detection_en.md 6.3 KB
Newer Older
X
xxxpsyduck 已提交
1
# TEXT DETECTION
K
Khanh Tran 已提交
2 3 4

This section uses the icdar15 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.

X
xxxpsyduck 已提交
5
## DATA PREPARATION
K
Khanh Tran 已提交
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
The icdar2015 dataset can be obtained from [official website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Registration is required for downloading.

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.txt
```

After 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
```

25
The provided annotation file format is as follow, seperated by "\t":
K
Khanh Tran 已提交
26 27 28 29
```
" 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]], ...}]
```
30
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.
K
Khanh Tran 已提交
31 32 33 34 35

`transcription` represents the text of the current text box, and this information is not needed in the text detection task.
If you want to train PaddleOCR on other datasets, you can build the annotation file according to the above format.


X
xxxpsyduck 已提交
36
## TRAINING
K
Khanh Tran 已提交
37 38 39 40 41 42 43 44

First download the pretrained model. The detection model of PaddleOCR currently supports two backbones, namely MobileNetV3 and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs.
```
cd PaddleOCR/
# Download the pre-trained model of MobileNetV3
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
# Download the pre-trained model of ResNet50
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
45 46 47 48 49 50 51 52 53 54 55 56

# decompressing the pre-training model file, take MobileNetV3 as an example
tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/

# Note: After decompressing the backbone pre-training weight file correctly, the file list in the folder is as follows:
./pretrain_models/MobileNetV3_large_x0_5_pretrained/
  └─ conv_last_bn_mean
  └─ conv_last_bn_offset
  └─ conv_last_bn_scale
  └─ conv_last_bn_variance
  └─ ......

K
Khanh Tran 已提交
57 58
```

59
**START TRAINING**  
M
MissPenguin 已提交
60
*If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
K
Khanh Tran 已提交
61 62 63 64
```
python3 tools/train.py -c configs/det/det_mv3_db.yml
```

M
MissPenguin 已提交
65 66
In 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](./config_en.md).
K
Khanh Tran 已提交
67

68
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
K
Khanh Tran 已提交
69 70 71 72
```
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
```

L
LDOUBLEV 已提交
73
**load trained model and conntinue training**
74
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.
L
LDOUBLEV 已提交
75 76 77 78 79 80

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

81
**Note**:The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, 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.pretrain_weights` will be loaded.
L
LDOUBLEV 已提交
82 83


X
xxxpsyduck 已提交
84
## EVALUATION
K
Khanh Tran 已提交
85 86 87 88 89

PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean.

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`

90
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.
K
Khanh Tran 已提交
91 92 93 94

```
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
```
95
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.
K
Khanh Tran 已提交
96 97

Such as:
98
```shell
K
Khanh Tran 已提交
99 100 101
python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```

102
* Note: `box_thresh` and `unclip_ratio` are parameters required for DB post-processing, and not need to be set when evaluating the EAST model.
K
Khanh Tran 已提交
103

104
## TEST
K
Khanh Tran 已提交
105 106

Test the detection result on a single image:
107
```shell
K
Khanh Tran 已提交
108 109 110 111
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
```

When testing the DB model, adjust the post-processing threshold:
112
```shell
K
Khanh Tran 已提交
113 114 115 116 117
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
```


Test the detection result on all images in the folder:
118
```shell
K
Khanh Tran 已提交
119 120
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
```