未验证 提交 5ed1e242 编写于 作者: M MissPenguin 提交者: GitHub

Merge pull request #555 from MissPenguin/develop

update docs
...@@ -110,6 +110,9 @@ PaddleOCR开源的文本检测算法列表: ...@@ -110,6 +110,9 @@ PaddleOCR开源的文本检测算法列表:
|-|-|-|-|-|-| |-|-|-|-|-|-|
|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)| |SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)|
**说明:** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载:[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集共3w张数据,训练中文检测模型的相关配置和预训练文件如下: 使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集共3w张数据,训练中文检测模型的相关配置和预训练文件如下:
|模型|骨干网络|配置文件|预训练模型| |模型|骨干网络|配置文件|预训练模型|
...@@ -145,7 +148,7 @@ PaddleOCR开源的文本识别算法列表: ...@@ -145,7 +148,7 @@ PaddleOCR开源的文本识别算法列表:
|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)| |RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)| |SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
**说明:** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广,增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载,提取码: y3ry。 **说明:** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载,提取码: y3ry。
原始论文使用两阶段训练平均精度为89.74%,PaddleOCR中使用one-stage训练,平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。 原始论文使用两阶段训练平均精度为89.74%,PaddleOCR中使用one-stage训练,平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据,进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型,相关配置和预训练文件如下: 使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据,进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型,相关配置和预训练文件如下:
......
# 文字检测 # 文字检测
本节以icdar15数据集为例,介绍PaddleOCR中检测模型的训练、评估与测试。 本节以icdar2015数据集为例,介绍PaddleOCR中检测模型的训练、评估与测试。
## 数据准备 ## 数据准备
icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到,首次下载需注册。 icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到,首次下载需注册。
将下载到的数据集解压到工作目录下,假设解压在 PaddleOCR/train_data/ 下。另外,PaddleOCR将零散的标注文件整理成单独的标注文件 将下载到的数据集解压到工作目录下,假设解压在 PaddleOCR/train_data/ 下。另外,PaddleOCR将零散的标注文件整理成单独的标注文件
,您可以通过wget的方式进行下载。 ,您可以通过wget的方式进行下载。
``` ```shell
# 在PaddleOCR路径下 # 在PaddleOCR路径下
cd PaddleOCR/ 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/train_icdar2015_label.txt
...@@ -23,21 +23,21 @@ wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_la ...@@ -23,21 +23,21 @@ wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/test_icdar2015_la
└─ test_icdar2015_label.txt icdar数据集的测试标注 └─ test_icdar2015_label.txt icdar数据集的测试标注
``` ```
提供的标注文件格式为,其中中间是"\t"分隔: 提供的标注文件格式如下,中间用"\t"分隔:
``` ```
" 图像文件名 json.dumps编码的图像标注信息" " 图像文件名 json.dumps编码的图像标注信息"
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}] ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]
``` ```
json.dumps编码前的图像标注信息是包含多个字典的list,字典中的 `points` 表示文本框的四个点的坐标(x, y),从左上角的点开始顺时针排列。 json.dumps编码前的图像标注信息是包含多个字典的list,字典中的 `points` 表示文本框的四个点的坐标(x, y),从左上角的点开始顺时针排列。
`transcription` 表示当前文本框的文字,在文本检测任务中并不需要这个信息。 `transcription` 表示当前文本框的文字,**当其内容为“###”时,表示该文本框无效,在训练时会跳过。**
如果您想在其他数据集上训练PaddleOCR,可以按照上述形式构建标注文件。
如果您想在其他数据集上训练,可以按照上述形式构建标注文件。
## 快速启动训练 ## 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、ResNet50_vd,
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。 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/ cd PaddleOCR/
# 下载MobileNetV3的预训练模型 # 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 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
...@@ -45,7 +45,7 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Mob ...@@ -45,7 +45,7 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Mob
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar 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 # 解压预训练模型文件,以MobileNetV3为例
tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
# 注:正确解压backbone预训练权重文件后,文件夹下包含众多以网络层命名的权重文件,格式如下: # 注:正确解压backbone预训练权重文件后,文件夹下包含众多以网络层命名的权重文件,格式如下:
./pretrain_models/MobileNetV3_large_x0_5_pretrained/ ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
...@@ -57,11 +57,11 @@ tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models ...@@ -57,11 +57,11 @@ tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models
``` ```
**启动训练** #### 启动训练
*如果您安装的是cpu版本,请将配置文件中的 `use_gpu` 字段修改为false* *如果您安装的是cpu版本,请将配置文件中的 `use_gpu` 字段修改为false*
``` ```shell
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
``` ```
...@@ -69,52 +69,52 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights= ...@@ -69,52 +69,52 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=
有关配置文件的详细解释,请参考[链接](./config.md) 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 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.yml -o Optimizer.base_lr=0.0001
``` ```
**断点训练** #### 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: 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.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。 **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。
## 指标评估 ## 指标评估
PaddleOCR计算三个OCR检测相关的指标,分别是:Precision、Recall、Hmean。 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.yml``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 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.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。 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: 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.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 * 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
## 测试检测效果 ## 测试检测效果
测试单张图像的检测效果 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.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, 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.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 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.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
``` ```
# TEXT DETECTION # TEXT DETECTION
This section uses the icdar15 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR. This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.
## DATA PREPARATION ## DATA PREPARATION
The icdar2015 dataset can be obtained from [official website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Registration is required for downloading. 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: 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:
``` ```shell
# Under the PaddleOCR path # Under the PaddleOCR path
cd PaddleOCR/ 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/train_icdar2015_label.txt
...@@ -27,16 +27,19 @@ The provided annotation file format is as follow, seperated by "\t": ...@@ -27,16 +27,19 @@ The provided annotation file format is as follow, seperated by "\t":
" Image file name Image annotation information encoded by json.dumps" " 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]]}, {...}] 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. 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.**
`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, please build the annotation file according to the above format.
If you want to train PaddleOCR on other datasets, you can build the annotation file according to the above format.
## TRAINING ## 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 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.
``` ```shell
cd PaddleOCR/ cd PaddleOCR/
# Download the pre-trained model of MobileNetV3 # 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 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
...@@ -44,7 +47,7 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Mob ...@@ -44,7 +47,7 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Mob
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar 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 # decompressing the pre-training model file, take MobileNetV3 as an example
tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/ 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: # 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/ ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
...@@ -56,9 +59,9 @@ tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models ...@@ -56,9 +59,9 @@ tar xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models
``` ```
**START TRAINING** #### START TRAINING
*If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.* *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 python3 tools/train.py -c configs/det/det_mv3_db.yml
``` ```
...@@ -66,19 +69,19 @@ In the above instruction, use `-c` to select the training to use the `configs/de ...@@ -66,19 +69,19 @@ In the above instruction, use `-c` to select the training to use the `configs/de
For a detailed explanation of the configuration file, please refer to [config](./config_en.md). For a detailed explanation of the configuration file, please refer to [config](./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 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.yml -o Optimizer.base_lr=0.0001
``` ```
**load trained model and conntinue training** #### load trained model and conntinue 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. 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: 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.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. **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.
## EVALUATION ## EVALUATION
...@@ -89,7 +92,7 @@ Run the following code to calculate the evaluation indicators. The result will b ...@@ -89,7 +92,7 @@ Run the following code to calculate the evaluation indicators. The result will b
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. 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.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. 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.
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