This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.
The icdar2015 dataset contains train set which has 1000 images obtained with wearable cameras and test set which has 500 images obtained with wearable cameras. The icdar2015 can be obtained from official website. Registration is required for downloading.
After registering and logging in, download the part marked in the red box in the figure below. And, the content downloaded by Training Set Images should be saved as the folder icdar_c4_train_imgs, and the content downloaded by Test Set Images is saved as the folder ch4_test_images
Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. In addition, PaddleOCR organizes many scattered annotation files into two separate annotation files for train and test respectively, which can be downloaded by wget:
# Under the PaddleOCR path
cd PaddleOCR/
wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/train_icdar2015_label.txt
wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_label.txtAfter decompressing the data set and downloading the annotation file, PaddleOCR/train_data/ has two folders and two files, which are:
/PaddleOCR/train_data/icdar2015/text_localization/
└─ icdar_c4_train_imgs/ Training data of icdar dataset
└─ ch4_test_images/ Testing data of icdar dataset
└─ train_icdar2015_label.txt Training annotation of icdar dataset
└─ test_icdar2015_label.txt Test annotation of icdar dataset
The provided annotation file format is as follow, seperated by "\t":
" Image file name Image annotation information encoded by json.dumps"
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
The image annotation after json.dumps() encoding is a list containing multiple dictionaries.
The points in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
transcription represents the text of the current text box. When its content is "###" it means that the text box is invalid and will be skipped during training.
If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format.
First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in PaddleClas to replace backbone according to your needs. And the responding download link of backbone pretrain weights can be found in (https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/README_cn.md#resnet%E5%8F%8A%E5%85%B6vd%E7%B3%BB%E5%88%97).
cd PaddleOCR/
# Download the pre-trained model of MobileNetV3
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
# or, download the pre-trained model of ResNet18_vd
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
# or, download the pre-trained model of ResNet50_vd
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
If CPU version installed, please set the parameter use_gpu to false in the configuration.
python3 tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrainedIn the above instruction, use -c to select the training to use the configs/det/det_db_mv3.yml configuration file.
For a detailed explanation of the configuration file, please refer to config.
You can also use -o to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001
# single GPU training
python3 tools/train.py -c configs/det/det_mv3_db.yml -o \
Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained \
Optimizer.base_lr=0.0001
# multi-GPU training
# Set the GPU ID used by the '--gpus' parameter.
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
If you expect to load trained model and continue the training again, you can specify the parameter Global.checkpoints as the model path to be loaded.
For example:
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/modelNote: The priority of Global.checkpoints is higher than that of Global.pretrained_model, that is, when two parameters are specified at the same time, the model specified by Global.checkpoints will be loaded first. If the model path specified by Global.checkpoints is wrong, the one specified by Global.pretrained_model will be loaded.
The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under ppocr/modeling. The data entering the network will pass through these four parts in sequence(transforms->backbones-> necks->heads).
├── architectures # Code for building network
├── transforms # Image Transformation Module
├── backbones # Feature extraction module
├── necks # Feature enhancement module
└── heads # Output moduleIf the Backbone to be replaced has a corresponding implementation in PaddleOCR, you can directly modify the parameters in the Backbone part of the configuration yml file.
However, if you want to use a new Backbone, an example of replacing the backbones is as follows:
- Create a new file under the ppocr/modeling/backbones folder, such as my_backbone.py.
- Add code in the my_backbone.py file, the sample code is as follows:
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class MyBackbone(nn.Layer):
def __init__(self, *args, **kwargs):
super(MyBackbone, self).__init__()
# your init code
self.conv = nn.xxxx
def forward(self, inputs):
# your network forward
y = self.conv(inputs)
return y- Import the added module in the ppocr/modeling/backbones/_init_.py file.
After adding the four-part modules of the network, you only need to configure them in the configuration file to use, such as:
Backbone:
name: MyBackbone
args1: args1NOTE: More details about replace Backbone and other mudule can be found in doc.
PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean(F-Score).
Run the following code to calculate the evaluation indicators. The result will be saved in the test result file specified by save_res_path in the configuration file det_db_mv3.yml
When evaluating, set post-processing parameters box_thresh=0.6, unclip_ratio=1.5. If you use different datasets, different models for training, these two parameters should be adjusted for better result.
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.
python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5- Note:
box_threshandunclip_ratioare parameters required for DB post-processing, and not need to be set when evaluating the EAST and SAST model.
Test the detection result on a single image:
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy"When testing the DB model, adjust the post-processing threshold:
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=2.0Test the detection result on all images in the folder:
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy"The inference model (the model saved by paddle.jit.save) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.
Compared with the checkpoints model, the inference model will additionally save the structural information of the model. Therefore, it is easier to deploy because the model structure and model parameters are already solidified in the inference model file, and is suitable for integration with actual systems.
Firstly, we can convert DB trained model to inference model:
python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model="./output/det_db/best_accuracy" Global.save_inference_dir="./output/det_db_inference/"The detection inference model prediction：
python3 tools/infer/predict_det.py --det_algorithm="DB" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=TrueIf it is other detection algorithms, such as the EAST, the det_algorithm parameter needs to be modified to EAST, and the default is the DB algorithm:
python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=TrueQ1: The prediction results of trained model and inference model are inconsistent?
A: Most of the problems are caused by the inconsistency of the pre-processing and post-processing parameters during the prediction of the trained model and the pre-processing and post-processing parameters during the prediction of the inference model. Taking the model trained by the det_mv3_db.yml configuration file as an example, the solution to the problem of inconsistent prediction results between the training model and the inference model is as follows:
- Check whether the trained model preprocessing is consistent with the prediction preprocessing function of the inference model. When the algorithm is evaluated, the input image size will affect the accuracy. In order to be consistent with the paper, the image is resized to [736, 1280] in the training icdar15 configuration file, but there is only a set of default parameters when the inference model predicts, which will be considered To predict the speed problem, the longest side of the image is limited to 960 for resize by default. The preprocessing function of the training model preprocessing and the inference model is located in ppocr/data/imaug/operators.py
- Check whether the post-processing of the trained model is consistent with the post-processing parameters of the inference.