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Text Detection

This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.

1. Data and Weights Preparation

1.1 Data Preparation

To prepare datasets, refer to ocr_datasets .

1.2 Download Pre-trained Model

First download the pre-trained 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 pre-trained 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://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams
# or, download the pre-trained model of ResNet18_vd
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams
# or, download the pre-trained model of ResNet50_vd
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams

2. Training

2.1 Start Training

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_pretrained

In the above instruction, use -c to select the training to use the configs/det/det_mv3_db.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

# multi-Node, multi-GPU training
# Set the IPs of your nodes used by the '--ips' parameter. Set the GPU ID used by the '--gpus' parameter.
python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --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

Note: For multi-Node multi-GPU training, you need to replace the ips value in the preceding command with the address of your machine, and the machines must be able to ping each other. In addition, it requires activating commands separately on multiple machines when we start the training. The command for viewing the IP address of the machine is ifconfig.

If you want to further speed up the training, you can use automatic mixed precision training. for single card training, the command is as follows:

python3 tools/train.py -c configs/det/det_mv3_db.yml \
     -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained \
     Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True

2.2 Load Trained Model and Continue Training

If you expect to load trained model and continue the training again, you can specify the parameter Global.checkpoints as the model path to be loaded.

For example:

python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model

Note: 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.

2.3 Training with New Backbone

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 module

If 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:

  1. Create a new file under the ppocr/modeling/backbones folder, such as my_backbone.py.
  2. 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
  1. 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: args1

NOTE: More details about replace Backbone and other mudule can be found in doc.

2.4 Mixed Precision Training

If you want to speed up your training further, you can use Auto Mixed Precision Training, taking a single machine and a single gpu as an example, the commands are as follows:

python3 tools/train.py -c configs/det/det_mv3_db.yml \
     -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained \
     Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True

2.5 Distributed Training

During multi-machine multi-gpu training, use the --ips parameter to set the used machine IP address, and the --gpus parameter to set the used GPU ID:

python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --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

Note: (1) When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. (2) Training needs to be launched separately on multiple machines. The command to view the ip address of the machine is ifconfig. (3) For more details about the distributed training speedup ratio, please refer to Distributed Training Tutorial.

2.6 Training with knowledge distillation

Knowledge distillation is supported in PaddleOCR for text detection training process. For more details, please refer to doc.

2.7 Training on other platform(Windows/macOS/Linux DCU)

  • Windows GPU/CPU The Windows platform is slightly different from the Linux platform: Windows platform only supports single gpu training and inference, specify GPU for training set CUDA_VISIBLE_DEVICES=0 On the Windows platform, DataLoader only supports single-process mode, so you need to set num_workers to 0;

  • macOS GPU mode is not supported, you need to set use_gpu to False in the configuration file, and the rest of the training evaluation prediction commands are exactly the same as Linux GPU.

  • Linux DCU Running on a DCU device requires setting the environment variable export HIP_VISIBLE_DEVICES=0,1,2,3, and the rest of the training and evaluation prediction commands are exactly the same as the Linux GPU.

2.8 Fine-tuning

In actual use, it is recommended to load the official pre-trained model and fine-tune it in your own data set. For the fine-tuning method of the detection model, please refer to: Model Fine-tuning Tutorial.

3. Evaluation and Test

3.1 Evaluation

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_thresh and unclip_ratio are parameters required for DB post-processing, and not need to be set when evaluating the EAST and SAST model.

3.2 Test

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=2.0

Test 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"

4. Inference

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=True

If 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=True

5. FAQ

Q1: 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: