Criterion(Chinese dataset)</th>
<th>Inference Time(ms)</th>
<th>Inference Time(Total model)(ms)</th>
<th>Acceleration Ratio</th>
<th>Model Size(MB)</th>
<th>Commpress Ratio</th>
<th>Download Link</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">0</td>
<td>Detection</td>
<td>MobileNetV3_DB</td>
<td>None</td>
<td>61.7</td>
<td>224</td>
<td rowspan="2">375</td>
<td rowspan="2">-</td>
<td rowspan="2">8.6</td>
<td rowspan="2">-</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>MobileNetV3_CRNN</td>
<td>None</td>
<td>62.0</td>
<td>9.52</td>
<td></td>
</tr>
<tr>
<td rowspan="2">1</td>
<td>Detection</td>
<td>SlimTextDet</td>
<td>PACT Quant Aware Training</td>
<td>62.1</td>
<td>195</td>
<td rowspan="2">348</td>
<td rowspan="2">8%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">2</td>
<td>Detection</td>
<td>SlimTextDet_quat_pruning</td>
<td>Pruning+PACT Quant Aware Training</td>
<td>60.86</td>
<td>142</td>
<td rowspan="2">288</td>
<td rowspan="2">30%</td>
<td rowspan="2">2.8</td>
<td rowspan="2">67.82%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PPACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
<tr>
<td rowspan="2">3</td>
<td>Detection</td>
<td>SlimTextDet_pruning</td>
<td>Pruning</td>
<td>61.57</td>
<td>138</td>
<td rowspan="2">295</td>
<td rowspan="2">27%</td>
<td rowspan="2">2.9</td>
<td rowspan="2">66.28%</td>
<td></td>
</tr>
<tr>
<td>Recognition</td>
<td>SlimTextRec</td>
<td>PACT Quant Aware Training</td>
<td>61.48</td>
<td>8.6</td>
<td></td>
</tr>
</tbody>
</table>
## Overview
Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model. Quantization is a technique that reduces this redundancyby reducing the full precision data to a fixed number, so as to reduce model calculation complexity and improve model inference performance.
This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the OCR model.
It is recommended that you could understand following pages before reading this example,：
- [The training strategy of OCR model](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)
## Install PaddleSlim
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd Paddleslim
python setup.py install
```
## Download Pretrain Model
[Download link of Detection pretrain model]()
[Download link of recognization pretrain model]()
## Quan-Aware Training
After loading the pre training model, the model can be quantified after defining the quantization strategy. For specific details of quantization method, see：[Model Quantization](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/quantization_api.html)
Enter the PaddleOCR root directory，perform model quantization with the following command：
```bash
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
```
## Export inference model
After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
```bash
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
```
doc/doc_ch/algorithm_overview.md 0 → 100644
浏览文件 @ 37f5ff96
<a name="算法介绍"></a>
## 算法介绍
- [1.文本检测算法](#文本检测算法)
- [2.文本识别算法](#文本识别算法)
<a name="文本检测算法"></a>
### 1.文本检测算法
PaddleOCR开源的文本检测算法列表：
- [x] DB([paper](https://arxiv.org/abs/1911.08947))（ppocr推荐）
- [x] EAST([paper](https://arxiv.org/abs/1704.03155))
- [x] SAST([paper](https://arxiv.org/abs/1908.05498))
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|
|-|-|-|-|-|-|
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)|
在Total-text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|
|-|-|-|-|-|-|
|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)|
**说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
使用[LSVT](./datasets.md#1icdar2019-lsvt)街景数据集共3w张数据，训练中文检测模型的相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
* 注： 上述DB模型的训练和评估，需设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化
PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./detection.md)。
<a name="文本识别算法"></a>
### 2.文本识别算法
PaddleOCR开源的文本识别算法列表：
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))（ppocr推荐）
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))
参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
|-|-|-|-|-|
|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
**说明：** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广，增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载，提取码: y3ry。
原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
使用[LSVT](./datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：
|模型|骨干网络|配置文件|预训练模型|
|-|-|-|-|
|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。
doc/doc_ch/detection.md
浏览文件 @ 37f5ff96
......@@ -14,6 +14,15 @@ wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_l
wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txt
```
PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `train_data/gen_label.py`, 这里以训练集为例：
```
# 将官网下载的标签文件转换为 train_icdar2015_label.txt
python gen_label.py --mode="det" --root_path="icdar_c4_train_imgs/" \
--input_path="ch4_training_localization_transcription_gt" \
--output_label="train_icdar2015_label.txt"
```
解压数据集和下载标注文件后，PaddleOCR/train_data/ 有两个文件夹和两个文件，分别是：
```
/PaddleOCR/train_data/icdar2015/text_localization/
......
doc/doc_ch/inference.md
浏览文件 @ 37f5ff96
......@@ -24,6 +24,7 @@ inference 模型（`fluid.io.save_inference_model`保存的模型）
- [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
- [3. 基于Attention损失的识别模型推理](#基于Attention损失的识别模型推理)
- [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- [5. 多语言模型的推理](#多语言模型的推理)
- [四、方向分类模型推理](#方向识别模型推理)
- [1. 方向分类模型推理](#方向分类模型推理)
......@@ -305,6 +306,22 @@ dict_character = list(self.character_str)
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
```
<a name="多语言模型的推理"></a>
### 5. 多语言模型的推理
如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，
需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/` 路径下有默认提供的小语种字体，例如韩文识别：
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/korean_dict.txt" --vis_font_path="doc/korean.ttf"
```
![](../imgs_words/korean/1.jpg)
执行命令后，上图的预测结果为：
``` text
2020-09-19 16:15:05,076-INFO: index: [205 206 38 39]
2020-09-19 16:15:05,077-INFO: word : 바탕으로
2020-09-19 16:15:05,077-INFO: score: 0.9171358942985535
```
<a name="方向分类模型推理"></a>
## 四、方向分类模型推理
......
doc/doc_ch/models_list.md
浏览文件 @ 37f5ff96
......@@ -7,22 +7,22 @@
- [3. 多语言识别模型](#多语言识别模型)
- [三、文本方向分类模型](#文本方向分类模型)
PaddleOCR提供的可下载模型包括`预测模型`、`训练模型`、`预训练模型`、`slim模型`，模型区别说明如下：
PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训练模型`、`slim模型`，模型区别说明如下：
|模型类型|模型格式|简介|
|-|-|-|
|预测模型|model、params|用于python预测引擎推理，[详情](./inference.md)|
|推理模型|model、params|用于python预测引擎推理，[详情](./inference.md)|
|训练模型、预训练模型|\*.pdmodel、\*.pdopt、\*.pdparams|训练过程中保存的checkpoints模型，保存的是模型的参数，多用于模型指标评估和恢复训练|
|slim模型|-|用于lite部署|
<a name="文本检测模型"></a>
### 一、文本检测模型
|模型名称|模型简介|预测模型大小|下载地址|
|模型名称|模型简介|推理模型大小|下载地址|
|-|-|-|-|
|ch_ppocr_mobile_slim_v1.1_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|-|[预测模型]() / [训练模型]() / [slim模型]()|
|ch_ppocr_mobile_v1.1_det|原始超轻量模型，支持中英文、多语种文本检测|2.6M|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|
|ch_ppocr_server_v1.1_det|通用模型，支持中英文、多语种文本检测，比超轻量模型更大，但效果更好|47.2M|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar)|
|ch_ppocr_mobile_slim_v1.1_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|1.4M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|
|ch_ppocr_mobile_v1.1_det|原始超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|
|ch_ppocr_server_v1.1_det|通用模型，支持中英文、多语种文本检测，比超轻量模型更大，但效果更好|47.2M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar)|
<a name="文本识别模型"></a>
......@@ -30,41 +30,42 @@ PaddleOCR提供的可下载模型包括`预测模型`、`训练模型`、`预训
<a name="中文识别模型"></a>
#### 1. 中文识别模型
|模型名称|模型简介|预测模型大小|下载地址|
|模型名称|模型简介|推理模型大小|下载地址|
|-|-|-|-|
|ch_ppocr_mobile_slim_v1.1_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|-|[预测模型]() / [训练模型]() / [slim模型]()|
|ch_ppocr_mobile_v1.1_rec|原始超轻量模型，支持中英文、数字识别|4.6M|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar)|
|ch_ppocr_server_v1.1_rec|通用模型，支持中英文、数字识别|105M|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar)|
|ch_ppocr_mobile_slim_v1.1_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|1.6M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|
|ch_ppocr_mobile_v1.1_rec|原始超轻量模型，支持中英文、数字识别|4.6M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar)|
|ch_ppocr_server_v1.1_rec|通用模型，支持中英文、数字识别|105M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar)|
**说明：** `训练模型`是基于预训练模型在真实数据与竖排合成文本数据上finetune得到的模型，在真实应用场景中有着更好的表现，`预训练模型`则是直接基于全量真实数据与合成数据训练得到，更适合用于在自己的数据集上finetune。
<a name="英文识别模型"></a>
#### 2. 英文识别模型
|模型名称|模型简介|预测模型大小|下载地址|
|模型名称|模型简介|推理模型大小|下载地址|
|-|-|-|-|
|en_ppocr_mobile_slim_v1.1_rec|slim裁剪量化版超轻量模型，支持英文、数字识别|-|[预测模型]() / [训练模型]() / [slim模型]()|
|en_ppocr_mobile_v1.1_rec|原始超轻量模型，支持英文、数字识别|2.0M|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_train.tar)|
|en_ppocr_mobile_slim_v1.1_rec|slim裁剪量化版超轻量模型，支持英文、数字识别|0.9M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/en/en_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/en/en_ppocr_mobile_v1.1_rec_quant_opt.nb)|
|en_ppocr_mobile_v1.1_rec|原始超轻量模型，支持英文、数字识别|2.0M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_train.tar)|
<a name="多语言识别模型"></a>
#### 3. 多语言识别模型（更多语言持续更新中...）
|模型名称|模型简介|预测模型大小|下载地址|
|模型名称|模型简介|推理模型大小|下载地址|
|-|-|-|-|
|-|法文识别|-|[预测模型]() / [训练模型]()|
|-|德文识别|-|[预测模型]() / [训练模型]()|
|-|韩文识别|-|[预测模型]() / [训练模型]()|
|-|日文识别|-|[预测模型]() / [训练模型]()|
| french_ppocr_mobile_v1.1_rec |法文识别|2.1M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_train.tar)|
| german_ppocr_mobile_v1.1_rec |德文识别|2.1M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_train.tar)|
| korean_ppocr_mobile_v1.1_rec |韩文识别|3.4M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_train.tar)|
| japan_ppocr_mobile_v1.1_rec |日文识别|3.7M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_train.tar)|
<a name="文本方向分类模型"></a>
### 三、文本方向分类模型
|模型名称|模型简介|预测模型大小|下载地址|
|模型名称|模型简介|推理模型大小|下载地址|
|-|-|-|-|
|ch_ppocr_mobile_v1.1_cls_quant|slim量化版模型|-|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_train.tar) / [slim模型]()|
|ch_ppocr_mobile_v1.1_cls|原始模型|850kb|[预测模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar)|
|ch_ppocr_mobile_v1.1_cls_quant|slim量化版模型|0.5M|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_train.tar) / [slim模型]()|
|ch_ppocr_mobile_v1.1_cls|原始模型|850kb|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar)|
## OCR模型列表（V1.0，7月16日更新）
|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址|
|-|-|-|-|-|
|chinese_db_crnn_mobile|8.6M超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
|chinese_db_crnn_mobile|8.6M超轻量级中文OCR模型|[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
|chinese_db_crnn_server|通用中文OCR模型|[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[推理模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
doc/doc_ch/recognition.md
浏览文件 @ 37f5ff96
......@@ -44,6 +44,13 @@ wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_t
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
```
PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `train_data/gen_label.py`, 这里以训练集为例：
```
# 将官网下载的标签文件转换为 rec_gt_label.txt
python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
```
最终训练集应有如下文件结构：
```
|-train_data
......@@ -201,7 +208,19 @@ Optimizer:
```
**注意，预测/评估时的配置文件请务必与训练一致。**
- 小语种
PaddleOCR也提供了多语言的， `configs/rec/multi_languages` 路径下的提供了多语言的配置文件，目前PaddleOCR支持的多语言算法有：
| 配置文件 | 算法名称 | backbone | trans | seq | pred | language ｜
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: |
| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 英语 ｜
| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 法语 ｜
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 德语 ｜
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 日语 ｜
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | 韩语 ｜
多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体和测试数据可以在[百度网盘]()上下载。
### 评估
......
doc/doc_ch/tree.md 0 → 100644
浏览文件 @ 37f5ff96
# 整体目录结构
PaddleOCR 的整体目录结构介绍如下：
```
PaddleOCR
├── configs // 配置文件，可通过yml文件选择模型结构并修改超参
│ ├── cls // 方向分类器相关配置文件
│ │ ├── cls_mv3.yml // 训练配置相关，包括骨干网络、head、loss、优化器
│ │ └── cls_reader.yml // 数据读取相关，数据读取方式、数据存储路径
│ ├── det // 检测相关配置文件
│ │ ├── det_db_icdar15_reader.yml // 数据读取
│ │ ├── det_mv3_db.yml // 训练配置
│ │ ...
│ └── rec // 识别相关配置文件
│ ├── rec_benchmark_reader.yml // LMDB 格式数据读取相关
│ ├── rec_chinese_common_train.yml // 通用中文训练配置
│ ├── rec_icdar15_reader.yml // simple 数据读取相关，包括数据读取函数、数据路径、标签文件
│ ...
├── deploy // 部署相关
│ ├── android_demo // android_demo
│ │ ...
│ ├── cpp_infer // C++ infer
│ │ ├── CMakeLists.txt // Cmake 文件
│ │ ├── docs // 说明文档
│ │ │ └── windows_vs2019_build.md
│ │ ├── include // 头文件
│ │ │ ├── clipper.h // clipper 库
│ │ │ ├── config.h // 预测配置
│ │ │ ├── ocr_cls.h // 方向分类器
│ │ │ ├── ocr_det.h // 文字检测
│ │ │ ├── ocr_rec.h // 文字识别
│ │ │ ├── postprocess_op.h // 检测后处理
│ │ │ ├── preprocess_op.h // 检测预处理
│ │ │ └── utility.h // 工具
│ │ ├── readme.md // 说明文档
│ │ ├── ...
│ │ ├── src // 源文件
│ │ │ ├── clipper.cpp
│ │ │ ├── config.cpp
│ │ │ ├── main.cpp
│ │ │ ├── ocr_cls.cpp
│ │ │ ├── ocr_det.cpp
│ │ │ ├── ocr_rec.cpp
│ │ │ ├── postprocess_op.cpp
│ │ │ ├── preprocess_op.cpp
│ │ │ └── utility.cpp
│ │ └── tools // 编译、执行脚本
│ │ ├── build.sh // 编译脚本
│ │ ├── config.txt // 配置文件
│ │ └── run.sh // 测试启动脚本
│ ├── docker
│ │ └── hubserving
│ │ ├── cpu
│ │ │ └── Dockerfile
│ │ ├── gpu
│ │ │ └── Dockerfile
│ │ ├── README_cn.md
│ │ ├── README.md
│ │ └── sample_request.txt
│ ├── hubserving // hubserving
│ │ ├── ocr_det // 文字检测
│ │ │ ├── config.json // serving 配置
│ │ │ ├── __init__.py
│ │ │ ├── module.py // 预测模型
│ │ │ └── params.py // 预测参数
│ │ ├── ocr_rec // 文字识别
│ │ │ ├── config.json
│ │ │ ├── __init__.py
│ │ │ ├── module.py
│ │ │ └── params.py
│ │ └── ocr_system // 系统预测
│ │ ├── config.json
│ │ ├── __init__.py
│ │ ├── module.py
│ │ └── params.py
│ ├── imgs // 预测图片
│ │ ├── cpp_infer_pred_12.png
│ │ └── demo.png
│ ├── ios_demo // ios demo
│ │ ...
│ ├── lite // lite 部署
│ │ ├── cls_process.cc // 方向分类器数据处理
│ │ ├── cls_process.h
│ │ ├── config.txt // 检测配置参数
│ │ ├── crnn_process.cc // crnn数据处理
│ │ ├── crnn_process.h
│ │ ├── db_post_process.cc // db数据处理
│ │ ├── db_post_process.h
│ │ ├── Makefile // 编译文件
│ │ ├── ocr_db_crnn.cc // 串联预测
│ │ ├── prepare.sh // 数据准备
│ │ ├── readme.md // 说明文档
│ │ ...
│ ├── pdserving // pdserving 部署
│ │ ├── det_local_server.py // 检测 快速版，部署方便预测速度快
│ │ ├── det_web_server.py // 检测 完整版，稳定性高分布式部署
│ │ ├── ocr_local_server.py // 检测+识别 快速版
│ │ ├── ocr_web_client.py // 客户端
│ │ ├── ocr_web_server.py // 检测+识别 完整版
│ │ ├── readme.md // 说明文档
│ │ ├── rec_local_server.py // 识别 快速版
│ │ └── rec_web_server.py // 识别 完整版
│ └── slim
│ └── quantization // 量化相关
│ ├── export_model.py // 导出模型
│ ├── quant.py // 量化
│ └── README.md // 说明文档
├── doc // 文档教程
│ ...
├── paddleocr.py
├── ppocr // 网络核心代码
│ ├── data // 数据处理
│ │ ├── cls // 方向分类器
│ │ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，读取数据并组成batch
│ │ │ └── randaugment.py // 随机数据增广操作
│ │ ├── det // 检测
│ │ │ ├── data_augment.py // 数据增广操作
│ │ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，读取数据并组成batch
│ │ │ ├── db_process.py // db 数据处理
│ │ │ ├── east_process.py // east 数据处理
│ │ │ ├── make_border_map.py // 生成边界图
│ │ │ ├── make_shrink_map.py // 生成收缩图
│ │ │ ├── random_crop_data.py // 随机切割
│ │ │ └── sast_process.py // sast 数据处理
│ │ ├── reader_main.py // 数据读取器主函数
│ │ └── rec // 识别
│ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，包含 LMDB_Reader 和 Simple_Reader
│ │ └── img_tools.py // 数据处理相关，包括数据归一化、扰动
│ ├── __init__.py
│ ├── modeling // 组网相关
│ │ ├── architectures // 模型架构，定义模型所需的各个模块
│ │ │ ├── cls_model.py // 方向分类器
│ │ │ ├── det_model.py // 检测
│ │ │ └── rec_model.py // 识别
│ │ ├── backbones // 骨干网络
│ │ │ ├── det_mobilenet_v3.py // 检测 mobilenet_v3
│ │ │ ├── det_resnet_vd.py
│ │ │ ├── det_resnet_vd_sast.py
│ │ │ ├── rec_mobilenet_v3.py // 识别 mobilenet_v3
│ │ │ ├── rec_resnet_fpn.py
│ │ │ └── rec_resnet_vd.py
│ │ ├── common_functions.py // 公共函数
│ │ ├── heads // 头函数
│ │ │ ├── cls_head.py // 分类头
│ │ │ ├── det_db_head.py // db 检测头
│ │ │ ├── det_east_head.py // east 检测头
│ │ │ ├── det_sast_head.py // sast 检测头
│ │ │ ├── rec_attention_head.py // 识别 attention
│ │ │ ├── rec_ctc_head.py // 识别 ctc
│ │ │ ├── rec_seq_encoder.py // 识别 序列编码
│ │ │ ├── rec_srn_all_head.py // 识别 srn 相关
│ │ │ └── self_attention // srn attention
│ │ │ └── model.py
│ │ ├── losses // 损失函数
│ │ │ ├── cls_loss.py // 方向分类器损失函数
│ │ │ ├── det_basic_loss.py // 检测基础loss
│ │ │ ├── det_db_loss.py // DB loss
│ │ │ ├── det_east_loss.py // EAST loss
│ │ │ ├── det_sast_loss.py // SAST loss
│ │ │ ├── rec_attention_loss.py // attention loss
│ │ │ ├── rec_ctc_loss.py // ctc loss
│ │ │ └── rec_srn_loss.py // srn loss
│ │ └── stns // 空间变换网络
│ │ └── tps.py // TPS 变换
│ ├── optimizer.py // 优化器
│ ├── postprocess // 后处理
│ │ ├── db_postprocess.py // DB 后处理
│ │ ├── east_postprocess.py // East 后处理
│ │ ├── lanms // lanms 相关
│ │ │ ...
│ │ ├── locality_aware_nms.py // nms
│ │ └── sast_postprocess.py // sast 后处理
│ └── utils // 工具
│ ├── character.py // 字符处理，包括对文本的编码和解码，计算预测准确率
│ ├── check.py // 参数加载检查
│ ├── ic15_dict.txt // 英文数字字典，区分大小写
│ ├── ppocr_keys_v1.txt // 中文字典，用于训练中文模型
│ ├── save_load.py // 模型保存和加载函数
│ ├── stats.py // 统计
│ └── utility.py // 工具函数，包含输入参数是否合法等相关检查工具
├── README_en.md // 说明文档
├── README.md
├── requirments.txt // 安装依赖
├── setup.py // whl包打包脚本
└── tools // 启动工具
├── eval.py // 评估函数
├── eval_utils // 评估工具
│ ├── eval_cls_utils.py // 分类相关
│ ├── eval_det_iou.py // 检测 iou 相关
│ ├── eval_det_utils.py // 检测相关
│ ├── eval_rec_utils.py // 识别相关
│ └── __init__.py
├── export_model.py // 导出 infer 模型
├── infer // 基于预测引擎预测
│ ├── predict_cls.py
│ ├── predict_det.py
│ ├── predict_rec.py
│ ├── predict_system.py
│ └── utility.py
├── infer_cls.py // 基于训练引擎 预测分类
├── infer_det.py // 基于训练引擎 预测检测
├── infer_rec.py // 基于训练引擎 预测识别
├── program.py // 整体流程
├── test_hubserving.py
└── train.py // 启动训练
```
doc/doc_ch/tricks.md 已删除 100644 → 0
浏览文件 @ dc0aea4e
## 中文OCR训练预测技巧
这里整理了一些中文OCR训练预测技巧，持续更新中，欢迎各位小伙伴贡献OCR炼丹秘籍～
- [更换骨干网络](#更换骨干网络)
- [中文长文本识别](#中文长文本识别)
- [空格识别](#空格识别)
<a name="更换骨干网络"></a>
#### 1、更换骨干网络
- **问题描述**
目前PaddleOCR中使用的骨干网络有ResNet_vd系列和MobileNetV3系列，更换骨干网络是否有助于效果提升？更换时需要注意什么？
- **炼丹建议**
- 无论是文字检测，还是文字识别，骨干网络的选择是预测效果和预测效率的权衡。一般，选择更大规模的骨干网络，例如ResNet101_vd，则检测或识别更准确，但预测耗时相应也会增加。而选择更小规模的骨干网络，例如MobileNetV3_small_x0_35，则预测更快，但检测或识别的准确率会大打折扣。幸运的是不同骨干网络的检测或识别效果与在ImageNet数据集图像1000分类任务效果正相关。[**飞桨图像分类套件PaddleClas**](https://github.com/PaddlePaddle/PaddleClas)汇总了ResNet_vd、Res2Net、HRNet、MobileNetV3、GhostNet等23种系列的分类网络结构，在上述图像分类任务的top1识别准确率，GPU(V100和T4)和CPU(骁龙855)的预测耗时以及相应的[**117个预训练模型下载地址**](https://paddleclas.readthedocs.io/zh_CN/latest/models/models_intro.html)。
- 文字检测骨干网络的替换，主要是确定类似与ResNet的4个stages，以方便集成后续的类似FPN的检测头。此外，对于文字检测问题，使用ImageNet训练的分类预训练模型，可以加速收敛和效果提升。
- 文字识别的骨干网络的替换，需要注意网络宽高stride的下降位置。由于文本识别一般宽高比例很大，因此高度下降频率少一些，宽度下降频率多一些。可以参考PaddleOCR中[MobileNetV3骨干网络](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/modeling/backbones/rec_mobilenet_v3.py)的改动。
<a name="中文长文本识别"></a>
#### 2、中文长文本识别
- **问题描述**
中文识别模型训练时分辨率最大是[3,32,320]，如果待识别的文本图像太长，如下图所示，该如何适配？
<div align="center">
<img src="../tricks/long_text_examples.jpg" width="600">
</div>
- **炼丹建议**
在中文识别模型训练时，并不是采用直接将训练样本缩放到[3,32,320]进行训练，而是先等比例缩放图像，保证图像高度为32，宽度不足320的部分补0，宽高比大于10的样本直接丢弃。预测时，如果是单张图像预测，则按上述操作直接对图像缩放，不做宽度320的限制。如果是多张图预测，则采用batch方式预测，每个batch的宽度动态变换，采用这个batch中最长宽度。[参考代码如下](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/tools/infer/predict_rec.py)：
```
def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape
assert imgC == img.shape[2]
if self.character_type == "ch":
imgW = int((32 * max_wh_ratio))
h, w = img.shape[:2]
ratio = w / float(h)
if math.ceil(imgH * ratio) > imgW:
resized_w = imgW
else:
resized_w = int(math.ceil(imgH * ratio))
resized_image = cv2.resize(img, (resized_w, imgH))
resized_image = resized_image.astype('float32')
resized_image = resized_image.transpose((2, 0, 1)) / 255
resized_image -= 0.5
resized_image /= 0.5
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
padding_im[:, :, 0:resized_w] = resized_image
return padding_im
```
<a name="空格识别"></a>
#### 3、空格识别
- **问题描述**
如下图所示，对于中英文混合场景，为了便于阅读和使用识别结果，往往需要将单词之间的空格识别出来，这种情况如何适配？
<div align="center">
<img src="../imgs_results/chinese_db_crnn_server/en_paper.jpg" width="600">
</div>
- **炼丹建议**
空格识别可以考虑以下两种方案:(1)优化文本检测算法。检测结果在空格处将文本断开。这种方案在检测数据标注时，需要将含有空格的文本行分成好多段。(2)优化文本识别算法。在识别字典里面引入空格字符，然后在识别的训练数据中，如果用空行，进行标注。此外，合成数据时，通过拼接训练数据，生成含有空格的文本。PaddleOCR目前采用的是第二种方案。
\ No newline at end of file
doc/doc_ch/update.md
浏览文件 @ 37f5ff96
# 更新
- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列系列中英文ocr模型，效果媲美商业效果。[模型下载](./models_list.md)
- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./FAQ.md)
- 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md)
- 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
- 2020.8.16 开源文本检测算法[SAST](https://arxiv.org/abs/1908.05498)和文本识别算法[SRN](https://arxiv.org/abs/2003.12294)
......
doc/doc_ch/visualization.md
浏览文件 @ 37f5ff96
# 效果展示
- [超轻量级中文OCR效果展示](#超轻量级中文OCR)
- [通用中文OCR效果展示](#通用中文OCR)
- [支持空格的中文OCR效果展示](#支持空格的中文OCR)
- PP-OCR 1.1系列模型效果
- [通用ppocr_server_1.1效果展示](#通用ppocr_server_1.1效果展示)
- [通用ppocr_mobile_1.1效果展示（待补充）]()
- PP-OCR 1.0系列模型效果
- [超轻量ppocr_mobile_1.0效果展示](#超轻量ppocr_mobile_1.0效果展示)
- [通用ppocr_server_1.0效果展示](#通用ppocr_server_1.0效果展示)
<a name="超轻量级中文OCR"></a>
## 超轻量级中文OCR效果展示
<a name="通用ppocr_server_1.1效果展示"></a>
## 通用ppocr_server_1.1效果展示
<div align="center">
<img src="../imgs_results/1.jpg" width="800">
<img src="../imgs_results/1102.jpg" width="800">
<img src="../imgs_results/1103.jpg" width="800">
<img src="../imgs_results/1104.jpg" width="800">
<img src="../imgs_results/1105.jpg" width="800">
<img src="../imgs_results/1110.jpg" width="800">
<img src="../imgs_results/1112.jpg" width="800">
</div>
<div align="center">
<img src="../imgs_results/7.jpg" width="800">
</div>
<div align="center">
<img src="../imgs_results/12.jpg" width="800">
</div>
<a name="超轻量ppocr_mobile_1.0效果展示"></a>
## 超轻量ppocr_mobile_1.0效果展示
<div align="center">
<img src="../imgs_results/4.jpg" width="800">
<img src="../imgs_results/1.jpg" width="800">
</div>
<div align="center">
<img src="../imgs_results/6.jpg" width="800">
<img src="../imgs_results/7.jpg" width="800">
</div>
<div align="center">
<img src="../imgs_results/9.jpg" width="800">
<img src="../imgs_results/6.jpg" width="800">
</div>
<div align="center">
<img src="../imgs_results/16.png" width="800">
</div>
<div align="center">
<img src="../imgs_results/22.jpg" width="800">
</div>
<a name="通用中文OCR"></a>
## 通用中文OCR效果展示
<a name="通用ppocr_server_1.0效果展示"></a>
## 通用ppocr_server_1.0效果展示
<div align="center">
<img src="../imgs_results/chinese_db_crnn_server/11.jpg" width="800">
......@@ -52,16 +54,3 @@
<div align="center">
<img src="../imgs_results/chinese_db_crnn_server/8.jpg" width="800">
</div>
<a name="支持空格的中文OCR"></a>
## 支持空格的中文OCR效果展示
### 轻量级模型
<div align="center">
<img src="../imgs_results/img_11.jpg" width="800">
</div>
### 通用模型
<div align="center">
<img src="../imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
</div>
doc/doc_ch/whl.md
浏览文件 @ 37f5ff96
......@@ -19,7 +19,9 @@ pip install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x是paddleocr的版本
* 检测+分类+识别全流程
```python
from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
# 参数依次为`zh`, `en`, `french`, `german`, `korean`, `japan`。
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
......
doc/doc_en/detection_en.md
浏览文件 @ 37f5ff96
......@@ -73,7 +73,7 @@ You can also use `-o` to change the training parameters without modifying the ym
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
```
#### load trained model and conntinue training
#### load trained model and continue training
If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
For example:
......
doc/doc_en/models_list_en.md 0 → 100644
浏览文件 @ 37f5ff96
## OCR model list（V1.1, updated on 9.22）
- [1. Text Detection Model](#Detection)
- [2. Text Recognition Model](#Recognition)
- [Chinese Recognition Model](#Chinese)
- [English Recognition Model](#English)
- [Multilingual Recognition Model](#Multilingual)
- [3. Text Angle Classification Model](#Angle)
The downloadable models provided by PaddleOCR include `inference model`, `trained model`, `pre-trained model` and `slim model`. The differences between the models are as follows:
|model type|model format|description|
|-|-|-|
|inference model|model、params|Used for reasoning based on Python prediction engine. [detail](./inference_en.md)|
|trained model / pre-trained model|\*.pdmodel、\*.pdopt、\*.pdparams|The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.|
|slim model|-|Generally used for Lite deployment|
<a name="Detection"></a>
### 1. Text Detection Model
|model name|description|model size|download|
|-|-|-|-|
|ch_ppocr_mobile_slim_v1.1_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|1.4M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|
|ch_ppocr_mobile_v1.1_det|Original lightweight model, supporting Chinese, English, multilingual text detection|2.6M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|
|ch_ppocr_server_v1.1_det|General model, which is larger than the lightweight model, but achieved better performance|47.2M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar)|
<a name="Recognition"></a>
### 2. Text Recognition Model
<a name="Chinese"></a>
#### Chinese Recognition Model
|model name|description|model size|download|
|-|-|-|-|
|ch_ppocr_mobile_slim_v1.1_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|1.6M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|
|ch_ppocr_mobile_v1.1_rec|Original lightweight model, supporting Chinese, English and number recognition|4.6M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar)|
|ch_ppocr_server_v1.1_rec|General model, supporting Chinese, English and number recognition|105M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar)|
**Note:** The `trained model` is finetuned on the `pre-trained model` with real data and synthsized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthsized data, which is more suitable for finetune on your own dataset.
<a name="English"></a>
#### English Recognition Model
|model name|description|model size|download|
|-|-|-|-|
|en_ppocr_mobile_slim_v1.1_rec|Slim pruned and quantized lightweight model, supporting English and number recognition|0.9M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/en/en_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim model](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/en/en_ppocr_mobile_v1.1_rec_quant_opt.nb)|
|en_ppocr_mobile_v1.1_rec|Original lightweight model, supporting English and number recognition|2.0M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_train.tar)|
<a name="Multilingual"></a>
#### Multilingual Recognition Model（Updating...）
|model name|description|model size|download|
|-|-|-|-|
| french_ppocr_mobile_v1.1_rec |Lightweight model for French recognition|2.1M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_train.tar)|
| german_ppocr_mobile_v1.1_rec |German model for French recognition|2.1M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_train.tar)|
| korean_ppocr_mobile_v1.1_rec |Lightweight model for Korean recognition|3.4M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_train.tar)|
| japan_ppocr_mobile_v1.1_rec |Lightweight model for Japanese recognition|3.7M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_train.tar)|
<a name="Angle"></a>
### 3. Text Angle Classification Model
|model name|description|model size|download|
|-|-|-|-|
|ch_ppocr_mobile_v1.1_cls_quant|Slim quantized model|0.5M|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_train.tar) / [slim model]()|
|ch_ppocr_mobile_v1.1_cls|Original model|850kb|[inference model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar)|
## OCR model list（V1.0, updated on 7.16）
|model name|description|detection model|recognition model|recognition model supporting space recognition|
|-|-|-|-|-|
|chinese_db_crnn_mobile|8.6M lightweight OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
|chinese_db_crnn_server|General OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
doc/doc_en/whl_en.md
浏览文件 @ 37f5ff96
......@@ -17,12 +17,16 @@ pip install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x is the version of padd
* detection classification and recognition
```python
from paddleocr import PaddleOCR,draw_ocr
# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
# You can set the parameter `lang` as `zh`, `en`, `french`, `german`, `korean`, `japan`
# to switch the language model in order.
ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
print(line)
# draw result
from PIL import Image
image = Image.open(img_path).convert('RGB')
......
doc/french.ttf 0 → 100644
浏览文件 @ 37f5ff96
文件已添加
doc/german.ttf 0 → 100644
浏览文件 @ 37f5ff96
文件已添加
doc/japan.ttc 0 → 100644
浏览文件 @ 37f5ff96
文件已添加
doc/korean.ttf 0 → 100644
浏览文件 @ 37f5ff96
文件已添加
paddleocr.py
浏览文件 @ 37f5ff96
......@@ -46,6 +46,26 @@ model_urls = {
'url':
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar',
'dict_path': './ppocr/utils/ic15_dict.txt'
},
'french': {
'url':
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar',
'dict_path': './ppocr/utils/french_dict.txt'
},
'german': {
'url':
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar',
'dict_path': './ppocr/utils/german_dict.txt'
},
'korean': {
'url':
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar',
'dict_path': './ppocr/utils/korean_dict.txt'
},
'japan': {
'url':
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar',
'dict_path': './ppocr/utils/japan_dict.txt'
}
},
'cls':
......@@ -165,8 +185,9 @@ class PaddleOCR(predict_system.TextSystem):
postprocess_params.__dict__.update(**kwargs)
self.use_angle_cls = postprocess_params.use_angle_cls
lang = postprocess_params.lang
assert lang in model_urls['rec'], 'param lang must in {}'.format(
model_urls['rec'].keys())
assert lang in model_urls[
'rec'], 'param lang must in {}, but got {}'.format(
model_urls['rec'].keys(), lang)
if postprocess_params.rec_char_dict_path is None:
postprocess_params.rec_char_dict_path = model_urls['rec'][lang][
'dict_path']
......
ppocr/utils/character.py
浏览文件 @ 37f5ff96
......@@ -29,7 +29,9 @@ class CharacterOps(object):
if self.character_type == "en":
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
elif self.character_type == "ch":
elif self.character_type in [
"ch", 'japan', 'korean', 'french', 'german'
]:
character_dict_path = config['character_dict_path']
add_space = False
if 'use_space_char' in config:
......@@ -166,7 +168,7 @@ def cal_predicts_accuracy_srn(char_ops,
cur_label = []
cur_pred = []
for j in range(max_text_len):
if labels[j + i * max_text_len] != int(char_num-1): #0
if labels[j + i * max_text_len] != int(char_num - 1): #0
cur_label.append(labels[j + i * max_text_len][0])
else:
break
......@@ -178,7 +180,8 @@ def cal_predicts_accuracy_srn(char_ops,
elif j == len(cur_label) and j == max_text_len:
acc_num += 1
break
elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1):
elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(
char_num - 1):
acc_num += 1
break
acc = acc_num * 1.0 / img_num
......
ppocr/utils/french_dict.txt 0 → 100644
浏览文件 @ 37f5ff96
!
"
%
&
'
(
)
+
,
-
.
/
0
1
2
3
4
5
6
7
8
9
:
;
?
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
[
]
a
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
t
u
v
w
x
y
z
«
³
µ
º
»
À
Á
Â
Å
É
Ê
Î
Ö
ß
à
á
â
ä
å
æ
ç
è
é
ê
ë
í
î
ï
ñ
ò
ó
ô
ö
ø
ù
ú
û
ü
ppocr/utils/german_dict.txt 0 → 100644
浏览文件 @ 37f5ff96
!
"
$
%
&
'
(
)
+
,
-
.
/
0
1
2
3
4
5
6
7
8
9
:
;
>
?
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
[
]
a
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
t
u
v
w
x
y
z
£
§
­
²
´
µ
·
º
¼
½
¿
À
Á
Ä
Å
Ç
É
Í
Ï
Ô
Ö
Ø
Ù
Ü
ß
à
á
â
ã
ä
å
æ
ç
è
é
ê
ë
í
ï
ñ
ò
ó
ô
ö
ø
ù
ú
û
ü
ppocr/utils/ic15_dict.txt
浏览文件 @ 37f5ff96
......@@ -34,3 +34,30 @@ w
x
y
z
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
ppocr/utils/japan_dict.txt 0 → 100644
浏览文件 @ 37f5ff96
此差异已折叠。
ppocr/utils/korean_dict.txt 0 → 100644
浏览文件 @ 37f5ff96
此差异已折叠。
tools/infer/predict_system.py
浏览文件 @ 37f5ff96
......@@ -133,6 +133,7 @@ def main(args):
image_file_list = get_image_file_list(args.image_dir)
text_sys = TextSystem(args)
is_visualize = True
font_path = args.vis_font_path
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)
if not flag:
......@@ -160,7 +161,7 @@ def main(args):
scores = [rec_res[i][1] for i in range(len(rec_res))]
draw_img = draw_ocr(
image, boxes, txts, scores, drop_score=drop_score)
image, boxes, txts, scores, drop_score=drop_score, font_path=font_path)
draw_img_save = "./inference_results/"
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
......
tools/infer/utility.py
浏览文件 @ 37f5ff96
......@@ -70,7 +70,11 @@ def parse_args():
"--rec_char_dict_path",
type=str,
default="./ppocr/utils/ppocr_keys_v1.txt")
parser.add_argument("--use_space_char", type=bool, default=True)
parser.add_argument("--use_space_char", type=str2bool, default=True)
parser.add_argument(
"--vis_font_path",
type=str,
default="./doc/simfang.ttf")
# params for text classifier
parser.add_argument("--use_angle_cls", type=str2bool, default=False)
......@@ -199,7 +203,7 @@ def draw_ocr(image,
return image
def draw_ocr_box_txt(image, boxes, txts):
def draw_ocr_box_txt(image, boxes, txts, font_path="./doc/simfang.ttf"):
h, w = image.height, image.width
img_left = image.copy()
img_right = Image.new('RGB', (w, h), (255, 255, 255))
......@@ -226,7 +230,7 @@ def draw_ocr_box_txt(image, boxes, txts):
if box_height > 2 * box_width:
font_size = max(int(box_width * 0.9), 10)
font = ImageFont.truetype(
"./doc/simfang.ttf", font_size, encoding="utf-8")
font_path, font_size, encoding="utf-8")
cur_y = box[0][1]
for c in txt:
char_size = font.getsize(c)
......@@ -236,7 +240,7 @@ def draw_ocr_box_txt(image, boxes, txts):
else:
font_size = max(int(box_height * 0.8), 10)
font = ImageFont.truetype(
"./doc/simfang.ttf", font_size, encoding="utf-8")
font_path, font_size, encoding="utf-8")
draw_right.text(
[box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
img_left = Image.blend(image, img_left, 0.5)
......
tools/program.py
浏览文件 @ 37f5ff96
......@@ -204,6 +204,15 @@ def build(config, main_prog, startup_prog, mode):
def build_export(config, main_prog, startup_prog):
"""
Build input and output for exporting a checkpoints model to an inference model
Args:
config(dict): config
main_prog(): main program
startup_prog(): startup program
Returns:
feeded_var_names(list[str]): var names of input for exported inference model
target_vars(list[Variable]): output vars for exported inference model
fetches_var_name: dict of checkpoints model outputs(included loss and measures)
"""
with fluid.program_guard(main_prog, startup_prog):
with fluid.unique_name.guard():
......@@ -246,6 +255,9 @@ def train_eval_det_run(config,
train_info_dict,
eval_info_dict,
is_pruning=False):
'''
main program of evaluation for detection
'''
train_batch_id = 0
log_smooth_window = config['Global']['log_smooth_window']
epoch_num = config['Global']['epoch_num']
......@@ -337,6 +349,9 @@ def train_eval_det_run(config,
def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
'''
main program of evaluation for recognition
'''
train_batch_id = 0
log_smooth_window = config['Global']['log_smooth_window']
epoch_num = config['Global']['epoch_num']
......@@ -513,6 +528,7 @@ def train_eval_cls_run(config, exe, train_info_dict, eval_info_dict):
def preprocess():
# load config from yml file
FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config)
merge_config(FLAGS.opt)
......@@ -522,6 +538,7 @@ def preprocess():
use_gpu = config['Global']['use_gpu']
check_gpu(use_gpu)
# check whether the set algorithm belongs to the supported algorithm list
alg = config['Global']['algorithm']
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
......
tools/train.py
浏览文件 @ 37f5ff96
......@@ -46,6 +46,7 @@ from paddle.fluid.contrib.model_stat import summary
def main():
# build train program
train_build_outputs = program.build(
config, train_program, startup_program, mode='train')
train_loader = train_build_outputs[0]
......@@ -54,6 +55,7 @@ def main():
train_opt_loss_name = train_build_outputs[3]
model_average = train_build_outputs[-1]
# build eval program
eval_program = fluid.Program()
eval_build_outputs = program.build(
config, eval_program, startup_program, mode='eval')
......@@ -61,9 +63,11 @@ def main():
eval_fetch_varname_list = eval_build_outputs[2]
eval_program = eval_program.clone(for_test=True)
# initialize train reader
train_reader = reader_main(config=config, mode="train")
train_loader.set_sample_list_generator(train_reader, places=place)
# initialize eval reader
eval_reader = reader_main(config=config, mode="eval")
exe = fluid.Executor(place)
......
train_data/gen_label.py 0 → 100644
浏览文件 @ 37f5ff96
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
import os
import argparse
def gen_rec_label(input_path, out_label):
with open(out_label, 'w') as out_file:
with open(input_path, 'r') as f:
for line in f.readlines():
tmp = line.strip('\n').replace(" ", "").split(',')
img_path, label = tmp[0], tmp[1]
label = label.replace("\"", "")
out_file.write(img_path + '\t' + label + '\n')
def gen_det_label(root_path, input_dir, out_label):
with open(out_label, 'w') as out_file:
for label_file in os.listdir(input_dir):
img_path = root_path + label_file[3:-4] + ".jpg"
label = []
with open(os.path.join(input_dir, label_file), 'r') as f:
for line in f.readlines():
tmp = line.strip("\n\r").replace("\xef\xbb\xbf", "").split(',')
points = tmp[:-2]
s = []
for i in range(0, len(points), 2):
b = points[i:i + 2]
s.append(b)
result = {"transcription": tmp[-1], "points": s}
label.append(result)
out_file.write(img_path + '\t' + str(label) + '\n')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'--mode',
type=str,
default="rec",
help='Generate rec_label or det_label, can be set rec or det')
parser.add_argument(
'--root_path',
type=str,
default=".",
help='The root directory of images.Only takes effect when mode=det ')
parser.add_argument(
'--input_path',
type=str,
default=".",
help='Input_label or input path to be converted')
parser.add_argument(
'--output_label',
type=str,
default="out_label.txt",
help='Output file name')
args = parser.parse_args()
if args.mode == "rec":
print("Generate rec label")
gen_rec_label(args.input_path, args.output_label)
elif args.mode == "det":
gen_det_label(args.root_path, args.input_path, args.output_label)
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或