用下载的模型测试 totaltext指标不对额，测试并没有达到实验公布的指标，直接按步骤跑的，

[cqray1990]

请提供下述完整信息以便快速定位问题/Please provide the following information to quickly locate the problem

• 系统环境/System Environment： ubantu 18
• 版本号/Version：Paddle： PaddleOCR： 问题相关组件/Related components：
• 运行指令/Command Code： python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints="./pretrain_models/en_server_pgnetA/best_accuracy"
• 完整报错/Complete Error Message：

[2022/10/23 12:18:34] ppocr INFO: total_num_gt:2543 [2022/10/23 12:18:34] ppocr INFO: total_num_det:2273 [2022/10/23 12:18:34] ppocr INFO: global_accumulative_recall:1884.7999999999959 [2022/10/23 12:18:34] ppocr INFO: hit_str_count:1294 [2022/10/23 12:18:34] ppocr INFO: recall:0.7411718442784097 [2022/10/23 12:18:34] ppocr INFO: precision:0.8337879454465452 [2022/10/23 12:18:34] ppocr INFO: f_score:0.7847567325787329 [2022/10/23 12:18:34] ppocr INFO: seqerr:0.3134550084889628 [2022/10/23 12:18:34] ppocr INFO: recall_e2e:0.5088478175383405 [2022/10/23 12:18:34] ppocr INFO: precision_e2e:0.5692916849978003 [2022/10/23 12:18:34] ppocr INFO: f_score_e2e:0.537375415282392 [2022/10/23 12:18:34] ppocr INFO: fps:27.01980638883

i download the test model from datalink you offer

yours：

Ours	87.03	82.48	84.69	61.71	58.43	60.03	48.73 (size=768)	download link

Ours 87.03 82.48 84.69 61.71 58.43 60.03 48.73 (size=768) download link

[cqray1990]

@huangjun12 @sdcb @littletomatodonkey

[LDOUBLEV]

readme中公开的论文指标需要用B模式的metric计算方式，A模式的计算方式的标签格式和PPOCR格式相同，但是效果差一些

采用B模式精度评估方式：

下载ground truth ：
wget https://paddleocr.bj.bcebos.com/dataset/Groundtruth.tar
修改metric部分参数，
Metric:
  name: E2EMetric
  mode: B   # two ways for eval, A: label from txt,  B: label from gt_mat
  gt_mat_dir:  ./train_data/Groundtruth/  # the dir of gt_mat
  character_dict_path: ppocr/utils/ic15_dict.txt
  main_indicator: f_score_e2e

最后评估出来的指标：

[2022/09/15 02:35:36] ppocr INFO: load pretrain successful from ./en_server_pgnetA/best_accuracy
eval model:: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [01:15<00:00,  3.97it/s]
[2022/09/15 02:36:52] ppocr INFO: metric eval ***************
[2022/09/15 02:36:52] ppocr INFO: total_num_gt:2204
[2022/09/15 02:36:52] ppocr INFO: total_num_det:2070
[2022/09/15 02:36:52] ppocr INFO: global_accumulative_recall:1818.3999999999967
[2022/09/15 02:36:52] ppocr INFO: hit_str_count:1267
[2022/09/15 02:36:52] ppocr INFO: recall:0.8250453720508152
[2022/09/15 02:36:52] ppocr INFO: precision:0.8749758454106266
[2022/09/15 02:36:52] ppocr INFO: f_score:0.8492773672439888
[2022/09/15 02:36:52] ppocr INFO: seqerr:0.30323361196656273
[2022/09/15 02:36:52] ppocr INFO: recall_e2e:0.5748638838475499
[2022/09/15 02:36:52] ppocr INFO: precision_e2e:0.6120772946859904
[2022/09/15 02:36:52] ppocr INFO: f_score_e2e:0.5928872250818905
[2022/09/15 02:36:52] ppocr INFO: fps:20.85154714822483