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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](../../../datasets/ocr_datasets.en.md).

### 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](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.0/ppcls/modeling/architectures) 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>).

```bash linenums="1"
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.*

```bash linenums="1"
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](../blog/config.en.md).

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.

```bash linenums="1"
# 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](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_en.html). for single card training, the command is as follows:

```bash linenums="1"
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:

```bash linenums="1"
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](../../../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones->
necks->heads).

```bash linenums="1"
├── 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](../../../../ppocr/modeling/backbones) folder, such as my_backbone.py.
2. Add code in the my_backbone.py file, the sample code is as follows:

```python linenums="1"
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
```

3. Import the added module in the [ppocr/modeling/backbones/\_*init\_*.py](../../../../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:

```yaml linenums="1"
  Backbone:
    name: MyBackbone
    args1: args1
```

**NOTE**: More details about replace Backbone and other module can be found in [doc](../../algorithm/add_new_algorithm.en.md).

### 2.4 Mixed Precision Training

If you want to speed up your training further, you can use [Auto Mixed Precision Training](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html), taking a single machine and a single gpu as an example, the commands are as follows:

```bash linenums="1"
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:

```bash linenums="1"
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](../blog/distributed_training.en.md).

### 2.6 Training with knowledge distillation

Knowledge distillation is supported in PaddleOCR for text detection training process. For more details, please refer to [doc](./../model_compress/knowledge_distillation.en.md).

### 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](./finetune.en.md).

## 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.

```bash linenums="1"
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:

```bash linenums="1"
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:

```bash linenums="1"
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:

```bash linenums="1"
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:

```bash linenums="1"
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：

```bash linenums="1"
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:

```bash linenums="1"
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:

- Check whether the [trained model preprocessing](../../../../configs/det/det_mv3_db.yml#L116) is consistent with the prediction [preprocessing function of the inference model](../../../../tools/infer/predict_det.py#L42). 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](../../../../ppocr/data/imaug/operators.py#L147).
- Check whether the [post-processing of the trained model](../../../../configs/det/det_mv3_db.yml#L51) is consistent with the [post-processing parameters of the inference](../../../../tools/infer/utility.py#L50).
