Merge pull request #793 from MissPenguin/develop
update detection.md for v1.1
This commit is contained in:
MissPenguin
GitHub
commit
a9e7c8f923
|
|
@ -44,13 +44,15 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
|
|||
|
||||
## 快速启动训练
|
||||
|
||||
首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet50_vd，
|
||||
首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet_vd系列，
|
||||
您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
|
||||
```shell
|
||||
cd PaddleOCR/
|
||||
# 下载MobileNetV3的预训练模型
|
||||
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
|
||||
# 下载ResNet50的预训练模型
|
||||
# 或，下载ResNet18_vd的预训练模型
|
||||
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
|
||||
# 或，下载ResNet50_vd的预训练模型
|
||||
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
|
||||
|
||||
# 解压预训练模型文件，以MobileNetV3为例
|
||||
|
|
@ -72,24 +74,24 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
|
|||
|
||||
```shell
|
||||
# 训练 mv3_db 模型，并将训练日志保存为 tain_det.log
|
||||
python3 tools/train.py -c configs/det/det_mv3_db.yml \
|
||||
python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml \
|
||||
-o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/ \
|
||||
2>&1 | tee train_det.log
|
||||
```
|
||||
|
||||
上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
|
||||
上述指令中，通过-c 选择训练使用configs/det/det_db_mv3_v1.1.yml配置文件。
|
||||
有关配置文件的详细解释，请参考[链接](./config.md)。
|
||||
|
||||
您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
|
||||
```shell
|
||||
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
|
||||
python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Optimizer.base_lr=0.0001
|
||||
```
|
||||
|
||||
#### 断点训练
|
||||
|
||||
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/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
|
||||
python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./your/trained/model
|
||||
```
|
||||
|
||||
**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`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.pretrain_weights`指定的模型。
|
||||
|
|
@ -98,17 +100,17 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
|
|||
|
||||
PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean。
|
||||
|
||||
运行如下代码，根据配置文件`det_db_mv3.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
|
||||
运行如下代码，根据配置文件`det_db_mv3_v1.1.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
|
||||
|
||||
评估时设置后处理参数`box_thresh=0.6`，`unclip_ratio=1.5`，使用不同数据集、不同模型训练，可调整这两个参数进行优化
|
||||
```shell
|
||||
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
|
||||
python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
|
||||
```
|
||||
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。
|
||||
|
||||
比如：
|
||||
```shell
|
||||
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
|
||||
python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
|
||||
```
|
||||
|
||||
* 注：`box_thresh`、`unclip_ratio`是DB后处理所需要的参数，在评估EAST模型时不需要设置
|
||||
|
|
@ -117,16 +119,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="./ou
|
|||
|
||||
测试单张图像的检测效果
|
||||
```shell
|
||||
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"
|
||||
python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
|
||||
```
|
||||
|
||||
测试DB模型时，调整后处理阈值，
|
||||
```shell
|
||||
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
|
||||
python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.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
|
||||
```
|
||||
|
||||
|
||||
测试文件夹下所有图像的检测效果
|
||||
```shell
|
||||
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"
|
||||
python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
|
||||
```
|
||||
|
|
|
|||
|
|
@ -38,12 +38,14 @@ If you want to train PaddleOCR on other datasets, please build the annotation fi
|
|||
|
||||
## TRAINING
|
||||
|
||||
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.
|
||||
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](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs.
|
||||
```shell
|
||||
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
|
||||
# or, download the pre-trained model of ResNet18_vd
|
||||
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
|
||||
# or, download the pre-trained model of ResNet50_vd
|
||||
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
|
||||
|
||||
# decompressing the pre-training model file, take MobileNetV3 as an example
|
||||
|
|
@ -62,7 +64,7 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
|
|||
#### START TRAINING
|
||||
*If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
|
||||
```shell
|
||||
python3 tools/train.py -c configs/det/det_mv3_db.yml 2>&1 | tee train_det.log
|
||||
python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml 2>&1 | tee train_det.log
|
||||
```
|
||||
|
||||
In the above instruction, use `-c` to select the training to use the `configs/det/det_db_mv3.yml` configuration file.
|
||||
|
|
@ -70,7 +72,7 @@ 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
|
||||
```shell
|
||||
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
|
||||
python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Optimizer.base_lr=0.0001
|
||||
```
|
||||
|
||||
#### load trained model and continue training
|
||||
|
|
@ -78,7 +80,7 @@ If you expect to load trained model and continue the training again, you can spe
|
|||
|
||||
For example:
|
||||
```shell
|
||||
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model
|
||||
python3 tools/train.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./your/trained/model
|
||||
```
|
||||
|
||||
**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.
|
||||
|
|
@ -88,18 +90,18 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
|
|||
|
||||
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`
|
||||
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_v1.1.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.
|
||||
|
||||
```shell
|
||||
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
|
||||
python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
|
||||
```
|
||||
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.
|
||||
|
||||
Such as:
|
||||
```shell
|
||||
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
|
||||
python3 tools/eval.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints="./output/det_db/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 model.
|
||||
|
|
@ -108,16 +110,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="./ou
|
|||
|
||||
Test the detection result on a single image:
|
||||
```shell
|
||||
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"
|
||||
python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.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:
|
||||
```shell
|
||||
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
|
||||
python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.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:
|
||||
```shell
|
||||
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"
|
||||
python3 tools/infer_det.py -c configs/det/det_mv3_db_v1.1.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
|
||||
```
|
||||
|
|
|
|||
Loading…
Reference in New Issue