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zK;ul2VcO|qL&1eV`j1xU>#;6&<-AEE&9$N)cqVza&A&1Nop`4o{1EksbvoMYq_XjJ zO+NR%e_Ck-UYJjrVh#A=kH)+X7M{+?^}cU5*#up;*)B0qilT%-I!+J;pyjBeHk zKOhBbgKH)VUQ4#e%2+BIQoDKxG+JRfIS+0(44>iFLrjT5)^4Yh# zLEC?dY>BxciN}fZt^IAnm5uf>zNzn9+ujh@G`ICJcl#pm)v71uH#C|@4ZE^*^ywQ~ zFR+tI4bdX`hknq)vTkBQj2Gmjgvrb+ZJVu5S)m8Iun+R9>ni_?hfos-w|sD_-M(<) zV8A)KS-xb$TAsXWrqYrFiKo=4mX{j9LXm!~dHze2ZqE6E0DBlw8VY$BZ`@#J5T2#d zcFWS7Z_c?piH{{LqsDW$uM9fq-}0MW@x1qGh%fK<58=N64!Ee9_2zO$U~qkHXOHok zTmD-b>48TZ{?zE2NN)iDw{*e5SaF5TGh~tJqER23NdhmfyDm~&xuU!+1+%zslv0oq zaDUsTwm&0s$PStuBDdVY^LcXpgyfmb#<4=nM<2Sy=qi_dv%NPchCf%j9y~0LhWsCp zXpV{gx@E4c0Fdv`u`RJ;BgU1*c ztEZvY;}f|_tuJ4H@O(7VZd%iXa$Gtg%}lt}IgA*q3@|cU;#FOZ3c1flA0Q>D`~c3Z-axC>v*~LH6}LNJA!O$9 zUWBKZ=AWmR_sS1uU9_JGpCcp`BJMc7!AFBMf^Tg>SphPnBCwPa(wz=0La`G`J7*cd zd$*_BV2b-LPp@RU_fD4i@mjN*p|w3AFWgrBz62`n3?;rAZcmB`3cd8IfO#rTJ ### 4.1 Python推理 -#### 4.1.1 基于训练模型的推理 - -训练完模型后可直接进行推理,以基于Resnet50_vd骨干网络,在ICDAR2015英文数据集训练的模型为例( 训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)),使用方式如下: - -```shell -# 下载模型并解压 -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar -tar xf det_r50_vd_east_v2.0_train.tar -# 执行预测 -python3 tools/infer_det.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.infer_img=./doc/imgs_en/img_10.jpg -``` - -#### 4.1.2 基于paddle预测引擎的推理 - 首先将EAST文本检测训练过程中保存的模型,转换成inference model。以基于Resnet50_vd骨干网络,在ICDAR2015英文数据集训练的模型为例( 训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)),可以使用如下命令进行转换: ```shell diff --git a/doc/doc_ch/ppocr_introduction.md b/doc/doc_ch/ppocr_introduction.md index 2c5f0a22..1e54eecc 100644 --- a/doc/doc_ch/ppocr_introduction.md +++ b/doc/doc_ch/ppocr_introduction.md @@ -38,7 +38,7 @@ PP-OCRv2在PP-OCR的基础上,进一步在5个方面重点优化,检测模 #### PP-OCRv3 -PP-OCRv3在PP-OCRv2的基础上进一步升级。首先,文本检测采用改进fpn网络结构提升检测精度。 +PP-OCRv3在PP-OCRv2的基础上进一步升级。文本检测采用改进fpn网络结构提升检测精度。 diff --git a/doc/doc_en/algorithm_det_east_en.md b/doc/doc_en/algorithm_det_east_en.md index 5de5bef8..bb6184a9 100644 --- a/doc/doc_en/algorithm_det_east_en.md +++ b/doc/doc_en/algorithm_det_east_en.md @@ -50,19 +50,6 @@ After the data download is complete, please refer to [Text Detection Training Tu ### 4.1 Python Inference -#### 4.1.2 Inference for Trained Model -After training, inference can be performed directly. Take [the model trained on the ICDAR2015 English dataset](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar) based on the Resnet50_vd backbone network as an example. - - -```shell -# download trained model -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar -tar xf det_r50_vd_east_v2.0_train.tar -# run inference -python3 tools/infer_det.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.infer_img=./doc/imgs_en/img_10.jpg -``` - -#### 4.1.2 Inference for Inference Model First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert: ```shell -- GitLab