Merge branch 'PaddlePaddle:dygraph' into dygraph
commit
f3c0bc8f99
|
@ -37,7 +37,7 @@ Architecture:
|
|||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
Student2:
|
||||
Teacher:
|
||||
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
freeze_params: false
|
||||
return_all_feats: false
|
||||
|
@ -62,15 +62,15 @@ Loss:
|
|||
loss_config_list:
|
||||
- DistillationDMLLoss:
|
||||
model_name_pairs:
|
||||
- ["Student", "Student2"]
|
||||
- ["Student", "Teacher"]
|
||||
maps_name: "thrink_maps"
|
||||
weight: 1.0
|
||||
act: "softmax"
|
||||
model_name_pairs: ["Student", "Student2"]
|
||||
model_name_pairs: ["Student", "Teacher"]
|
||||
key: maps
|
||||
- DistillationDBLoss:
|
||||
weight: 1.0
|
||||
model_name_list: ["Student", "Student2"]
|
||||
model_name_list: ["Student", "Teacher"]
|
||||
# key: maps
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
|
@ -94,7 +94,7 @@ Optimizer:
|
|||
|
||||
PostProcess:
|
||||
name: DistillationDBPostProcess
|
||||
model_name: ["Student", "Student2"]
|
||||
model_name: ["Student", "Teacher"]
|
||||
key: head_out
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
|
|
|
@ -0,0 +1,131 @@
|
|||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 500
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/v3_en_mobile
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/en_dict.txt
|
||||
max_text_length: &max_text_length 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
distributed: true
|
||||
save_res_path: ./output/rec/predicts_ppocrv3_en.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 3.0e-05
|
||||
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
Head:
|
||||
name: MultiHead
|
||||
head_list:
|
||||
- CTCHead:
|
||||
Neck:
|
||||
name: svtr
|
||||
dims: 64
|
||||
depth: 2
|
||||
hidden_dims: 120
|
||||
use_guide: True
|
||||
Head:
|
||||
fc_decay: 0.00001
|
||||
- SARHead:
|
||||
enc_dim: 512
|
||||
max_text_length: *max_text_length
|
||||
|
||||
Loss:
|
||||
name: MultiLoss
|
||||
loss_config_list:
|
||||
- CTCLoss:
|
||||
- SARLoss:
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
ignore_space: False
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/
|
||||
ext_op_transform_idx: 1
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecConAug:
|
||||
prob: 0.5
|
||||
ext_data_num: 2
|
||||
image_shape: [48, 320, 3]
|
||||
- RecAug:
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: true
|
||||
batch_size_per_card: 128
|
||||
drop_last: true
|
||||
num_workers: 4
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- MultiLabelEncode:
|
||||
- RecResizeImg:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys:
|
||||
- image
|
||||
- label_ctc
|
||||
- label_sar
|
||||
- length
|
||||
- valid_ratio
|
||||
loader:
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 128
|
||||
num_workers: 4
|
|
@ -23,10 +23,9 @@ PP-OCR has supported muti deployment schemes. Click the link to get the specific
|
|||
- [Python Inference](../doc/doc_en/inference_ppocr_en.md)
|
||||
- [C++ Inference](./cpp_infer/readme.md)
|
||||
- [Serving (Python/C++)](./pdserving/README.md)
|
||||
- [Paddle-Lite (ARM CPU/OpenCL ARM GPU/Metal ARM GPU)](./lite/readme.md)
|
||||
- [Paddle-Lite (ARM CPU/OpenCL ARM GPU)](./lite/readme.md)
|
||||
- [Paddle.js](./paddlejs/README.md)
|
||||
- [Jetson Inference]()
|
||||
- [XPU Inference]()
|
||||
- [Paddle2ONNX](./paddle2onnx/readme.md)
|
||||
|
||||
If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。
|
||||
If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。
|
||||
|
|
|
@ -23,10 +23,9 @@ PP-OCR模型已打通多种场景部署方案,点击链接获取具体的使
|
|||
- [Python 推理](../doc/doc_ch/inference_ppocr.md)
|
||||
- [C++ 推理](./cpp_infer/readme_ch.md)
|
||||
- [Serving 服务化部署(Python/C++)](./pdserving/README_CN.md)
|
||||
- [Paddle-Lite 端侧部署(ARM CPU/OpenCL ARM GPU/Metal ARM GPU)](./lite/readme_ch.md)
|
||||
- [Paddle-Lite 端侧部署(ARM CPU/OpenCL ARM GPU)](./lite/readme_ch.md)
|
||||
- [Paddle.js 部署](./paddlejs/README_ch.md)
|
||||
- [Jetson 推理]()
|
||||
- [XPU 推理]()
|
||||
- [Paddle2ONNX 推理](./paddle2onnx/readme_ch.md)
|
||||
|
||||
需要PP-OCR以外的学术算法模型的推理部署,请直接进入相应算法主页面,[入口](../doc/doc_ch/algorithm_overview.md)。
|
||||
需要PP-OCR以外的学术算法模型的推理部署,请直接进入相应算法主页面,[入口](../doc/doc_ch/algorithm_overview.md)。
|
||||
|
|
|
@ -1,19 +1,18 @@
|
|||
- [Tutorial of PaddleOCR Mobile deployment](#tutorial-of-paddleocr-mobile-deployment)
|
||||
- [1. Preparation](#1-preparation)
|
||||
# Mobile deployment based on Paddle-Lite
|
||||
|
||||
- [1. Preparation](#1-preparation)
|
||||
- [Preparation environment](#preparation-environment)
|
||||
- [1.1 Prepare the cross-compilation environment](#11-prepare-the-cross-compilation-environment)
|
||||
- [1.2 Prepare Paddle-Lite library](#12-prepare-paddle-lite-library)
|
||||
- [2 Run](#2-run)
|
||||
- [2. Run](#2-run)
|
||||
- [2.1 Inference Model Optimization](#21-inference-model-optimization)
|
||||
- [2.2 Run optimized model on Phone](#22-run-optimized-model-on-phone)
|
||||
- [注意:](#注意)
|
||||
- [FAQ](#faq)
|
||||
- [FAQ](#faq)
|
||||
|
||||
# Tutorial of PaddleOCR Mobile deployment
|
||||
|
||||
This tutorial will introduce how to use [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy PaddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
|
||||
This tutorial will introduce how to use [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy PaddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
|
||||
|
||||
paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoT, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
|
||||
Paddle-Lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoT, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
|
||||
|
||||
## 1. Preparation
|
||||
|
||||
|
@ -223,7 +222,7 @@ demo/cxx/ocr/
|
|||
|-- ocr_db_crnn.cc C++ main code
|
||||
```
|
||||
|
||||
#### 注意:
|
||||
**Note**:
|
||||
1. `ppocr_keys_v1.txt` is a Chinese dictionary file. If the nb model is used for English recognition or other language recognition, dictionary file should be replaced with a dictionary of the corresponding language. PaddleOCR provides a variety of dictionaries under ppocr/utils/, including:
|
||||
```
|
||||
dict/french_dict.txt # french
|
||||
|
|
|
@ -1,15 +1,14 @@
|
|||
- [端侧部署](#端侧部署)
|
||||
- [1. 准备环境](#1-准备环境)
|
||||
# 端侧部署
|
||||
|
||||
- [1. 准备环境](#1-准备环境)
|
||||
- [运行准备](#运行准备)
|
||||
- [1.1 准备交叉编译环境](#11-准备交叉编译环境)
|
||||
- [1.2 准备预测库](#12-准备预测库)
|
||||
- [2 开始运行](#2-开始运行)
|
||||
- [2 开始运行](#2-开始运行)
|
||||
- [2.1 模型优化](#21-模型优化)
|
||||
- [2.2 与手机联调](#22-与手机联调)
|
||||
- [注意:](#注意)
|
||||
- [FAQ](#faq)
|
||||
- [FAQ](#faq)
|
||||
|
||||
# 端侧部署
|
||||
|
||||
本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleOCR超轻量中文检测、识别模型的详细步骤。
|
||||
|
||||
|
|
|
@ -39,14 +39,14 @@ python3.7 -m pip install onnxruntime==1.9.0
|
|||
有两种方式获取Paddle静态图模型:在 [model_list](../../doc/doc_ch/models_list.md) 中下载PaddleOCR提供的预测模型;
|
||||
参考[模型导出说明](../../doc/doc_ch/inference.md#训练模型转inference模型)把训练好的权重转为 inference_model。
|
||||
|
||||
以 ppocr 中文检测、识别、分类模型为例:
|
||||
以 PP-OCRv3 中文检测、识别、分类模型为例:
|
||||
|
||||
```
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
|
||||
cd ./inference && tar xf ch_PP-OCRv2_det_infer.tar && cd ..
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
|
||||
cd ./inference && tar xf ch_PP-OCRv3_det_infer.tar && cd ..
|
||||
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
|
||||
cd ./inference && tar xf ch_PP-OCRv2_rec_infer.tar && cd ..
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
|
||||
cd ./inference && tar xf ch_PP-OCRv3_rec_infer.tar && cd ..
|
||||
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
|
||||
cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd ..
|
||||
|
@ -57,7 +57,7 @@ cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd ..
|
|||
使用 Paddle2ONNX 将Paddle静态图模型转换为ONNX模型格式:
|
||||
|
||||
```
|
||||
paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
|
||||
paddle2onnx --model_dir ./inference/ch_PP-OCRv3_det_infer \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--save_file ./inference/det_onnx/model.onnx \
|
||||
|
@ -65,7 +65,7 @@ paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
|
|||
--input_shape_dict="{'x':[-1,3,-1,-1]}" \
|
||||
--enable_onnx_checker True
|
||||
|
||||
paddle2onnx --model_dir ./inference/ch_PP-OCRv2_rec_infer \
|
||||
paddle2onnx --model_dir ./inference/ch_PP-OCRv3_rec_infer \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--save_file ./inference/rec_onnx/model.onnx \
|
||||
|
@ -105,8 +105,8 @@ python3.7 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \
|
|||
```
|
||||
python3.7 tools/infer/predict_system.py --use_gpu=False \
|
||||
--cls_model_dir=./inference/ch_ppocr_mobile_v2.0_cls_infer \
|
||||
--rec_model_dir=./inference/ch_PP-OCRv2_rec_infer \
|
||||
--det_model_dir=./inference/ch_PP-OCRv2_det_infer \
|
||||
--rec_model_dir=./inference/ch_PP-OCRv3_rec_infer \
|
||||
--det_model_dir=./inference/ch_PP-OCRv3_det_infer \
|
||||
--image_dir=./deploy/lite/imgs/lite_demo.png
|
||||
```
|
||||
|
||||
|
|
|
@ -15,6 +15,14 @@ Some Key Features of Paddle Serving:
|
|||
- Industrial serving features supported, such as models management, online loading, online A/B testing etc.
|
||||
- Highly concurrent and efficient communication between clients and servers supported.
|
||||
|
||||
PaddleServing supports deployment in multiple languages. In this example, two deployment methods, python pipeline and C++, are provided. The comparison between the two is as follows:
|
||||
|
||||
| Language | Speed | Secondary development | Do you need to compile |
|
||||
|-----|-----|---------|------------|
|
||||
| C++ | fast | Slightly difficult | Single model prediction does not need to be compiled, multi-model concatenation needs to be compiled |
|
||||
| python | general | easy | single-model/multi-model no compilation required |
|
||||
|
||||
|
||||
The introduction and tutorial of Paddle Serving service deployment framework reference [document](https://github.com/PaddlePaddle/Serving/blob/develop/README.md).
|
||||
|
||||
|
||||
|
@ -25,6 +33,7 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
|
|||
- [Environmental preparation](#environmental-preparation)
|
||||
- [Model conversion](#model-conversion)
|
||||
- [Paddle Serving pipeline deployment](#paddle-serving-pipeline-deployment)
|
||||
- [Paddle Serving C++ deployment](#C++)
|
||||
- [WINDOWS Users](#windows-users)
|
||||
- [FAQ](#faq)
|
||||
|
||||
|
@ -41,23 +50,23 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee
|
|||
|
||||
```bash
|
||||
# Install serving which used to start the service
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
|
||||
pip3 install paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
|
||||
pip3 install paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
|
||||
|
||||
# Install paddle-serving-server for cuda10.1
|
||||
# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
|
||||
# pip3 install paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
|
||||
# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
|
||||
# pip3 install paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
|
||||
|
||||
# Install serving which used to start the service
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.7.0-cp37-none-any.whl
|
||||
pip3 install paddle_serving_client-0.7.0-cp37-none-any.whl
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.8.3-cp37-none-any.whl
|
||||
pip3 install paddle_serving_client-0.8.3-cp37-none-any.whl
|
||||
|
||||
# Install serving-app
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.7.0-py3-none-any.whl
|
||||
pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.8.3-py3-none-any.whl
|
||||
pip3 install paddle_serving_app-0.8.3-py3-none-any.whl
|
||||
```
|
||||
|
||||
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md).
|
||||
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Latest_Packages_CN.md).
|
||||
|
||||
|
||||
<a name="model-conversion"></a>
|
||||
|
@ -67,37 +76,37 @@ When using PaddleServing for service deployment, you need to convert the saved i
|
|||
Firstly, download the [inference model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/README_ch.md#pp-ocr%E7%B3%BB%E5%88%97%E6%A8%A1%E5%9E%8B%E5%88%97%E8%A1%A8%E6%9B%B4%E6%96%B0%E4%B8%AD) of PPOCR
|
||||
```
|
||||
# Download and unzip the OCR text detection model
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar -O ch_PP-OCRv2_det_infer.tar && tar -xf ch_PP-OCRv2_det_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar -O ch_PP-OCRv3_det_infer.tar && tar -xf ch_PP-OCRv3_det_infer.tar
|
||||
# Download and unzip the OCR text recognition model
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar -O ch_PP-OCRv2_rec_infer.tar && tar -xf ch_PP-OCRv2_rec_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar -O ch_PP-OCRv3_rec_infer.tar && tar -xf ch_PP-OCRv3_rec_infer.tar
|
||||
```
|
||||
Then, you can use installed paddle_serving_client tool to convert inference model to mobile model.
|
||||
```
|
||||
# Detection model conversion
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_det_infer/ \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--serving_server ./ppocr_det_mobile_2.0_serving/ \
|
||||
--serving_client ./ppocr_det_mobile_2.0_client/
|
||||
--serving_server ./ppocr_det_v3_serving/ \
|
||||
--serving_client ./ppocr_det_v3_client/
|
||||
|
||||
# Recognition model conversion
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--serving_server ./ppocr_rec_mobile_2.0_serving/ \
|
||||
--serving_client ./ppocr_rec_mobile_2.0_client/
|
||||
--serving_server ./ppocr_rec_v3_serving/ \
|
||||
--serving_client ./ppocr_rec_v3_client/
|
||||
|
||||
```
|
||||
|
||||
After the detection model is converted, there will be additional folders of `ppocr_det_mobile_2.0_serving` and `ppocr_det_mobile_2.0_client` in the current folder, with the following format:
|
||||
After the detection model is converted, there will be additional folders of `ppocr_det_v3_serving` and `ppocr_det_v3_client` in the current folder, with the following format:
|
||||
```
|
||||
|- ppocr_det_mobile_2.0_serving/
|
||||
|- ppocr_det_v3_serving/
|
||||
|- __model__
|
||||
|- __params__
|
||||
|- serving_server_conf.prototxt
|
||||
|- serving_server_conf.stream.prototxt
|
||||
|
||||
|- ppocr_det_mobile_2.0_client
|
||||
|- ppocr_det_v3_client
|
||||
|- serving_client_conf.prototxt
|
||||
|- serving_client_conf.stream.prototxt
|
||||
|
||||
|
@ -193,16 +202,13 @@ The recognition model is the same.
|
|||
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
|
||||
```
|
||||
|
||||
<a name="C++"></a>
|
||||
## C++ Serving
|
||||
|
||||
Service deployment based on python obviously has the advantage of convenient secondary development. However, the real application often needs to pursue better performance. PaddleServing also provides a more performant C++ deployment version.
|
||||
|
||||
The C++ service deployment is the same as python in the environment setup and data preparation stages, the difference is when the service is started and the client sends requests.
|
||||
|
||||
| Language | Speed | Secondary development | Do you need to compile |
|
||||
|-----|-----|---------|------------|
|
||||
| C++ | fast | Slightly difficult | Single model prediction does not need to be compiled, multi-model concatenation needs to be compiled |
|
||||
| python | general | easy | single-model/multi-model no compilation required |
|
||||
|
||||
1. Compile Serving
|
||||
|
||||
|
@ -211,7 +217,7 @@ The C++ service deployment is the same as python in the environment setup and da
|
|||
2. Run the following command to start the service.
|
||||
```
|
||||
# Start the service and save the running log in log.txt
|
||||
python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
|
||||
python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
|
||||
```
|
||||
After the service is successfully started, a log similar to the following will be printed in log.txt
|
||||

|
||||
|
@ -219,7 +225,7 @@ The C++ service deployment is the same as python in the environment setup and da
|
|||
3. Send service request
|
||||
|
||||
Due to the need for pre and post-processing in the C++Server part, in order to speed up the input to the C++Server is only the base64 encoded string of the picture, it needs to be manually modified
|
||||
Change the feed_type field and shape field in ppocrv2_det_client/serving_client_conf.prototxt to the following:
|
||||
Change the feed_type field and shape field in ppocr_det_v3_client/serving_client_conf.prototxt to the following:
|
||||
|
||||
```
|
||||
feed_var {
|
||||
|
@ -234,7 +240,7 @@ The C++ service deployment is the same as python in the environment setup and da
|
|||
start the client:
|
||||
|
||||
```
|
||||
python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
|
||||
python3 ocr_cpp_client.py ppocr_det_v3_client ppocr_rec_v3_client
|
||||
```
|
||||
After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:
|
||||

|
||||
|
|
|
@ -9,13 +9,21 @@ PaddleOCR提供2种服务部署方式:
|
|||
|
||||
# 基于PaddleServing的服务部署
|
||||
|
||||
本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PP-OCR动态图模型的pipeline在线服务。
|
||||
本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md) 工具部署PP-OCR动态图模型的pipeline在线服务。
|
||||
|
||||
相比较于hubserving部署,PaddleServing具备以下优点:
|
||||
- 支持客户端和服务端之间高并发和高效通信
|
||||
- 支持 工业级的服务能力 例如模型管理,在线加载,在线A/B测试等
|
||||
- 支持 多种编程语言 开发客户端,例如C++, Python和Java
|
||||
|
||||
PaddleServing 支持多种语言部署,本例中提供了python pipeline 和 C++ 两种部署方式,两者的对比如下:
|
||||
|
||||
| 语言 | 速度 | 二次开发 | 是否需要编译 |
|
||||
|-----|-----|---------|------------|
|
||||
| C++ | 很快 | 略有难度 | 单模型预测无需编译,多模型串联需要编译 |
|
||||
| python | 一般 | 容易 | 单模型/多模型 均无需编译|
|
||||
|
||||
|
||||
更多有关PaddleServing服务化部署框架介绍和使用教程参考[文档](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)。
|
||||
|
||||
AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](https://aistudio.baidu.com/aistudio/projectdetail/3630726)。
|
||||
|
@ -24,6 +32,7 @@ AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](h
|
|||
- [环境准备](#环境准备)
|
||||
- [模型转换](#模型转换)
|
||||
- [Paddle Serving pipeline部署](#部署)
|
||||
- [Paddle Serving C++部署](#C++)
|
||||
- [Windows用户](#Windows用户)
|
||||
- [FAQ](#FAQ)
|
||||
|
||||
|
@ -34,26 +43,33 @@ AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](h
|
|||
|
||||
- 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
|
||||
|
||||
```
|
||||
git clone https://github.com/PaddlePaddle/PaddleOCR
|
||||
|
||||
# 进入到工作目录
|
||||
cd PaddleOCR/deploy/pdserving/
|
||||
```
|
||||
|
||||
- 准备PaddleServing的运行环境,步骤如下
|
||||
|
||||
```bash
|
||||
# 安装serving,用于启动服务
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
|
||||
pip3 install paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
|
||||
pip3 install paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
|
||||
# 如果是cuda10.1环境,可以使用下面的命令安装paddle-serving-server
|
||||
# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
|
||||
# pip3 install paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
|
||||
# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
|
||||
# pip3 install paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
|
||||
|
||||
# 安装client,用于向服务发送请求
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.7.0-cp37-none-any.whl
|
||||
pip3 install paddle_serving_client-0.7.0-cp37-none-any.whl
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.8.3-cp37-none-any.whl
|
||||
pip3 install paddle_serving_client-0.8.3-cp37-none-any.whl
|
||||
|
||||
# 安装serving-app
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.7.0-py3-none-any.whl
|
||||
pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.8.3-py3-none-any.whl
|
||||
pip3 install paddle_serving_app-0.8.3-py3-none-any.whl
|
||||
```
|
||||
|
||||
**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md)。
|
||||
**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Latest_Packages_CN.md)。
|
||||
|
||||
<a name="模型转换"></a>
|
||||
## 模型转换
|
||||
|
@ -64,38 +80,38 @@ pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
|
|||
|
||||
```bash
|
||||
# 下载并解压 OCR 文本检测模型
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar -O ch_PP-OCRv2_det_infer.tar && tar -xf ch_PP-OCRv2_det_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar -O ch_PP-OCRv3_det_infer.tar && tar -xf ch_PP-OCRv3_det_infer.tar
|
||||
# 下载并解压 OCR 文本识别模型
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar -O ch_PP-OCRv2_rec_infer.tar && tar -xf ch_PP-OCRv2_rec_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar -O ch_PP-OCRv3_rec_infer.tar && tar -xf ch_PP-OCRv3_rec_infer.tar
|
||||
```
|
||||
|
||||
接下来,用安装的paddle_serving_client把下载的inference模型转换成易于server部署的模型格式。
|
||||
|
||||
```bash
|
||||
# 转换检测模型
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_det_infer/ \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--serving_server ./ppocr_det_mobile_2.0_serving/ \
|
||||
--serving_client ./ppocr_det_mobile_2.0_client/
|
||||
--serving_server ./ppocr_det_v3_serving/ \
|
||||
--serving_client ./ppocr_det_v3_client/
|
||||
|
||||
# 转换识别模型
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--serving_server ./ppocr_rec_mobile_2.0_serving/ \
|
||||
--serving_client ./ppocr_rec_mobile_2.0_client/
|
||||
--serving_server ./ppocr_rec_v3_serving/ \
|
||||
--serving_client ./ppocr_rec_v3_client/
|
||||
```
|
||||
|
||||
检测模型转换完成后,会在当前文件夹多出`ppocr_det_mobile_2.0_serving` 和`ppocr_det_mobile_2.0_client`的文件夹,具备如下格式:
|
||||
检测模型转换完成后,会在当前文件夹多出`ppocr_det_v3_serving` 和`ppocr_det_v3_client`的文件夹,具备如下格式:
|
||||
```
|
||||
|- ppocr_det_mobile_2.0_serving/
|
||||
|- ppocr_det_v3_serving/
|
||||
|- __model__
|
||||
|- __params__
|
||||
|- serving_server_conf.prototxt
|
||||
|- serving_server_conf.stream.prototxt
|
||||
|
||||
|- ppocr_det_mobile_2.0_client
|
||||
|- ppocr_det_v3_client
|
||||
|- serving_client_conf.prototxt
|
||||
|- serving_client_conf.stream.prototxt
|
||||
|
||||
|
@ -105,13 +121,8 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
|
|||
<a name="部署"></a>
|
||||
## Paddle Serving pipeline部署
|
||||
|
||||
1. 下载PaddleOCR代码,若已下载可跳过此步骤
|
||||
```
|
||||
git clone https://github.com/PaddlePaddle/PaddleOCR
|
||||
1. 确认工作目录下文件结构:
|
||||
|
||||
# 进入到工作目录
|
||||
cd PaddleOCR/deploy/pdserving/
|
||||
```
|
||||
pdserver目录包含启动pipeline服务和发送预测请求的代码,包括:
|
||||
```
|
||||
__init__.py
|
||||
|
@ -196,16 +207,12 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
|
|||
|
||||
C++ 服务部署在环境搭建和数据准备阶段与 python 相同,区别在于启动服务和客户端发送请求时不同。
|
||||
|
||||
| 语言 | 速度 | 二次开发 | 是否需要编译 |
|
||||
|-----|-----|---------|------------|
|
||||
| C++ | 很快 | 略有难度 | 单模型预测无需编译,多模型串联需要编译 |
|
||||
| python | 一般 | 容易 | 单模型/多模型 均无需编译|
|
||||
|
||||
1. 准备 Serving 环境
|
||||
|
||||
为了提高预测性能,C++ 服务同样提供了多模型串联服务。与python pipeline服务不同,多模型串联的过程中需要将模型前后处理代码写在服务端,因此需要在本地重新编译生成serving。
|
||||
|
||||
首先需要下载Serving代码库, 把OCR文本检测预处理相关代码替换到Serving库中
|
||||
|
||||
```
|
||||
git clone https://github.com/PaddlePaddle/Serving
|
||||
|
||||
|
@ -223,7 +230,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
|
|||
|
||||
```
|
||||
# 启动服务,运行日志保存在log.txt
|
||||
python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
|
||||
python3 -m paddle_serving_server.serve --model ppocr_det_v3_serving ppocr_rec_v3_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
|
||||
```
|
||||
成功启动服务后,log.txt中会打印类似如下日志
|
||||

|
||||
|
@ -231,7 +238,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
|
|||
3. 发送服务请求:
|
||||
|
||||
由于需要在C++Server部分进行前后处理,为了加速传入C++Server的仅仅是图片的base64编码的字符串,故需要手动修改
|
||||
ppocrv2_det_client/serving_client_conf.prototxt 中 feed_type 字段 和 shape 字段,修改成如下内容:
|
||||
ppocr_det_v3_client/serving_client_conf.prototxt 中 feed_type 字段 和 shape 字段,修改成如下内容:
|
||||
```
|
||||
feed_var {
|
||||
name: "x"
|
||||
|
@ -243,7 +250,7 @@ cp -rf general_detection_op.cpp Serving/core/general-server/op
|
|||
```
|
||||
启动客户端
|
||||
```
|
||||
python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
|
||||
python3 ocr_cpp_client.py ppocr_det_v3_client ppocr_rec_v3_client
|
||||
```
|
||||
|
||||
成功运行后,模型预测的结果会打印在cmd窗口中,结果示例为:
|
||||
|
|
|
@ -34,7 +34,7 @@ op:
|
|||
client_type: local_predictor
|
||||
|
||||
#det模型路径
|
||||
model_config: ./ppocr_det_mobile_2.0_serving
|
||||
model_config: ./ppocr_det_v3_serving
|
||||
|
||||
#Fetch结果列表,以client_config中fetch_var的alias_name为准
|
||||
fetch_list: ["save_infer_model/scale_0.tmp_1"]
|
||||
|
@ -60,10 +60,10 @@ op:
|
|||
client_type: local_predictor
|
||||
|
||||
#rec模型路径
|
||||
model_config: ./ppocr_rec_mobile_2.0_serving
|
||||
model_config: ./ppocr_rec_v3_serving
|
||||
|
||||
#Fetch结果列表,以client_config中fetch_var的alias_name为准
|
||||
fetch_list: ["save_infer_model/scale_0.tmp_1"]
|
||||
fetch_list: ["softmax_5.tmp_0"]
|
||||
|
||||
#计算硬件ID,当devices为""或不写时为CPU预测;当devices为"0", "0,1,2"时为GPU预测,表示使用的GPU卡
|
||||
devices: "0"
|
||||
|
|
|
@ -392,38 +392,8 @@ class OCRReader(object):
|
|||
|
||||
return norm_img_batch[0]
|
||||
|
||||
def postprocess_old(self, outputs, with_score=False):
|
||||
rec_res = []
|
||||
rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
|
||||
rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
|
||||
if with_score:
|
||||
predict_lod = outputs["softmax_0.tmp_0.lod"]
|
||||
for rno in range(len(rec_idx_lod) - 1):
|
||||
beg = rec_idx_lod[rno]
|
||||
end = rec_idx_lod[rno + 1]
|
||||
if isinstance(rec_idx_batch, list):
|
||||
rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
|
||||
else: #nd array
|
||||
rec_idx_tmp = rec_idx_batch[beg:end, 0]
|
||||
preds_text = self.char_ops.decode(rec_idx_tmp)
|
||||
if with_score:
|
||||
beg = predict_lod[rno]
|
||||
end = predict_lod[rno + 1]
|
||||
if isinstance(outputs["softmax_0.tmp_0"], list):
|
||||
outputs["softmax_0.tmp_0"] = np.array(outputs[
|
||||
"softmax_0.tmp_0"]).astype(np.float32)
|
||||
probs = outputs["softmax_0.tmp_0"][beg:end, :]
|
||||
ind = np.argmax(probs, axis=1)
|
||||
blank = probs.shape[1]
|
||||
valid_ind = np.where(ind != (blank - 1))[0]
|
||||
score = np.mean(probs[valid_ind, ind[valid_ind]])
|
||||
rec_res.append([preds_text, score])
|
||||
else:
|
||||
rec_res.append([preds_text])
|
||||
return rec_res
|
||||
|
||||
def postprocess(self, outputs, with_score=False):
|
||||
preds = outputs["save_infer_model/scale_0.tmp_1"]
|
||||
preds = outputs["softmax_5.tmp_0"]
|
||||
try:
|
||||
preds = preds.numpy()
|
||||
except:
|
||||
|
|
|
@ -392,38 +392,8 @@ class OCRReader(object):
|
|||
|
||||
return norm_img_batch[0]
|
||||
|
||||
def postprocess_old(self, outputs, with_score=False):
|
||||
rec_res = []
|
||||
rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
|
||||
rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
|
||||
if with_score:
|
||||
predict_lod = outputs["softmax_0.tmp_0.lod"]
|
||||
for rno in range(len(rec_idx_lod) - 1):
|
||||
beg = rec_idx_lod[rno]
|
||||
end = rec_idx_lod[rno + 1]
|
||||
if isinstance(rec_idx_batch, list):
|
||||
rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
|
||||
else: #nd array
|
||||
rec_idx_tmp = rec_idx_batch[beg:end, 0]
|
||||
preds_text = self.char_ops.decode(rec_idx_tmp)
|
||||
if with_score:
|
||||
beg = predict_lod[rno]
|
||||
end = predict_lod[rno + 1]
|
||||
if isinstance(outputs["softmax_0.tmp_0"], list):
|
||||
outputs["softmax_0.tmp_0"] = np.array(outputs[
|
||||
"softmax_0.tmp_0"]).astype(np.float32)
|
||||
probs = outputs["softmax_0.tmp_0"][beg:end, :]
|
||||
ind = np.argmax(probs, axis=1)
|
||||
blank = probs.shape[1]
|
||||
valid_ind = np.where(ind != (blank - 1))[0]
|
||||
score = np.mean(probs[valid_ind, ind[valid_ind]])
|
||||
rec_res.append([preds_text, score])
|
||||
else:
|
||||
rec_res.append([preds_text])
|
||||
return rec_res
|
||||
|
||||
def postprocess(self, outputs, with_score=False):
|
||||
preds = outputs["save_infer_model/scale_0.tmp_1"]
|
||||
preds = outputs["softmax_5.tmp_0"]
|
||||
try:
|
||||
preds = preds.numpy()
|
||||
except:
|
||||
|
|
|
@ -0,0 +1,140 @@
|
|||
# CRNN
|
||||
|
||||
- [1. 算法简介](#1)
|
||||
- [2. 环境配置](#2)
|
||||
- [3. 模型训练、评估、预测](#3)
|
||||
- [3.1 训练](#3-1)
|
||||
- [3.2 评估](#3-2)
|
||||
- [3.3 预测](#3-3)
|
||||
- [4. 推理部署](#4)
|
||||
- [4.1 Python推理](#4-1)
|
||||
- [4.2 C++推理](#4-2)
|
||||
- [4.3 Serving服务化部署](#4-3)
|
||||
- [4.4 更多推理部署](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. 算法简介
|
||||
|
||||
论文信息:
|
||||
> [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://arxiv.org/abs/1507.05717)
|
||||
|
||||
> Baoguang Shi, Xiang Bai, Cong Yao
|
||||
|
||||
> IEEE, 2015
|
||||
|
||||
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|配置文件|下载链接|
|
||||
|---|---|---|---|---|
|
||||
|CRNN|Resnet34_vd|81.04%|[configs/rec/rec_r34_vd_none_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
|
||||
|CRNN|MobileNetV3|77.95%|[configs/rec/rec_mv3_none_bilstm_ctc.yml](../../configs/rec/rec_mv3_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. 环境配置
|
||||
请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境,参考[《项目克隆》](./clone.md)克隆项目代码。
|
||||
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. 模型训练、评估、预测
|
||||
|
||||
请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化,训练不同的识别模型只需要**更换配置文件**即可。
|
||||
|
||||
- 训练
|
||||
|
||||
在完成数据准备后,便可以启动训练,训练命令如下:
|
||||
|
||||
```
|
||||
#单卡训练(训练周期长,不建议)
|
||||
python3 tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml
|
||||
|
||||
#多卡训练,通过--gpus参数指定卡号
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_r34_vd_none_bilstm_ctc.yml
|
||||
|
||||
```
|
||||
|
||||
- 评估
|
||||
|
||||
```
|
||||
# GPU 评估, Global.pretrained_model 为待测权重
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
- 预测:
|
||||
|
||||
```
|
||||
# 预测使用的配置文件必须与训练一致
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. 推理部署
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python推理
|
||||
|
||||
首先将 CRNN 文本识别训练过程中保存的模型,转换成inference model。以基于Resnet34_vd骨干网络,使用MJSynth和SynthText两个英文文本识别合成数据集训练的[模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) 为例,可以使用如下命令进行转换:
|
||||
```shell
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn
|
||||
```
|
||||
CRNN 文本识别模型推理,可以执行如下命令:
|
||||
|
||||
```shell
|
||||
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
|
||||
```
|
||||
|
||||

|
||||
|
||||
执行命令后,上面图像的识别结果如下:
|
||||
|
||||
```bash
|
||||
Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
|
||||
```
|
||||
|
||||
**注意**:由于上述模型是参考[DTRB](https://arxiv.org/abs/1904.01906)文本识别训练和评估流程,与超轻量级中文识别模型训练有两方面不同:
|
||||
|
||||
- 训练时采用的图像分辨率不同,训练上述模型采用的图像分辨率是[3,32,100],而中文模型训练时,为了保证长文本的识别效果,训练时采用的图像分辨率是[3, 32, 320]。预测推理程序默认的的形状参数是训练中文采用的图像分辨率,即[3, 32, 320]。因此,这里推理上述英文模型时,需要通过参数rec_image_shape设置识别图像的形状。
|
||||
|
||||
- 字符列表,DTRB论文中实验只是针对26个小写英文本母和10个数字进行实验,总共36个字符。所有大小字符都转成了小写字符,不在上面列表的字符都忽略,认为是空格。因此这里没有输入字符字典,而是通过如下命令生成字典.因此在推理时需要设置参数rec_char_dict_path,指定为英文字典"./ppocr/utils/ic15_dict.txt"。
|
||||
|
||||
```
|
||||
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
|
||||
dict_character = list(self.character_str)
|
||||
```
|
||||
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++推理
|
||||
|
||||
准备好推理模型后,参考[cpp infer](../../deploy/cpp_infer/)教程进行操作即可。
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving服务化部署
|
||||
|
||||
准备好推理模型后,参考[pdserving](../../deploy/pdserving/)教程进行Serving服务化部署,包括Python Serving和C++ Serving两种模式。
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 更多推理部署
|
||||
|
||||
CRNN模型还支持以下推理部署方式:
|
||||
|
||||
- Paddle2ONNX推理:准备好推理模型后,参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
|
||||
## 引用
|
||||
|
||||
```bibtex
|
||||
@ARTICLE{7801919,
|
||||
author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
|
||||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
|
||||
title={An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition},
|
||||
year={2017},
|
||||
volume={39},
|
||||
number={11},
|
||||
pages={2298-2304},
|
||||
doi={10.1109/TPAMI.2016.2646371}}
|
||||
```
|
|
@ -0,0 +1,113 @@
|
|||
# SEED
|
||||
|
||||
- [1. 算法简介](#1)
|
||||
- [2. 环境配置](#2)
|
||||
- [3. 模型训练、评估、预测](#3)
|
||||
- [3.1 训练](#3-1)
|
||||
- [3.2 评估](#3-2)
|
||||
- [3.3 预测](#3-3)
|
||||
- [4. 推理部署](#4)
|
||||
- [4.1 Python推理](#4-1)
|
||||
- [4.2 C++推理](#4-2)
|
||||
- [4.3 Serving服务化部署](#4-3)
|
||||
- [4.4 更多推理部署](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. 算法简介
|
||||
|
||||
论文信息:
|
||||
> [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://arxiv.org/pdf/2005.10977.pdf)
|
||||
|
||||
> Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping
|
||||
|
||||
> CVPR, 2020
|
||||
|
||||
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|配置文件|下载链接|
|
||||
|---|---|---|---|---|
|
||||
|SEED|Aster_Resnet| 85.2% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. 环境配置
|
||||
请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境,参考[《项目克隆》](./clone.md)克隆项目代码。
|
||||
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. 模型训练、评估、预测
|
||||
|
||||
请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化,训练不同的识别模型只需要**更换配置文件**即可。
|
||||
|
||||
- 训练
|
||||
|
||||
SEED模型需要额外加载FastText训练好的[语言模型](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) ,并且安装 fasttext 依赖:
|
||||
|
||||
```
|
||||
python3 -m pip install fasttext==0.9.1
|
||||
```
|
||||
|
||||
然后,在完成数据准备后,便可以启动训练,训练命令如下:
|
||||
|
||||
```
|
||||
#单卡训练(训练周期长,不建议)
|
||||
python3 tools/train.py -c configs/rec/rec_resnet_stn_bilstm_att.yml
|
||||
|
||||
#多卡训练,通过--gpus参数指定卡号
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_resnet_stn_bilstm_att.yml
|
||||
|
||||
```
|
||||
|
||||
- 评估
|
||||
|
||||
```
|
||||
# GPU 评估, Global.pretrained_model 为待测权重
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
- 预测:
|
||||
|
||||
```
|
||||
# 预测使用的配置文件必须与训练一致
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. 推理部署
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python推理
|
||||
|
||||
comming soon
|
||||
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++推理
|
||||
|
||||
comming soon
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving服务化部署
|
||||
|
||||
comming soon
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 更多推理部署
|
||||
|
||||
comming soon
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
|
||||
## 引用
|
||||
|
||||
```bibtex
|
||||
@inproceedings{qiao2020seed,
|
||||
title={Seed: Semantics enhanced encoder-decoder framework for scene text recognition},
|
||||
author={Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping},
|
||||
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
|
||||
pages={13528--13537},
|
||||
year={2020}
|
||||
}
|
||||
```
|
|
@ -0,0 +1,139 @@
|
|||
# STAR-Net
|
||||
|
||||
- [1. 算法简介](#1)
|
||||
- [2. 环境配置](#2)
|
||||
- [3. 模型训练、评估、预测](#3)
|
||||
- [3.1 训练](#3-1)
|
||||
- [3.2 评估](#3-2)
|
||||
- [3.3 预测](#3-3)
|
||||
- [4. 推理部署](#4)
|
||||
- [4.1 Python推理](#4-1)
|
||||
- [4.2 C++推理](#4-2)
|
||||
- [4.3 Serving服务化部署](#4-3)
|
||||
- [4.4 更多推理部署](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. 算法简介
|
||||
|
||||
论文信息:
|
||||
> [STAR-Net: a spatial attention residue network for scene text recognition.](http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf)
|
||||
|
||||
> Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han.
|
||||
|
||||
> BMVC, pages 43.1-43.13, 2016
|
||||
|
||||
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|配置文件|下载链接|
|
||||
|---|---|---|---|---|
|
||||
|StarNet|Resnet34_vd|84.44%|[configs/rec/rec_r34_vd_tps_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_tps_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
|
||||
|StarNet|MobileNetV3|81.42%|[configs/rec/rec_mv3_tps_bilstm_ctc.yml](../../configs/rec/rec_mv3_tps_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. 环境配置
|
||||
请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境,参考[《项目克隆》](./clone.md)克隆项目代码。
|
||||
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. 模型训练、评估、预测
|
||||
|
||||
请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化,训练不同的识别模型只需要**更换配置文件**即可。
|
||||
|
||||
- 训练
|
||||
|
||||
在完成数据准备后,便可以启动训练,训练命令如下:
|
||||
|
||||
```
|
||||
#单卡训练(训练周期长,不建议)
|
||||
python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
|
||||
|
||||
#多卡训练,通过--gpus参数指定卡号
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_r34_vd_tps_bilstm_ctc.yml
|
||||
|
||||
```
|
||||
|
||||
- 评估
|
||||
|
||||
```
|
||||
# GPU 评估, Global.pretrained_model 为待测权重
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
- 预测:
|
||||
|
||||
```
|
||||
# 预测使用的配置文件必须与训练一致
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. 推理部署
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python推理
|
||||
|
||||
首先将 STAR-Net 文本识别训练过程中保存的模型,转换成inference model。以基于Resnet34_vd骨干网络,使用MJSynth和SynthText两个英文文本识别合成数据集训练的[模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) 为例,可以使用如下命令进行转换:
|
||||
```shell
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_starnet
|
||||
```
|
||||
STAR-Net 文本识别模型推理,可以执行如下命令:
|
||||
|
||||
```shell
|
||||
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
|
||||
```
|
||||
|
||||

|
||||
|
||||
执行命令后,上面图像的识别结果如下:
|
||||
|
||||
```bash
|
||||
Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
|
||||
```
|
||||
|
||||
**注意**:由于上述模型是参考[DTRB](https://arxiv.org/abs/1904.01906)文本识别训练和评估流程,与超轻量级中文识别模型训练有两方面不同:
|
||||
|
||||
- 训练时采用的图像分辨率不同,训练上述模型采用的图像分辨率是[3,32,100],而中文模型训练时,为了保证长文本的识别效果,训练时采用的图像分辨率是[3, 32, 320]。预测推理程序默认的的形状参数是训练中文采用的图像分辨率,即[3, 32, 320]。因此,这里推理上述英文模型时,需要通过参数rec_image_shape设置识别图像的形状。
|
||||
|
||||
- 字符列表,DTRB论文中实验只是针对26个小写英文本母和10个数字进行实验,总共36个字符。所有大小字符都转成了小写字符,不在上面列表的字符都忽略,认为是空格。因此这里没有输入字符字典,而是通过如下命令生成字典.因此在推理时需要设置参数rec_char_dict_path,指定为英文字典"./ppocr/utils/ic15_dict.txt"。
|
||||
|
||||
```
|
||||
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
|
||||
dict_character = list(self.character_str)
|
||||
```
|
||||
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++推理
|
||||
|
||||
准备好推理模型后,参考[cpp infer](../../deploy/cpp_infer/)教程进行操作即可。
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving服务化部署
|
||||
|
||||
准备好推理模型后,参考[pdserving](../../deploy/pdserving/)教程进行Serving服务化部署,包括Python Serving和C++ Serving两种模式。
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 更多推理部署
|
||||
|
||||
STAR-Net模型还支持以下推理部署方式:
|
||||
|
||||
- Paddle2ONNX推理:准备好推理模型后,参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
|
||||
## 引用
|
||||
|
||||
```bibtex
|
||||
@inproceedings{liu2016star,
|
||||
title={STAR-Net: a spatial attention residue network for scene text recognition.},
|
||||
author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
|
||||
booktitle={BMVC},
|
||||
volume={2},
|
||||
pages={7},
|
||||
year={2016}
|
||||
}
|
||||
```
|
|
@ -176,8 +176,62 @@ ppocr 还支持方向分类, 更多使用方式请参考:[whl包使用说明
|
|||
ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别模型可以参考 [法语配置文件](../../configs/rec/multi_language/rec_french_lite_train.yml)
|
||||
修改训练数据路径、字典等参数。
|
||||
|
||||
具体数据准备、训练过程可参考:[文本检测](../doc_ch/detection.md)、[文本识别](../doc_ch/recognition.md),更多功能如预测部署、
|
||||
数据标注等功能可以阅读完整的[文档教程](../../README_ch.md)。
|
||||
详细数据准备、训练过程可参考:[文本识别](../doc_ch/recognition.md)、[文本检测](../doc_ch/detection.md)。
|
||||
|
||||
假设已经准备好了训练数据,可根据以下步骤快速启动训练:
|
||||
|
||||
- 修改配置文件
|
||||
|
||||
以 `rec_french_lite_train.yml` 为例:
|
||||
|
||||
```
|
||||
Global:
|
||||
...
|
||||
# 添加自定义字典,如修改字典请将路径指向新字典
|
||||
character_dict_path: ./ppocr/utils/dict/french_dict.txt
|
||||
...
|
||||
# 识别空格
|
||||
use_space_char: True
|
||||
|
||||
...
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
# 数据集格式,支持LMDBDataSet以及SimpleDataSet
|
||||
name: SimpleDataSet
|
||||
# 数据集路径
|
||||
data_dir: ./train_data/
|
||||
# 训练集标签文件
|
||||
label_file_list: ["./train_data/french_train.txt"]
|
||||
...
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
# 数据集格式,支持LMDBDataSet以及SimpleDataSet
|
||||
name: SimpleDataSet
|
||||
# 数据集路径
|
||||
data_dir: ./train_data
|
||||
# 验证集标签文件
|
||||
label_file_list: ["./train_data/french_val.txt"]
|
||||
...
|
||||
```
|
||||
|
||||
- 启动训练:
|
||||
|
||||
```
|
||||
# 下载预训练模型
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar
|
||||
tar -xf french_mobile_v2.0_rec_train.tar
|
||||
|
||||
#加载预训练模型 单卡训练
|
||||
python3 tools/train.py -c configs/rec/rec_french_lite_train.yml -o Global.pretrained_model=french_mobile_v2.0_rec_train/best_accuracy
|
||||
|
||||
#加载预训练模型 多卡训练,通过--gpus参数指定卡号
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_french_lite_train.yml -o Global.pretrained_model=french_mobile_v2.0_rec_train/best_accuracy
|
||||
```
|
||||
|
||||
|
||||
更多功能如预测部署、数据标注等功能可以阅读完整的[文档教程](../../README_ch.md)。
|
||||
|
||||
<a name="预测部署"></a>
|
||||
## 4 预测部署
|
||||
|
|
|
@ -52,6 +52,27 @@ PP-OCRv3文本检测从网络结构、蒸馏训练策略两个方向做了进一
|
|||
|4|1 + 2 + LKPAN|4.6M|86.0|156ms|
|
||||
|
||||
|
||||
PP-OCRv3识别从网络结构、训练策略、数据增强三个方向做了进一步优化:
|
||||
- 网络结构上:使用[SVTR](todo:add_link)中的 Transformer block 替换LSTM,提升模型精度和预测速度;
|
||||
- 训练策略上:参考 [GTC](https://arxiv.org/pdf/2002.01276.pdf) 策略,使用注意力机制模块指导CTC训练,定位和识别字符,提升不规则文本的识别精度;设计方向分类前序任务,获取更优预训练模型,加速模型收敛过程,提升精度。
|
||||
- 数据增强上:使用[RecConAug](todo:add_link)数据增广方法,随机结合图片,提升训练数据的上下文信息丰富度,增强模型鲁棒性。
|
||||
|
||||
基于上述策略,PP-OCRv3识别模型相比上一版本,速度加速30%,精度进一步提升4.5%。 具体消融实验:
|
||||
|
||||
| id | 策略 | 模型大小 | 精度 | CPU+mkldnn 预测耗时 |
|
||||
|-----|-----|--------|----|------------|
|
||||
| 01 | PP-OCRv2 | 8M | 69.3% | 26ms |
|
||||
| 02 | SVTR_tiny | 19M | 80.1% | - |
|
||||
| 03 | LCNet_SVTR_G6 | 8.2M | 76% | - |
|
||||
| 04 | LCNet_SVTR_G1 | - | - | - |
|
||||
| 05 | PP-OCRv3 | 12M | 71.9% | 19ms |
|
||||
| 06 | + GTC | 12M | 75.8% | 19ms |
|
||||
| 07 | + RecConAug | 12M | 76.3% | 19ms |
|
||||
| 08 | + SSL pretrain | 12M | 76.9% | 19ms |
|
||||
| 09 | + UDML | 12M | 78.4% | 19ms |
|
||||
| 10 | + unlabeled data | 12M | 79.4% | 19ms |
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. 特性
|
||||
|
||||
|
@ -129,7 +150,7 @@ PP-OCR中英文模型列表如下:
|
|||
| 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PP-OCRv3模型(16.2M) | ch_PP-OCRv3_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
|
||||
| 英文超轻量PP-OCRv3模型(13.4M) | en_PP-OCRv3_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
|
||||
| 英文超轻量PP-OCRv3模型(13.4M) | en_PP-OCRv3_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
|
||||
| 中英文超轻量PP-OCRv2模型(13.0M) | ch_PP-OCRv2_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
|
||||
| 中英文超轻量PP-OCR mobile模型(9.4M) | ch_ppocr_mobile_v2.0_xx | 移动端&服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
|
||||
| 中英文通用PP-OCR server模型(143.4M) | ch_ppocr_server_v2.0_xx | 服务器端 | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
|
||||
|
|
|
@ -105,8 +105,6 @@ train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单
|
|||
|
||||
若您本地没有数据集,可以在官网下载 [ICDAR2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据,用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ,下载 benchmark 所需的lmdb格式数据集。
|
||||
|
||||
如果希望复现SAR的论文指标,需要下载[SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg), 提取码:627x。此外,真实数据集icdar2013, icdar2015, cocotext, IIIT5也作为训练数据的一部分。具体数据细节可以参考论文SAR。
|
||||
|
||||
如果你使用的是icdar2015的公开数据集,PaddleOCR 提供了一份用于训练 ICDAR2015 数据集的标签文件,通过以下方式下载:
|
||||
|
||||
```
|
||||
|
@ -169,12 +167,13 @@ PaddleOCR内置了一部分字典,可以按需使用。
|
|||
|
||||
`ppocr/utils/en_dict.txt` 是一个包含96个字符的英文字典
|
||||
|
||||
|
||||
目前的多语言模型仍处在demo阶段,会持续优化模型并补充语种,**非常欢迎您为我们提供其他语言的字典和字体**,
|
||||
如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict),我们会在Repo中感谢您。
|
||||
|
||||
- 自定义字典
|
||||
|
||||
如需自定义dic文件,请在 `configs/rec/rec_icdar15_train.yml` 中添加 `character_dict_path` 字段, 指向您的字典路径。
|
||||
如需自定义dic文件,请在 `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` 中添加 `character_dict_path` 字段, 指向您的字典路径。
|
||||
|
||||
<a name="支持空格"></a>
|
||||
## 1.4 添加空格类别
|
||||
|
@ -195,7 +194,7 @@ PaddleOCR提供了多种数据增强方式,默认配置文件中已经添加
|
|||
<a name="开始训练"></a>
|
||||
# 2. 开始训练
|
||||
|
||||
PaddleOCR提供了训练脚本、评估脚本和预测脚本,本节将以 CRNN 识别模型为例:
|
||||
PaddleOCR提供了训练脚本、评估脚本和预测脚本,本节将以 PP-OCRv3 英文识别模型为例:
|
||||
|
||||
<a name="启动训练"></a>
|
||||
## 2.1 启动训练
|
||||
|
@ -204,11 +203,11 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本,本节将以 CRNN
|
|||
|
||||
```
|
||||
cd PaddleOCR/
|
||||
# 下载MobileNetV3的预训练模型
|
||||
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
|
||||
# 下载英文PP-OCRv3的预训练模型
|
||||
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar
|
||||
# 解压模型参数
|
||||
cd pretrain_models
|
||||
tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
|
||||
tar -xf en_PP-OCRv3_rec_train.tar && rm -rf en_PP-OCRv3_rec_train.tar
|
||||
```
|
||||
|
||||
开始训练:
|
||||
|
@ -220,44 +219,23 @@ tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc
|
|||
# 训练icdar15英文数据 训练日志会自动保存为 "{save_model_dir}" 下的train.log
|
||||
|
||||
#单卡训练(训练周期长,不建议)
|
||||
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
|
||||
python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
|
||||
|
||||
#多卡训练,通过--gpus参数指定卡号
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
|
||||
```
|
||||
|
||||
|
||||
PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_train.yml` 中修改 `eval_batch_step` 设置评估频率,默认每500个iter评估一次。评估过程中默认将最佳acc模型,保存为 `output/rec_CRNN/best_accuracy` 。
|
||||
PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` 中修改 `eval_batch_step` 设置评估频率,默认每500个iter评估一次。评估过程中默认将最佳acc模型,保存为 `output/en_PP-OCRv3_rec/best_accuracy` 。
|
||||
|
||||
如果验证集很大,测试将会比较耗时,建议减少评估次数,或训练完再进行评估。
|
||||
|
||||
**提示:** 可通过 -c 参数选择 `configs/rec/` 路径下的多种模型配置进行训练,PaddleOCR支持的识别算法有:
|
||||
**提示:** 可通过 -c 参数选择 `configs/rec/` 路径下的多种模型配置进行训练,PaddleOCR支持的识别算法可以参考[前沿算法列表](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/algorithm_overview.md#12-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95):
|
||||
|
||||
|
||||
| 配置文件 | 算法名称 | backbone | trans | seq | pred |
|
||||
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
|
||||
| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
|
||||
| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
|
||||
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
|
||||
| rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
|
||||
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
|
||||
| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
|
||||
| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
|
||||
| rec_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
|
||||
| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
|
||||
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
|
||||
| rec_mtb_nrtr.yml | NRTR | nrtr_mtb | None | transformer encoder | transformer decoder |
|
||||
| rec_r31_sar.yml | SAR | ResNet31 | None | LSTM encoder | LSTM decoder |
|
||||
| rec_resnet_stn_bilstm_att.yml | SEED | Aster_Resnet | STN | BiLSTM | att |
|
||||
训练中文数据,推荐使用[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml),如您希望尝试其他算法在中文数据集上的效果,请参考下列说明修改配置文件:
|
||||
|
||||
*其中SEED模型需要额外加载FastText训练好的[语言模型](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) ,并且安装 fasttext 依赖:
|
||||
```
|
||||
python3.7 -m pip install fasttext==0.9.1
|
||||
```
|
||||
|
||||
训练中文数据,推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml),如您希望尝试其他算法在中文数据集上的效果,请参考下列说明修改配置文件:
|
||||
|
||||
以 `rec_chinese_lite_train_v2.0.yml` 为例:
|
||||
以 `ch_PP-OCRv3_rec_distillation.yml` 为例:
|
||||
```
|
||||
Global:
|
||||
...
|
||||
|
@ -290,7 +268,7 @@ Train:
|
|||
...
|
||||
- RecResizeImg:
|
||||
# 修改 image_shape 以适应长文本
|
||||
image_shape: [3, 32, 320]
|
||||
image_shape: [3, 48, 320]
|
||||
...
|
||||
loader:
|
||||
...
|
||||
|
@ -310,7 +288,7 @@ Eval:
|
|||
...
|
||||
- RecResizeImg:
|
||||
# 修改 image_shape 以适应长文本
|
||||
image_shape: [3, 32, 320]
|
||||
image_shape: [3, 48, 320]
|
||||
...
|
||||
loader:
|
||||
# 单卡验证的batch_size
|
||||
|
@ -325,7 +303,7 @@ Eval:
|
|||
|
||||
如果训练程序中断,如果希望加载训练中断的模型从而恢复训练,可以通过指定Global.checkpoints指定要加载的模型路径:
|
||||
```shell
|
||||
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints=./your/trained/model
|
||||
python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.checkpoints=./your/trained/model
|
||||
```
|
||||
|
||||
**注意**:`Global.checkpoints`的优先级高于`Global.pretrained_model`的优先级,即同时指定两个参数时,优先加载`Global.checkpoints`指定的模型,如果`Global.checkpoints`指定的模型路径有误,会加载`Global.pretrained_model`指定的模型。
|
||||
|
@ -383,8 +361,8 @@ args1: args1
|
|||
如果您想进一步加快训练速度,可以使用[自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html), 以单机单卡为例,命令如下:
|
||||
|
||||
```shell
|
||||
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml \
|
||||
-o Global.pretrained_model=./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train \
|
||||
python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml \
|
||||
-o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy \
|
||||
Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
|
||||
```
|
||||
|
||||
|
@ -394,8 +372,8 @@ python3 tools/train.py -c configs/rec/rec_icdar15_train.yml \
|
|||
多机多卡训练时,通过 `--ips` 参数设置使用的机器IP地址,通过 `--gpus` 参数设置使用的GPU ID:
|
||||
|
||||
```bash
|
||||
python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml \
|
||||
-o Global.pretrained_model=./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train
|
||||
python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml \
|
||||
-o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
|
||||
```
|
||||
|
||||
**注意:** 采用多机多卡训练时,需要替换上面命令中的ips值为您机器的地址,机器之间需要能够相互ping通。另外,训练时需要在多个机器上分别启动命令。查看机器ip地址的命令为`ifconfig`。
|
||||
|
@ -485,11 +463,12 @@ DCU设备上运行需要设置环境变量 `export HIP_VISIBLE_DEVICES=0,1,2,3`
|
|||
<a name="31-----"></a>
|
||||
## 3.1 指标评估
|
||||
|
||||
训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时,需要设置`Global.checkpoints`指向保存的参数文件。评估数据集可以通过 `configs/rec/rec_icdar15_train.yml` 修改Eval中的 `label_file_path` 设置。
|
||||
训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时,需要设置`Global.checkpoints`指向保存的参数文件。评估数据集可以通过 `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` 修改Eval中的 `label_file_path` 设置。
|
||||
|
||||
|
||||
```
|
||||
# GPU 评估, Global.checkpoints 为待测权重
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.checkpoints={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
<a name="32-------"></a>
|
||||
|
@ -519,7 +498,7 @@ output/rec/
|
|||
|
||||
```
|
||||
# 预测英文结果
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
python3 tools/infer_rec.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
预测图片:
|
||||
|
@ -538,7 +517,7 @@ infer_img: doc/imgs_words/en/word_1.png
|
|||
|
||||
```
|
||||
# 预测中文结果
|
||||
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
|
||||
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
|
||||
```
|
||||
|
||||
预测图片:
|
||||
|
@ -569,15 +548,15 @@ inference 模型(`paddle.jit.save`保存的模型)
|
|||
# Global.pretrained_model 参数设置待转换的训练模型地址,不用添加文件后缀 .pdmodel,.pdopt或.pdparams。
|
||||
# Global.save_inference_dir参数设置转换的模型将保存的地址。
|
||||
|
||||
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
|
||||
python3 tools/export_model.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy Global.save_inference_dir=./inference/en_PP-OCRv3_rec/
|
||||
```
|
||||
|
||||
**注意:**如果您是在自己的数据集上训练的模型,并且调整了中文字符的字典文件,请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
|
||||
**注意:**如果您是在自己的数据集上训练的模型,并且调整了中文字符的字典文件,请注意修改配置文件中的`character_dict_path`为自定义字典文件。
|
||||
|
||||
转换成功后,在目录下有三个文件:
|
||||
|
||||
```
|
||||
/inference/rec_crnn/
|
||||
inference/en_PP-OCRv3_rec/
|
||||
├── inference.pdiparams # 识别inference模型的参数文件
|
||||
├── inference.pdiparams.info # 识别inference模型的参数信息,可忽略
|
||||
└── inference.pdmodel # 识别inference模型的program文件
|
||||
|
@ -588,7 +567,7 @@ python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_trai
|
|||
如果训练时修改了文本的字典,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
|
||||
|
||||
```
|
||||
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_dict_path="your text dict path"
|
||||
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, 48, 320" --rec_char_dict_path="your text dict path"
|
||||
```
|
||||
|
||||
|
||||
|
|
|
@ -0,0 +1,122 @@
|
|||
# STAR-Net
|
||||
|
||||
- [1. Introduction](#1)
|
||||
- [2. Environment](#2)
|
||||
- [3. Model Training / Evaluation / Prediction](#3)
|
||||
- [3.1 Training](#3-1)
|
||||
- [3.2 Evaluation](#3-2)
|
||||
- [3.3 Prediction](#3-3)
|
||||
- [4. Inference and Deployment](#4)
|
||||
- [4.1 Python Inference](#4-1)
|
||||
- [4.2 C++ Inference](#4-2)
|
||||
- [4.3 Serving](#4-3)
|
||||
- [4.4 More](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. Introduction
|
||||
|
||||
Paper:
|
||||
> [STAR-Net: a spatial attention residue network for scene text recognition.](http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf)
|
||||
|
||||
> Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han.
|
||||
|
||||
> BMVC, pages 43.1-43.13, 2016
|
||||
|
||||
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|
||||
|
||||
|Model|Backbone|ACC|config|Download link|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|---|---|---|---|---|
|
||||
|StarNet|Resnet34_vd|84.44%|[configs/rec/rec_r34_vd_tps_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_tps_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
|
||||
|StarNet|MobileNetV3|81.42%|[configs/rec/rec_mv3_tps_bilstm_ctc.yml](../../configs/rec/rec_mv3_tps_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. Environment
|
||||
Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code.
|
||||
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. Model Training / Evaluation / Prediction
|
||||
|
||||
Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
|
||||
|
||||
Training:
|
||||
|
||||
Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
|
||||
|
||||
```
|
||||
#Single GPU training (long training period, not recommended)
|
||||
python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
|
||||
|
||||
#Multi GPU training, specify the gpu number through the --gpus parameter
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_r34_vd_tps_bilstm_ctc.yml
|
||||
```
|
||||
|
||||
Evaluation:
|
||||
|
||||
```
|
||||
# GPU evaluation
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
Prediction:
|
||||
|
||||
```
|
||||
# The configuration file used for prediction must match the training
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. Inference and Deployment
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python Inference
|
||||
First, the model saved during the STAR-Net text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_STAR-Net_train.tar) ), you can use the following command to convert:
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_starnet
|
||||
```
|
||||
|
||||
For STAR-Net text recognition model inference, the following commands can be executed:
|
||||
|
||||
```
|
||||
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
|
||||
```
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++ Inference
|
||||
|
||||
With the inference model prepared, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial for C++ inference.
|
||||
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving
|
||||
|
||||
With the inference model prepared, refer to the [pdserving](../../deploy/pdserving/) tutorial for service deployment by Paddle Serving.
|
||||
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 More
|
||||
|
||||
More deployment schemes supported for STAR-Net:
|
||||
|
||||
- Paddle2ONNX: with the inference model prepared, please refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
|
||||
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@inproceedings{liu2016star,
|
||||
title={STAR-Net: a spatial attention residue network for scene text recognition.},
|
||||
author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
|
||||
booktitle={BMVC},
|
||||
volume={2},
|
||||
pages={7},
|
||||
year={2016}
|
||||
}
|
||||
```
|
|
@ -0,0 +1,123 @@
|
|||
# CRNN
|
||||
|
||||
- [1. Introduction](#1)
|
||||
- [2. Environment](#2)
|
||||
- [3. Model Training / Evaluation / Prediction](#3)
|
||||
- [3.1 Training](#3-1)
|
||||
- [3.2 Evaluation](#3-2)
|
||||
- [3.3 Prediction](#3-3)
|
||||
- [4. Inference and Deployment](#4)
|
||||
- [4.1 Python Inference](#4-1)
|
||||
- [4.2 C++ Inference](#4-2)
|
||||
- [4.3 Serving](#4-3)
|
||||
- [4.4 More](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. Introduction
|
||||
|
||||
Paper:
|
||||
> [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://arxiv.org/abs/1507.05717)
|
||||
|
||||
> Baoguang Shi, Xiang Bai, Cong Yao
|
||||
|
||||
> IEEE, 2015
|
||||
|
||||
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|
||||
|
||||
|Model|Backbone|ACC|config|Download link|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|---|---|---|---|---|
|
||||
|CRNN|Resnet34_vd|81.04%|[configs/rec/rec_r34_vd_none_bilstm_ctc.yml](../../configs/rec/rec_r34_vd_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
|
||||
|CRNN|MobileNetV3|77.95%|[configs/rec/rec_mv3_none_bilstm_ctc.yml](../../configs/rec/rec_mv3_none_bilstm_ctc.yml)|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. Environment
|
||||
Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code.
|
||||
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. Model Training / Evaluation / Prediction
|
||||
|
||||
Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
|
||||
|
||||
Training:
|
||||
|
||||
Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
|
||||
|
||||
```
|
||||
#Single GPU training (long training period, not recommended)
|
||||
python3 tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml
|
||||
|
||||
#Multi GPU training, specify the gpu number through the --gpus parameter
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml
|
||||
```
|
||||
|
||||
Evaluation:
|
||||
|
||||
```
|
||||
# GPU evaluation
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
Prediction:
|
||||
|
||||
```
|
||||
# The configuration file used for prediction must match the training
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. Inference and Deployment
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python Inference
|
||||
First, the model saved during the CRNN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_CRNN_train.tar) ), you can use the following command to convert:
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn
|
||||
```
|
||||
|
||||
For CRNN text recognition model inference, the following commands can be executed:
|
||||
|
||||
```
|
||||
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
|
||||
```
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++ Inference
|
||||
|
||||
With the inference model prepared, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial for C++ inference.
|
||||
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving
|
||||
|
||||
With the inference model prepared, refer to the [pdserving](../../deploy/pdserving/) tutorial for service deployment by Paddle Serving.
|
||||
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 More
|
||||
|
||||
More deployment schemes supported for CRNN:
|
||||
|
||||
- Paddle2ONNX: with the inference model prepared, please refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
|
||||
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@ARTICLE{7801919,
|
||||
author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
|
||||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
|
||||
title={An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition},
|
||||
year={2017},
|
||||
volume={39},
|
||||
number={11},
|
||||
pages={2298-2304},
|
||||
doi={10.1109/TPAMI.2016.2646371}}
|
||||
```
|
|
@ -0,0 +1,111 @@
|
|||
# SEED
|
||||
|
||||
- [1. Introduction](#1)
|
||||
- [2. Environment](#2)
|
||||
- [3. Model Training / Evaluation / Prediction](#3)
|
||||
- [3.1 Training](#3-1)
|
||||
- [3.2 Evaluation](#3-2)
|
||||
- [3.3 Prediction](#3-3)
|
||||
- [4. Inference and Deployment](#4)
|
||||
- [4.1 Python Inference](#4-1)
|
||||
- [4.2 C++ Inference](#4-2)
|
||||
- [4.3 Serving](#4-3)
|
||||
- [4.4 More](#4-4)
|
||||
- [5. FAQ](#5)
|
||||
|
||||
<a name="1"></a>
|
||||
## 1. Introduction
|
||||
|
||||
Paper:
|
||||
> [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://arxiv.org/pdf/2005.10977.pdf)
|
||||
|
||||
> Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping
|
||||
|
||||
> CVPR, 2020
|
||||
|
||||
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|
||||
|
||||
|Model|Backbone|ACC|config|Download link|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|SEED|Aster_Resnet| 85.2% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. Environment
|
||||
Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code.
|
||||
|
||||
|
||||
<a name="3"></a>
|
||||
## 3. Model Training / Evaluation / Prediction
|
||||
|
||||
Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
|
||||
|
||||
Training:
|
||||
|
||||
The SEED model needs to additionally load the [language model](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) trained by FastText, and install the fasttext dependencies:
|
||||
|
||||
```
|
||||
python3 -m pip install fasttext==0.9.1
|
||||
```
|
||||
|
||||
Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
|
||||
|
||||
```
|
||||
#Single GPU training (long training period, not recommended)
|
||||
python3 tools/train.py -c configs/rec/rec_resnet_stn_bilstm_att.yml
|
||||
|
||||
#Multi GPU training, specify the gpu number through the --gpus parameter
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_resnet_stn_bilstm_att.yml
|
||||
```
|
||||
|
||||
Evaluation:
|
||||
|
||||
```
|
||||
# GPU evaluation
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
Prediction:
|
||||
|
||||
```
|
||||
# The configuration file used for prediction must match the training
|
||||
python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
<a name="4"></a>
|
||||
## 4. Inference and Deployment
|
||||
|
||||
<a name="4-1"></a>
|
||||
### 4.1 Python Inference
|
||||
|
||||
Not support
|
||||
|
||||
<a name="4-2"></a>
|
||||
### 4.2 C++ Inference
|
||||
|
||||
Not support
|
||||
|
||||
<a name="4-3"></a>
|
||||
### 4.3 Serving
|
||||
|
||||
Not support
|
||||
|
||||
<a name="4-4"></a>
|
||||
### 4.4 More
|
||||
|
||||
Not support
|
||||
|
||||
<a name="5"></a>
|
||||
## 5. FAQ
|
||||
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@inproceedings{qiao2020seed,
|
||||
title={Seed: Semantics enhanced encoder-decoder framework for scene text recognition},
|
||||
author={Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping},
|
||||
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
|
||||
pages={13528--13537},
|
||||
year={2020}
|
||||
}
|
||||
```
|
|
@ -121,7 +121,7 @@ For more tutorials, including model training, model compression, deployment, etc
|
|||
| Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model |
|
||||
| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
|
||||
| Chinese and English ultra-lightweight PP-OCRv3 model(16.2M) | ch_PP-OCRv3_xx | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
|
||||
| English ultra-lightweight PP-OCRv3 model(13.4M) | en_PP-OCRv3_xx | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
|
||||
| English ultra-lightweight PP-OCRv3 model(13.4M) | en_PP-OCRv3_xx | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
|
||||
| Chinese and English ultra-lightweight PP-OCRv2 model(11.6M) | ch_PP-OCRv2_xx |Mobile & Server|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)|
|
||||
| Chinese and English ultra-lightweight PP-OCR model (9.4M) | ch_ppocr_mobile_v2.0_xx | Mobile & server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) |
|
||||
| Chinese and English general PP-OCR model (143.4M) | ch_ppocr_server_v2.0_xx | Server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) |
|
||||
|
|
|
@ -28,7 +28,34 @@
|
|||
|
||||
To prepare datasets, refer to [ocr_datasets](./dataset/ocr_datasets.md) .
|
||||
|
||||
If you want to reproduce the paper SAR, you need to download extra dataset [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg), extraction code: 627x. Besides, icdar2013, icdar2015, cocotext, IIIT5k datasets are also used to train. For specific details, please refer to the paper SAR.
|
||||
PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:
|
||||
|
||||
```
|
||||
# Training set label
|
||||
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
|
||||
# Test Set Label
|
||||
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
|
||||
```
|
||||
|
||||
PaddleOCR also provides a data format conversion script, which can convert ICDAR official website label to a data format
|
||||
supported by PaddleOCR. The data conversion tool is in `ppocr/utils/gen_label.py`, here is the training set as an example:
|
||||
|
||||
```
|
||||
# convert the official gt to rec_gt_label.txt
|
||||
python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
|
||||
```
|
||||
|
||||
The data format is as follows, (a) is the original picture, (b) is the Ground Truth text file corresponding to each picture:
|
||||
|
||||

|
||||
|
||||
|
||||
- Multilingual dataset
|
||||
|
||||
The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
|
||||
* [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) ,Extraction code:frgi.
|
||||
* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
|
||||
|
||||
|
||||
<a name="Dictionary"></a>
|
||||
### 1.2 Dictionary
|
||||
|
@ -101,11 +128,11 @@ First download the pretrain model, you can download the trained model to finetun
|
|||
|
||||
```
|
||||
cd PaddleOCR/
|
||||
# Download the pre-trained model of MobileNetV3
|
||||
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
|
||||
# Download the pre-trained model of en_PP-OCRv3
|
||||
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar
|
||||
# Decompress model parameters
|
||||
cd pretrain_models
|
||||
tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
|
||||
tar -xf en_PP-OCRv3_rec_train.tar && rm -rf en_PP-OCRv3_rec_train.tar
|
||||
```
|
||||
|
||||
Start training:
|
||||
|
@ -115,9 +142,10 @@ Start training:
|
|||
# Training icdar15 English data and The training log will be automatically saved as train.log under "{save_model_dir}"
|
||||
|
||||
#specify the single card training(Long training time, not recommended)
|
||||
python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
|
||||
python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy
|
||||
|
||||
#specify the card number through --gpus
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy
|
||||
```
|
||||
|
||||
|
||||
|
@ -125,31 +153,13 @@ PaddleOCR supports alternating training and evaluation. You can modify `eval_bat
|
|||
|
||||
If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
|
||||
|
||||
* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported by PaddleOCR are:
|
||||
|
||||
|
||||
| Configuration file | Algorithm | backbone | trans | seq | pred |
|
||||
| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: |
|
||||
| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
|
||||
| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc |
|
||||
| rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc |
|
||||
| rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc |
|
||||
| rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
|
||||
| rec_mv3_none_bilstm_ctc.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc |
|
||||
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
|
||||
| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
|
||||
| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
|
||||
| rec_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
|
||||
| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
|
||||
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
|
||||
| rec_mtb_nrtr.yml | NRTR | nrtr_mtb | None | transformer encoder | transformer decoder |
|
||||
| rec_r31_sar.yml | SAR | ResNet31 | None | LSTM encoder | LSTM decoder |
|
||||
* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported at [rec_algorithm](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/algorithm_overview.md):
|
||||
|
||||
|
||||
For training Chinese data, it is recommended to use
|
||||
[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
|
||||
co
|
||||
Take `rec_chinese_lite_train_v2.0.yml` as an example:
|
||||
[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
|
||||
|
||||
Take `ch_PP-OCRv3_rec_distillation.yml` as an example:
|
||||
```
|
||||
Global:
|
||||
...
|
||||
|
@ -183,7 +193,7 @@ Train:
|
|||
...
|
||||
- RecResizeImg:
|
||||
# Modify image_shape to fit long text
|
||||
image_shape: [3, 32, 320]
|
||||
image_shape: [3, 48, 320]
|
||||
...
|
||||
loader:
|
||||
...
|
||||
|
@ -203,7 +213,7 @@ Eval:
|
|||
...
|
||||
- RecResizeImg:
|
||||
# Modify image_shape to fit long text
|
||||
image_shape: [3, 32, 320]
|
||||
image_shape: [3, 48, 320]
|
||||
...
|
||||
loader:
|
||||
# Eval batch_size for Single card
|
||||
|
@ -380,11 +390,12 @@ Running on a DCU device requires setting the environment variable `export HIP_VI
|
|||
<a name="31-evaluation"></a>
|
||||
### 3.1 Evaluation
|
||||
|
||||
The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
|
||||
The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` file.
|
||||
|
||||
|
||||
```
|
||||
# GPU evaluation, Global.checkpoints is the weight to be tested
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
|
||||
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.checkpoints={path/to/weights}/best_accuracy
|
||||
```
|
||||
|
||||
<a name="32-test"></a>
|
||||
|
@ -417,7 +428,7 @@ Among them, best_accuracy.* is the best model on the evaluation set; iter_epoch_
|
|||
|
||||
```
|
||||
# Predict English results
|
||||
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.jpg
|
||||
python3 tools/infer_rec.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
|
||||
```
|
||||
|
||||
|
||||
|
@ -436,7 +447,7 @@ The configuration file used for prediction must be consistent with the training.
|
|||
|
||||
```
|
||||
# Predict Chinese results
|
||||
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
|
||||
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
|
||||
```
|
||||
|
||||
Input image:
|
||||
|
@ -467,7 +478,7 @@ The recognition model is converted to the inference model in the same way as the
|
|||
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
|
||||
# Global.save_inference_dir Set the address where the converted model will be saved.
|
||||
|
||||
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
|
||||
python3 tools/export_model.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy Global.save_inference_dir=./inference/en_PP-OCRv3_rec/
|
||||
```
|
||||
|
||||
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
|
||||
|
@ -475,7 +486,8 @@ If you have a model trained on your own dataset with a different dictionary file
|
|||
After the conversion is successful, there are three files in the model save directory:
|
||||
|
||||
```
|
||||
inference/rec_crnn/
|
||||
|
||||
inference/en_PP-OCRv3_rec/
|
||||
├── inference.pdiparams # The parameter file of recognition inference model
|
||||
├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
|
||||
└── inference.pdmodel # The program file of recognition model
|
||||
|
|
|
@ -43,12 +43,15 @@ class Momentum(object):
|
|||
self.grad_clip = grad_clip
|
||||
|
||||
def __call__(self, model):
|
||||
train_params = [
|
||||
param for param in model.parameters() if param.trainable is True
|
||||
]
|
||||
opt = optim.Momentum(
|
||||
learning_rate=self.learning_rate,
|
||||
momentum=self.momentum,
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
parameters=model.parameters())
|
||||
parameters=train_params)
|
||||
return opt
|
||||
|
||||
|
||||
|
@ -76,6 +79,9 @@ class Adam(object):
|
|||
self.lazy_mode = lazy_mode
|
||||
|
||||
def __call__(self, model):
|
||||
train_params = [
|
||||
param for param in model.parameters() if param.trainable is True
|
||||
]
|
||||
opt = optim.Adam(
|
||||
learning_rate=self.learning_rate,
|
||||
beta1=self.beta1,
|
||||
|
@ -85,7 +91,7 @@ class Adam(object):
|
|||
grad_clip=self.grad_clip,
|
||||
name=self.name,
|
||||
lazy_mode=self.lazy_mode,
|
||||
parameters=model.parameters())
|
||||
parameters=train_params)
|
||||
return opt
|
||||
|
||||
|
||||
|
@ -118,6 +124,9 @@ class RMSProp(object):
|
|||
self.grad_clip = grad_clip
|
||||
|
||||
def __call__(self, model):
|
||||
train_params = [
|
||||
param for param in model.parameters() if param.trainable is True
|
||||
]
|
||||
opt = optim.RMSProp(
|
||||
learning_rate=self.learning_rate,
|
||||
momentum=self.momentum,
|
||||
|
@ -125,7 +134,7 @@ class RMSProp(object):
|
|||
epsilon=self.epsilon,
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
parameters=model.parameters())
|
||||
parameters=train_params)
|
||||
return opt
|
||||
|
||||
|
||||
|
@ -149,6 +158,9 @@ class Adadelta(object):
|
|||
self.name = name
|
||||
|
||||
def __call__(self, model):
|
||||
train_params = [
|
||||
param for param in model.parameters() if param.trainable is True
|
||||
]
|
||||
opt = optim.Adadelta(
|
||||
learning_rate=self.learning_rate,
|
||||
epsilon=self.epsilon,
|
||||
|
@ -156,7 +168,7 @@ class Adadelta(object):
|
|||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
name=self.name,
|
||||
parameters=model.parameters())
|
||||
parameters=train_params)
|
||||
return opt
|
||||
|
||||
|
||||
|
@ -190,17 +202,20 @@ class AdamW(object):
|
|||
self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay
|
||||
|
||||
def __call__(self, model):
|
||||
parameters = model.parameters()
|
||||
parameters = [
|
||||
param for param in model.parameters() if param.trainable is True
|
||||
]
|
||||
|
||||
self.no_weight_decay_param_name_list = [
|
||||
p.name for n, p in model.named_parameters() if any(nd in n for nd in self.no_weight_decay_name_list)
|
||||
p.name for n, p in model.named_parameters()
|
||||
if any(nd in n for nd in self.no_weight_decay_name_list)
|
||||
]
|
||||
|
||||
if self.one_dim_param_no_weight_decay:
|
||||
self.no_weight_decay_param_name_list += [
|
||||
p.name for n, p in model.named_parameters() if len(p.shape) == 1
|
||||
p.name for n, p in model.named_parameters() if len(p.shape) == 1
|
||||
]
|
||||
|
||||
|
||||
opt = optim.AdamW(
|
||||
learning_rate=self.learning_rate,
|
||||
beta1=self.beta1,
|
||||
|
@ -216,4 +231,4 @@ class AdamW(object):
|
|||
return opt
|
||||
|
||||
def _apply_decay_param_fun(self, name):
|
||||
return name not in self.no_weight_decay_param_name_list
|
||||
return name not in self.no_weight_decay_param_name_list
|
||||
|
|
|
@ -60,7 +60,7 @@ def get_image_file_list(img_file):
|
|||
raise Exception("not found any img file in {}".format(img_file))
|
||||
|
||||
img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
|
||||
if os.path.isfile(img_file) and _check_image_file(file_path):
|
||||
if os.path.isfile(img_file) and _check_image_file(img_file):
|
||||
imgs_lists.append(img_file)
|
||||
elif os.path.isdir(img_file):
|
||||
for single_file in os.listdir(img_file):
|
||||
|
|
|
@ -20,8 +20,8 @@ import os
|
|||
import sys
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(__dir__)
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
|
||||
sys.path.insert(0, __dir__)
|
||||
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
|
||||
|
||||
from ppocr.data import build_dataloader
|
||||
from ppocr.modeling.architectures import build_model
|
||||
|
|
|
@ -195,7 +195,7 @@ def main(args):
|
|||
text_sys.text_detector.autolog.report()
|
||||
text_sys.text_recognizer.autolog.report()
|
||||
|
||||
with open(os.path.join(draw_img_save_dir, "system_results.txt"), 'w') as f:
|
||||
with open(os.path.join(draw_img_save_dir, "system_results.txt"), 'w', encoding='utf-8') as f:
|
||||
f.writelines(save_results)
|
||||
|
||||
|
||||
|
|
Loading…
Reference in New Issue