PaddleOCR 小白提问：使用ch_PP-OCRv3_rec_train中英文识别模型，使用个人数据对模型进行训练最后acc为0

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

版本号/Version：Paddle：2.4.0.post117
PaddleOCR：2.4.0.3 问题相关组件/Related components：
运行指令/Command Code：python tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=./ch_PP-OCRv3_rec_train/best_accuracy

个人数据为一张图片里面两行数字，每张图片里面数字的位置都不一样，制作对应txt文件格式为：图片名称图片内容使用推理模型能够识别图片，但是部分会有错误，识别结果为一个列表里面两个元素，这里是不是我的数据标注有问题？

个人数据标注文件内容：

下图为使用官方例子进行识别：

训练时直接下载paddlceocr2.6整个代码，然后下载pp-ocrv3对应识别训练模型，但是我的训练过程不会打印acc、loss等信息，只显示保存模型地址，是我哪里没开启打印控制吗？最后显示训练结果为0.

最后训练结束如图显示：

希望大佬解答，谢谢！

Sep 22 '23 10:09 sssjc666

print_batch_step: 10 训练的配置文件里面不是有这个参数，设置运行多少步打印一次log

Sep 23 '23 03:09 hezhenname

print_batch_step: 10 训练的配置文件里面不是有这个参数，设置运行多少步打印一次log

有这个参数的，我的设置是默认的10，运行完还是不打印

Sep 25 '23 00:09 sssjc666

那就不清楚了，我没遇到过这样的情况，前面acc=0应该是正常的，我当时试过v3，前100轮都是0，之后才慢慢增加

Sep 25 '23 04:09 hezhenname

那就不清楚了，我没遇到过这样的情况，前面acc=0应该是正常的，我当时试过v3，前100轮都是0，之后才慢慢增加

h好的，多谢回复。我还有一个问题就是，我的图片是两行数字，直接使用推理模型出来结果是正确的，但是识别出来的结果是一个列表里面两个元素，我的数据标注这样可以吗？

待识别图像： 1695617413140

数据标注文件：识别结果文件：

Sep 25 '23 04:09 sssjc666

PaddleOCR识别应该都是一行一行识别的吧，感觉你这样标注并不会把后面的连起来

Sep 25 '23 07:09 hezhenname

PaddleOCR识别应该都是一行一行识别的吧，感觉你这样标注并不会把后面的连起来

好的，多谢，我试一下其他的格式

Sep 26 '23 07:09 sssjc666

@sssjc666 can you help me, I have a same problem related to yours.

here is the detailed explaination of how I set enviroment of PaddleOCR.

git clone https://github.com/PaddlePaddle/PaddleOCR.git

cd PaddleOCR/

conda create --name PaddleOCR python=3.8

conda activate PaddleOCR

pip install -r requirements.txt

pip install paddlepaddle-gpu python setup.py install

After that i test an image using pretrained model downloaded from PaddleOCR github repo.

python3 tools/infer_rec.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=/additional_drive/ibrar/PaddleOCR/pretrain_models/en_PP-OCRv3_rec_train/best_accuracy.pdparams Global.infer_img=/additional_drive2/ibrar/preprocessing/test_img/colon.png Global.use_gpu=False

It accuractly recognize the image text.

ls /usr/lib | grep lib

sudo ln -s /usr/local/cuda-12.1/targets/x86_64-linux/include/libcudnn.so.8.9.1 libcudnn.so sudo ln -s /usr/local/cuda-12.1/targets/x86_64-linux/lib/libcublas.so.12.1.3.1 libcublas.so

export LD_LIBRARY_PATH=/usr/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/home/gpu1/.conda/pkgs/cudnn-7.6.5-cuda10.1_0/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/additional_drive/ibrar/PaddleOCR:$LD_LIBRARY_PATH

I have prepared my dataset of arabic language having and set the path in yml file for training PaddleOCR in my custom dataset of arabic language.

Here is the yml file

Global:
  debug: false
  use_gpu: true
  epoch_num: 500
  log_smooth_window: 20
  print_batch_step: 10
  save_model_dir: ./output/v3_arabic_mobile
  save_epoch_step: 3
  eval_batch_step: [0, 2000]
  cal_metric_during_train: true
  pretrained_model:
  checkpoints:
  save_inference_dir:
  use_visualdl: false
  infer_img: ./doc/imgs_words/arabic/ar_2.jpg
  character_dict_path: ppocr/utils/dict/arabic_dict.txt
  max_text_length: &max_text_length 25
  infer_mode: false
  use_space_char: true
  distributed: false
  save_res_path: ./output/rec/predicts_ppocrv3_arabic.txt


Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
  lr:
    name: Cosine
    learning_rate: 0.001
    warmup_epoch: 5
  regularizer:
    name: L2
    factor: 3.0e-05


Architecture:
  model_type: rec
  algorithm: SVTR_LCNet
  Transform:
  Backbone:
    name: MobileNetV1Enhance
    scale: 0.5
    last_conv_stride: [1, 2]
    last_pool_type: avg
    last_pool_kernel_size: [2, 2]
  Head:
    name: MultiHead
    head_list:
      - CTCHead:
          Neck:
            name: svtr
            dims: 64
            depth: 2
            hidden_dims: 120
            use_guide: True
          Head:
            fc_decay: 0.00001
      - SARHead:
          enc_dim: 512
          max_text_length: *max_text_length

Loss:
  name: MultiLoss
  loss_config_list:
    - CTCLoss:
    - SARLoss:

PostProcess:  
  name: CTCLabelDecode

Metric:
  name: RecMetric
  main_indicator: acc
  ignore_space: False

Train:
  dataset:
    name: SimpleDataSet
    data_dir: /additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/train/
    ext_op_transform_idx: 1
    label_file_list:
    - /additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/train/paddle_rec_arr_updated.txt
    transforms:
    - DecodeImage:
        img_mode: BGR
        channel_first: false
    - RecConAug:
        prob: 0.5
        ext_data_num: 2
        image_shape: [48, 320, 3]
    - RecAug:
    - MultiLabelEncode:
    - RecResizeImg:
        image_shape: [3, 48, 320]
    - KeepKeys:
        keep_keys:
        - image
        - label_ctc
        - label_sar
        - length
        - valid_ratio
  loader:
    shuffle: true
    batch_size_per_card: 128
    drop_last: true
    num_workers: 4
Eval:
  dataset:
    name: SimpleDataSet
    data_dir: /additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/val/ 
    label_file_list:
    - /additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/val/paddle_rec_arr_updated.txt
    transforms:
    - DecodeImage:
        img_mode: BGR
        channel_first: false
    - MultiLabelEncode:
    - RecResizeImg:
        image_shape: [3, 48, 320]
    - KeepKeys:
        keep_keys:
        - image
        - label_ctc
        - label_sar
        - length
        - valid_ratio
  loader:
    shuffle: false
    drop_last: false
    batch_size_per_card: 128
    num_workers: 4

python3 tools/train.py -c configs/rec/PP-OCRv3/multi_language/arabic_PP-OCRv3_rec.yml

and here is the ouput I have, still loss : nanxxx

[2023/10/06 13:21:14] ppocr INFO: Architecture : 
[2023/10/06 13:21:14] ppocr INFO:     Backbone : 
[2023/10/06 13:21:14] ppocr INFO:         last_conv_stride : [1, 2]
[2023/10/06 13:21:14] ppocr INFO:         last_pool_kernel_size : [2, 2]
[2023/10/06 13:21:14] ppocr INFO:         last_pool_type : avg
[2023/10/06 13:21:14] ppocr INFO:         name : MobileNetV1Enhance
[2023/10/06 13:21:14] ppocr INFO:         scale : 0.5
[2023/10/06 13:21:14] ppocr INFO:     Head : 
[2023/10/06 13:21:14] ppocr INFO:         head_list : 
[2023/10/06 13:21:14] ppocr INFO:             CTCHead : 
[2023/10/06 13:21:14] ppocr INFO:                 Head : 
[2023/10/06 13:21:14] ppocr INFO:                     fc_decay : 1e-05
[2023/10/06 13:21:14] ppocr INFO:                 Neck : 
[2023/10/06 13:21:14] ppocr INFO:                     depth : 2
[2023/10/06 13:21:14] ppocr INFO:                     dims : 64
[2023/10/06 13:21:14] ppocr INFO:                     hidden_dims : 120
[2023/10/06 13:21:14] ppocr INFO:                     name : svtr
[2023/10/06 13:21:14] ppocr INFO:                     use_guide : True
[2023/10/06 13:21:14] ppocr INFO:             SARHead : 
[2023/10/06 13:21:14] ppocr INFO:                 enc_dim : 512
[2023/10/06 13:21:14] ppocr INFO:                 max_text_length : 25
[2023/10/06 13:21:14] ppocr INFO:         name : MultiHead
[2023/10/06 13:21:14] ppocr INFO:     Transform : None
[2023/10/06 13:21:14] ppocr INFO:     algorithm : SVTR_LCNet
[2023/10/06 13:21:14] ppocr INFO:     model_type : rec
[2023/10/06 13:21:14] ppocr INFO: Eval : 
[2023/10/06 13:21:14] ppocr INFO:     dataset : 
[2023/10/06 13:21:14] ppocr INFO:         data_dir : /additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/val/
[2023/10/06 13:21:14] ppocr INFO:         label_file_list : ['/additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/val/paddle_rec_arr_updated.txt']
[2023/10/06 13:21:14] ppocr INFO:         name : SimpleDataSet
[2023/10/06 13:21:14] ppocr INFO:         transforms : 
[2023/10/06 13:21:14] ppocr INFO:             DecodeImage : 
[2023/10/06 13:21:14] ppocr INFO:                 channel_first : False
[2023/10/06 13:21:14] ppocr INFO:                 img_mode : BGR
[2023/10/06 13:21:14] ppocr INFO:             MultiLabelEncode : None
[2023/10/06 13:21:14] ppocr INFO:             RecResizeImg : 
[2023/10/06 13:21:14] ppocr INFO:                 image_shape : [3, 48, 320]
[2023/10/06 13:21:14] ppocr INFO:             KeepKeys : 
[2023/10/06 13:21:14] ppocr INFO:                 keep_keys : ['image', 'label_ctc', 'label_sar', 'length', 'valid_ratio']
[2023/10/06 13:21:14] ppocr INFO:     loader : 
[2023/10/06 13:21:14] ppocr INFO:         batch_size_per_card : 128
[2023/10/06 13:21:14] ppocr INFO:         drop_last : False
[2023/10/06 13:21:14] ppocr INFO:         num_workers : 4
[2023/10/06 13:21:14] ppocr INFO:         shuffle : False
[2023/10/06 13:21:14] ppocr INFO: Global : 
[2023/10/06 13:21:14] ppocr INFO:     cal_metric_during_train : True
[2023/10/06 13:21:14] ppocr INFO:     character_dict_path : ppocr/utils/dict/arabic_dict.txt
[2023/10/06 13:21:14] ppocr INFO:     checkpoints : None
[2023/10/06 13:21:14] ppocr INFO:     debug : False
[2023/10/06 13:21:14] ppocr INFO:     distributed : False
[2023/10/06 13:21:14] ppocr INFO:     epoch_num : 500
[2023/10/06 13:21:14] ppocr INFO:     eval_batch_step : [0, 2000]
[2023/10/06 13:21:14] ppocr INFO:     infer_img : ./doc/imgs_words/arabic/ar_2.jpg
[2023/10/06 13:21:14] ppocr INFO:     infer_mode : False
[2023/10/06 13:21:14] ppocr INFO:     log_smooth_window : 20
[2023/10/06 13:21:14] ppocr INFO:     max_text_length : 25
[2023/10/06 13:21:14] ppocr INFO:     pretrained_model : None
[2023/10/06 13:21:14] ppocr INFO:     print_batch_step : 10
[2023/10/06 13:21:14] ppocr INFO:     save_epoch_step : 3
[2023/10/06 13:21:14] ppocr INFO:     save_inference_dir : None
[2023/10/06 13:21:14] ppocr INFO:     save_model_dir : ./output/v3_arabic_mobile
[2023/10/06 13:21:14] ppocr INFO:     save_res_path : ./output/rec/predicts_ppocrv3_arabic.txt
[2023/10/06 13:21:14] ppocr INFO:     use_gpu : True
[2023/10/06 13:21:14] ppocr INFO:     use_space_char : True
[2023/10/06 13:21:14] ppocr INFO:     use_visualdl : False
[2023/10/06 13:21:14] ppocr INFO: Loss : 
[2023/10/06 13:21:14] ppocr INFO:     loss_config_list : 
[2023/10/06 13:21:14] ppocr INFO:         CTCLoss : None
[2023/10/06 13:21:14] ppocr INFO:         SARLoss : None
[2023/10/06 13:21:14] ppocr INFO:     name : MultiLoss
[2023/10/06 13:21:14] ppocr INFO: Metric : 
[2023/10/06 13:21:14] ppocr INFO:     ignore_space : False
[2023/10/06 13:21:14] ppocr INFO:     main_indicator : acc
[2023/10/06 13:21:14] ppocr INFO:     name : RecMetric
[2023/10/06 13:21:14] ppocr INFO: Optimizer : 
[2023/10/06 13:21:14] ppocr INFO:     beta1 : 0.9
[2023/10/06 13:21:14] ppocr INFO:     beta2 : 0.999
[2023/10/06 13:21:14] ppocr INFO:     lr : 
[2023/10/06 13:21:14] ppocr INFO:         learning_rate : 0.001
[2023/10/06 13:21:14] ppocr INFO:         name : Cosine
[2023/10/06 13:21:14] ppocr INFO:         warmup_epoch : 5
[2023/10/06 13:21:14] ppocr INFO:     name : Adam
[2023/10/06 13:21:14] ppocr INFO:     regularizer : 
[2023/10/06 13:21:14] ppocr INFO:         factor : 3e-05
[2023/10/06 13:21:14] ppocr INFO:         name : L2
[2023/10/06 13:21:14] ppocr INFO: PostProcess : 
[2023/10/06 13:21:14] ppocr INFO:     name : CTCLabelDecode
[2023/10/06 13:21:14] ppocr INFO: Train : 
[2023/10/06 13:21:14] ppocr INFO:     dataset : 
[2023/10/06 13:21:14] ppocr INFO:         data_dir : /additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/train/
[2023/10/06 13:21:14] ppocr INFO:         ext_op_transform_idx : 1
[2023/10/06 13:21:14] ppocr INFO:         label_file_list : ['/additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/train/paddle_rec_arr_updated.txt']
[2023/10/06 13:21:14] ppocr INFO:         name : SimpleDataSet
[2023/10/06 13:21:14] ppocr INFO:         transforms : 
[2023/10/06 13:21:14] ppocr INFO:             DecodeImage : 
[2023/10/06 13:21:14] ppocr INFO:                 channel_first : False
[2023/10/06 13:21:14] ppocr INFO:                 img_mode : BGR
[2023/10/06 13:21:14] ppocr INFO:             RecConAug : 
[2023/10/06 13:21:14] ppocr INFO:                 ext_data_num : 2
[2023/10/06 13:21:14] ppocr INFO:                 image_shape : [48, 320, 3]
[2023/10/06 13:21:14] ppocr INFO:                 prob : 0.5
[2023/10/06 13:21:14] ppocr INFO:             RecAug : None
[2023/10/06 13:21:14] ppocr INFO:             MultiLabelEncode : None
[2023/10/06 13:21:14] ppocr INFO:             RecResizeImg : 
[2023/10/06 13:21:14] ppocr INFO:                 image_shape : [3, 48, 320]
[2023/10/06 13:21:14] ppocr INFO:             KeepKeys : 
[2023/10/06 13:21:14] ppocr INFO:                 keep_keys : ['image', 'label_ctc', 'label_sar', 'length', 'valid_ratio']
[2023/10/06 13:21:14] ppocr INFO:     loader : 
[2023/10/06 13:21:14] ppocr INFO:         batch_size_per_card : 128
[2023/10/06 13:21:14] ppocr INFO:         drop_last : True
[2023/10/06 13:21:14] ppocr INFO:         num_workers : 4
[2023/10/06 13:21:14] ppocr INFO:         shuffle : True
[2023/10/06 13:21:14] ppocr INFO: profiler_options : None
[2023/10/06 13:21:14] ppocr INFO: train with paddle 2.5.1 and device Place(gpu:0)
[2023/10/06 13:21:14] ppocr INFO: Initialize indexs of datasets:['/additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/train/paddle_rec_arr_updated.txt']
[2023/10/06 13:21:15] ppocr INFO: Initialize indexs of datasets:['/additional_drive2/zain/dataset/raw_data/arabic_docs_combined_caparsoft_Sep22/val/paddle_rec_arr_updated.txt']
[2023/10/06 13:21:16] ppocr INFO: train dataloader has 749 iters
[2023/10/06 13:21:16] ppocr INFO: valid dataloader has 82 iters
[2023/10/06 13:21:16] ppocr INFO: train from scratch
[2023/10/06 13:21:16] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 2000 iterations
[2023/10/06 13:21:35] ppocr INFO: epoch: [1/500], global_step: 10, lr: 0.000001, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 1.08825 s, avg_batch_cost: 1.89337 s, avg_samples: 128.0, ips: 67.60450 samples/s, eta: 8 days, 4:57:26
[2023/10/06 13:21:48] ppocr INFO: epoch: [1/500], global_step: 20, lr: 0.000003, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.78203 s, avg_batch_cost: 1.33533 s, avg_samples: 128.0, ips: 95.85615 samples/s, eta: 6 days, 23:55:41
[2023/10/06 13:22:03] ppocr INFO: epoch: [1/500], global_step: 30, lr: 0.000005, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 1.00161 s, avg_batch_cost: 1.54638 s, avg_samples: 128.0, ips: 82.77376 samples/s, eta: 6 days, 21:34:01
[2023/10/06 13:22:15] ppocr INFO: epoch: [1/500], global_step: 40, lr: 0.000008, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.61786 s, avg_batch_cost: 1.16110 s, avg_samples: 128.0, ips: 110.24001 samples/s, eta: 6 days, 10:21:56
[2023/10/06 13:22:30] ppocr INFO: epoch: [1/500], global_step: 50, lr: 0.000011, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.89978 s, avg_batch_cost: 1.44257 s, avg_samples: 128.0, ips: 88.73038 samples/s, eta: 6 days, 9:29:55
[2023/10/06 13:22:40] ppocr INFO: epoch: [1/500], global_step: 60, lr: 0.000013, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.55627 s, avg_batch_cost: 1.09501 s, avg_samples: 128.0, ips: 116.89396 samples/s, eta: 6 days, 2:53:39
[2023/10/06 13:22:54] ppocr INFO: epoch: [1/500], global_step: 70, lr: 0.000016, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.79183 s, avg_batch_cost: 1.33457 s, avg_samples: 128.0, ips: 95.91138 samples/s, eta: 6 days, 1:44:07
[2023/10/06 13:23:05] ppocr INFO: epoch: [1/500], global_step: 80, lr: 0.000019, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.54361 s, avg_batch_cost: 1.07552 s, avg_samples: 128.0, ips: 119.01189 samples/s, eta: 5 days, 21:29:51
[2023/10/06 13:23:16] ppocr INFO: epoch: [1/500], global_step: 90, lr: 0.000021, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.59985 s, avg_batch_cost: 1.13322 s, avg_samples: 128.0, ips: 112.95207 samples/s, eta: 5 days, 18:52:03
[2023/10/06 13:23:27] ppocr INFO: epoch: [1/500], global_step: 100, lr: 0.000024, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.51949 s, avg_batch_cost: 1.06212 s, avg_samples: 128.0, ips: 120.51386 samples/s, eta: 5 days, 16:01:25
[2023/10/06 13:23:37] ppocr INFO: epoch: [1/500], global_step: 110, lr: 0.000027, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.52135 s, avg_batch_cost: 1.04407 s, avg_samples: 128.0, ips: 122.59768 samples/s, eta: 5 days, 13:31:31
[2023/10/06 13:23:48] ppocr INFO: epoch: [1/500], global_step: 120, lr: 0.000029, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.51163 s, avg_batch_cost: 1.07150 s, avg_samples: 128.0, ips: 119.45873 samples/s, eta: 5 days, 11:40:51
[2023/10/06 13:23:57] ppocr INFO: epoch: [1/500], global_step: 130, lr: 0.000032, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.37556 s, avg_batch_cost: 0.92996 s, avg_samples: 128.0, ips: 137.64060 samples/s, eta: 5 days, 8:59:14
[2023/10/06 13:24:07] ppocr INFO: epoch: [1/500], global_step: 140, lr: 0.000035, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.43143 s, avg_batch_cost: 0.95315 s, avg_samples: 128.0, ips: 134.29092 samples/s, eta: 5 days, 6:51:02
[2023/10/06 13:24:15] ppocr INFO: epoch: [1/500], global_step: 150, lr: 0.000037, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.28326 s, avg_batch_cost: 0.82487 s, avg_samples: 128.0, ips: 155.17676 samples/s, eta: 5 days, 4:06:32
[2023/10/06 13:24:25] ppocr INFO: epoch: [1/500], global_step: 160, lr: 0.000040, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.47801 s, avg_batch_cost: 1.01583 s, avg_samples: 128.0, ips: 126.00486 samples/s, eta: 5 days, 2:57:03
[2023/10/06 13:24:33] ppocr INFO: epoch: [1/500], global_step: 170, lr: 0.000043, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.25367 s, avg_batch_cost: 0.80017 s, avg_samples: 128.0, ips: 159.96559 samples/s, eta: 5 days, 0:36:35
[2023/10/06 13:24:41] ppocr INFO: epoch: [1/500], global_step: 180, lr: 0.000045, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.25914 s, avg_batch_cost: 0.81007 s, avg_samples: 128.0, ips: 158.01161 samples/s, eta: 4 days, 22:35:08
[2023/10/06 13:24:48] ppocr INFO: epoch: [1/500], global_step: 190, lr: 0.000048, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.17812 s, avg_batch_cost: 0.71288 s, avg_samples: 128.0, ips: 179.55362 samples/s, eta: 4 days, 20:14:32
[2023/10/06 13:24:56] ppocr INFO: epoch: [1/500], global_step: 200, lr: 0.000051, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.24905 s, avg_batch_cost: 0.77150 s, avg_samples: 128.0, ips: 165.91077 samples/s, eta: 4 days, 18:26:16
[2023/10/06 13:25:03] ppocr INFO: epoch: [1/500], global_step: 210, lr: 0.000053, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.15386 s, avg_batch_cost: 0.70094 s, avg_samples: 128.0, ips: 182.61269 samples/s, eta: 4 days, 16:27:21
[2023/10/06 13:25:10] ppocr INFO: epoch: [1/500], global_step: 220, lr: 0.000056, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.16276 s, avg_batch_cost: 0.71987 s, avg_samples: 128.0, ips: 177.80991 samples/s, eta: 4 days, 14:44:36
[2023/10/06 13:25:16] ppocr INFO: epoch: [1/500], global_step: 230, lr: 0.000059, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.07249 s, avg_batch_cost: 0.61327 s, avg_samples: 128.0, ips: 208.71682 samples/s, eta: 4 days, 12:41:51
[2023/10/06 13:25:23] ppocr INFO: epoch: [1/500], global_step: 240, lr: 0.000061, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.12566 s, avg_batch_cost: 0.67631 s, avg_samples: 128.0, ips: 189.26302 samples/s, eta: 4 days, 11:05:43
[2023/10/06 13:25:29] ppocr INFO: epoch: [1/500], global_step: 250, lr: 0.000064, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.04584 s, avg_batch_cost: 0.59374 s, avg_samples: 128.0, ips: 215.58130 samples/s, eta: 4 days, 9:16:40
[2023/10/06 13:25:34] ppocr INFO: epoch: [1/500], global_step: 260, lr: 0.000067, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00561 s, avg_batch_cost: 0.54258 s, avg_samples: 128.0, ips: 235.91162 samples/s, eta: 4 days, 7:23:43
[2023/10/06 13:25:40] ppocr INFO: epoch: [1/500], global_step: 270, lr: 0.000069, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.51401 s, avg_samples: 128.0, ips: 249.02132 samples/s, eta: 4 days, 5:32:32
[2023/10/06 13:25:45] ppocr INFO: epoch: [1/500], global_step: 280, lr: 0.000072, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.55473 s, avg_samples: 128.0, ips: 230.74335 samples/s, eta: 4 days, 3:58:21
[2023/10/06 13:25:50] ppocr INFO: epoch: [1/500], global_step: 290, lr: 0.000075, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.01990 s, avg_batch_cost: 0.54240 s, avg_samples: 128.0, ips: 235.98734 samples/s, eta: 4 days, 2:28:00
[2023/10/06 13:25:56] ppocr INFO: epoch: [1/500], global_step: 300, lr: 0.000077, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54070 s, avg_samples: 128.0, ips: 236.72930 samples/s, eta: 4 days, 1:03:19
[2023/10/06 13:26:01] ppocr INFO: epoch: [1/500], global_step: 310, lr: 0.000080, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53417 s, avg_samples: 128.0, ips: 239.62504 samples/s, eta: 3 days, 23:42:47
[2023/10/06 13:26:06] ppocr INFO: epoch: [1/500], global_step: 320, lr: 0.000083, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.52612 s, avg_samples: 128.0, ips: 243.28941 samples/s, eta: 3 days, 22:25:42
[2023/10/06 13:26:12] ppocr INFO: epoch: [1/500], global_step: 330, lr: 0.000085, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00016 s, avg_batch_cost: 0.53212 s, avg_samples: 128.0, ips: 240.54676 samples/s, eta: 3 days, 21:14:26
[2023/10/06 13:26:17] ppocr INFO: epoch: [1/500], global_step: 340, lr: 0.000088, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.52129 s, avg_samples: 128.0, ips: 245.54243 samples/s, eta: 3 days, 20:05:21
[2023/10/06 13:26:23] ppocr INFO: epoch: [1/500], global_step: 350, lr: 0.000091, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.55921 s, avg_samples: 128.0, ips: 228.89524 samples/s, eta: 3 days, 19:06:58
[2023/10/06 13:26:28] ppocr INFO: epoch: [1/500], global_step: 360, lr: 0.000093, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00016 s, avg_batch_cost: 0.54348 s, avg_samples: 128.0, ips: 235.52057 samples/s, eta: 3 days, 18:09:07
[2023/10/06 13:26:33] ppocr INFO: epoch: [1/500], global_step: 370, lr: 0.000096, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.52004 s, avg_samples: 128.0, ips: 246.13627 samples/s, eta: 3 days, 17:10:25
[2023/10/06 13:26:39] ppocr INFO: epoch: [1/500], global_step: 380, lr: 0.000099, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.53828 s, avg_samples: 128.0, ips: 237.79401 samples/s, eta: 3 days, 16:17:48
[2023/10/06 13:26:44] ppocr INFO: epoch: [1/500], global_step: 390, lr: 0.000101, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.52969 s, avg_samples: 128.0, ips: 241.64856 samples/s, eta: 3 days, 15:26:31
[2023/10/06 13:26:49] ppocr INFO: epoch: [1/500], global_step: 400, lr: 0.000104, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53065 s, avg_samples: 128.0, ips: 241.21462 samples/s, eta: 3 days, 14:37:55
[2023/10/06 13:26:55] ppocr INFO: epoch: [1/500], global_step: 410, lr: 0.000107, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53041 s, avg_samples: 128.0, ips: 241.32191 samples/s, eta: 3 days, 13:51:40
[2023/10/06 13:27:00] ppocr INFO: epoch: [1/500], global_step: 420, lr: 0.000109, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54004 s, avg_samples: 128.0, ips: 237.02089 samples/s, eta: 3 days, 13:09:02
[2023/10/06 13:27:05] ppocr INFO: epoch: [1/500], global_step: 430, lr: 0.000112, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52477 s, avg_samples: 128.0, ips: 243.91501 samples/s, eta: 3 days, 12:26:11
[2023/10/06 13:27:10] ppocr INFO: epoch: [1/500], global_step: 440, lr: 0.000115, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52309 s, avg_samples: 128.0, ips: 244.70179 samples/s, eta: 3 days, 11:45:01
[2023/10/06 13:27:16] ppocr INFO: epoch: [1/500], global_step: 450, lr: 0.000117, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52461 s, avg_samples: 128.0, ips: 243.99003 samples/s, eta: 3 days, 11:05:54
[2023/10/06 13:27:21] ppocr INFO: epoch: [1/500], global_step: 460, lr: 0.000120, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.55077 s, avg_samples: 128.0, ips: 232.40186 samples/s, eta: 3 days, 10:32:01
[2023/10/06 13:27:27] ppocr INFO: epoch: [1/500], global_step: 470, lr: 0.000123, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53369 s, avg_samples: 128.0, ips: 239.83958 samples/s, eta: 3 days, 9:57:19
[2023/10/06 13:27:32] ppocr INFO: epoch: [1/500], global_step: 480, lr: 0.000125, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.54482 s, avg_samples: 128.0, ips: 234.93821 samples/s, eta: 3 days, 9:25:30
[2023/10/06 13:27:37] ppocr INFO: epoch: [1/500], global_step: 490, lr: 0.000128, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52896 s, avg_samples: 128.0, ips: 241.98328 samples/s, eta: 3 days, 8:52:57
[2023/10/06 13:27:43] ppocr INFO: epoch: [1/500], global_step: 500, lr: 0.000131, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52320 s, avg_samples: 128.0, ips: 244.64700 samples/s, eta: 3 days, 8:21:00
[2023/10/06 13:27:48] ppocr INFO: epoch: [1/500], global_step: 510, lr: 0.000133, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.51550 s, avg_samples: 128.0, ips: 248.30066 samples/s, eta: 3 days, 7:49:21
[2023/10/06 13:27:53] ppocr INFO: epoch: [1/500], global_step: 520, lr: 0.000136, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.52065 s, avg_samples: 128.0, ips: 245.84786 samples/s, eta: 3 days, 7:19:31
[2023/10/06 13:27:58] ppocr INFO: epoch: [1/500], global_step: 530, lr: 0.000139, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52368 s, avg_samples: 128.0, ips: 244.42396 samples/s, eta: 3 days, 6:51:11
[2023/10/06 13:28:03] ppocr INFO: epoch: [1/500], global_step: 540, lr: 0.000141, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53489 s, avg_samples: 128.0, ips: 239.30113 samples/s, eta: 3 days, 6:25:11
[2023/10/06 13:28:09] ppocr INFO: epoch: [1/500], global_step: 550, lr: 0.000144, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53439 s, avg_samples: 128.0, ips: 239.52446 samples/s, eta: 3 days, 6:00:04
[2023/10/06 13:28:14] ppocr INFO: epoch: [1/500], global_step: 560, lr: 0.000147, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53297 s, avg_samples: 128.0, ips: 240.16332 samples/s, eta: 3 days, 5:35:41
[2023/10/06 13:28:20] ppocr INFO: epoch: [1/500], global_step: 570, lr: 0.000149, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53791 s, avg_samples: 128.0, ips: 237.95709 samples/s, eta: 3 days, 5:12:42
[2023/10/06 13:28:25] ppocr INFO: epoch: [1/500], global_step: 580, lr: 0.000152, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54265 s, avg_samples: 128.0, ips: 235.88022 samples/s, eta: 3 days, 4:51:00
[2023/10/06 13:28:30] ppocr INFO: epoch: [1/500], global_step: 590, lr: 0.000155, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.53701 s, avg_samples: 128.0, ips: 238.35654 samples/s, eta: 3 days, 4:29:27
[2023/10/06 13:28:36] ppocr INFO: epoch: [1/500], global_step: 600, lr: 0.000157, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.56142 s, avg_samples: 128.0, ips: 227.99258 samples/s, eta: 3 days, 4:11:09
[2023/10/06 13:28:41] ppocr INFO: epoch: [1/500], global_step: 610, lr: 0.000160, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54799 s, avg_samples: 128.0, ips: 233.58242 samples/s, eta: 3 days, 3:52:04
[2023/10/06 13:28:47] ppocr INFO: epoch: [1/500], global_step: 620, lr: 0.000163, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53408 s, avg_samples: 128.0, ips: 239.66634 samples/s, eta: 3 days, 3:32:13
[2023/10/06 13:28:52] ppocr INFO: epoch: [1/500], global_step: 630, lr: 0.000165, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.56540 s, avg_samples: 128.0, ips: 226.38746 samples/s, eta: 3 days, 3:16:04
[2023/10/06 13:28:58] ppocr INFO: epoch: [1/500], global_step: 640, lr: 0.000168, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.56888 s, avg_samples: 128.0, ips: 225.00174 samples/s, eta: 3 days, 3:00:47
[2023/10/06 13:29:04] ppocr INFO: epoch: [1/500], global_step: 650, lr: 0.000171, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.56244 s, avg_samples: 128.0, ips: 227.58003 samples/s, eta: 3 days, 2:45:20
[2023/10/06 13:29:09] ppocr INFO: epoch: [1/500], global_step: 660, lr: 0.000173, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53802 s, avg_samples: 128.0, ips: 237.90886 samples/s, eta: 3 days, 2:28:03
[2023/10/06 13:29:15] ppocr INFO: epoch: [1/500], global_step: 670, lr: 0.000176, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54924 s, avg_samples: 128.0, ips: 233.04895 samples/s, eta: 3 days, 2:12:19
[2023/10/06 13:29:20] ppocr INFO: epoch: [1/500], global_step: 680, lr: 0.000179, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54630 s, avg_samples: 128.0, ips: 234.30499 samples/s, eta: 3 days, 1:56:47
[2023/10/06 13:29:25] ppocr INFO: epoch: [1/500], global_step: 690, lr: 0.000181, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52432 s, avg_samples: 128.0, ips: 244.12569 samples/s, eta: 3 days, 1:39:42
[2023/10/06 13:29:31] ppocr INFO: epoch: [1/500], global_step: 700, lr: 0.000184, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53362 s, avg_samples: 128.0, ips: 239.86984 samples/s, eta: 3 days, 1:23:56
[2023/10/06 13:29:36] ppocr INFO: epoch: [1/500], global_step: 710, lr: 0.000187, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.53125 s, avg_samples: 128.0, ips: 240.94088 samples/s, eta: 3 days, 1:08:24
[2023/10/06 13:29:41] ppocr INFO: epoch: [1/500], global_step: 720, lr: 0.000189, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.52896 s, avg_samples: 128.0, ips: 241.98269 samples/s, eta: 3 days, 0:53:07
[2023/10/06 13:29:47] ppocr INFO: epoch: [1/500], global_step: 730, lr: 0.000192, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.52500 s, avg_samples: 128.0, ips: 243.80807 samples/s, eta: 3 days, 0:37:53
[2023/10/06 13:29:52] ppocr INFO: epoch: [1/500], global_step: 740, lr: 0.000195, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.54336 s, avg_samples: 128.0, ips: 235.57141 samples/s, eta: 3 days, 0:24:37
[2023/10/06 13:29:57] ppocr INFO: epoch: [1/500], global_step: 749, lr: 0.000197, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00024 s, avg_batch_cost: 0.48613 s, avg_samples: 115.2, ips: 236.97573 samples/s, eta: 3 days, 0:12:45
[2023/10/06 13:29:57] ppocr INFO: save model in ./output/v3_arabic_mobile/latest
[2023/10/06 13:29:58] ppocr INFO: epoch: [2/500], global_step: 750, lr: 0.000197, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.08468 s, avg_batch_cost: 0.14658 s, avg_samples: 12.8, ips: 87.32420 samples/s, eta: 3 days, 0:19:08
[2023/10/06 13:30:04] ppocr INFO: epoch: [2/500], global_step: 760, lr: 0.000200, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54022 s, avg_samples: 128.0, ips: 236.94009 samples/s, eta: 3 days, 0:06:12
[2023/10/06 13:30:09] ppocr INFO: epoch: [2/500], global_step: 770, lr: 0.000203, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54077 s, avg_samples: 128.0, ips: 236.69792 samples/s, eta: 2 days, 23:53:39
[2023/10/06 13:30:14] ppocr INFO: epoch: [2/500], global_step: 780, lr: 0.000205, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52389 s, avg_samples: 128.0, ips: 244.32843 samples/s, eta: 2 days, 23:40:04
[2023/10/06 13:30:20] ppocr INFO: epoch: [2/500], global_step: 790, lr: 0.000208, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53729 s, avg_samples: 128.0, ips: 238.23244 samples/s, eta: 2 days, 23:27:53
[2023/10/06 13:30:25] ppocr INFO: epoch: [2/500], global_step: 800, lr: 0.000211, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.52781 s, avg_samples: 128.0, ips: 242.50922 samples/s, eta: 2 days, 23:15:16
[2023/10/06 13:30:30] ppocr INFO: epoch: [2/500], global_step: 810, lr: 0.000213, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.53596 s, avg_samples: 128.0, ips: 238.82419 samples/s, eta: 2 days, 23:03:35
[2023/10/06 13:30:36] ppocr INFO: epoch: [2/500], global_step: 820, lr: 0.000216, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.52174 s, avg_samples: 128.0, ips: 245.33112 samples/s, eta: 2 days, 22:51:06
[2023/10/06 13:30:41] ppocr INFO: epoch: [2/500], global_step: 830, lr: 0.000219, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53321 s, avg_samples: 128.0, ips: 240.05770 samples/s, eta: 2 days, 22:39:46
[2023/10/06 13:30:46] ppocr INFO: epoch: [2/500], global_step: 840, lr: 0.000221, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.51663 s, avg_samples: 128.0, ips: 247.75835 samples/s, eta: 2 days, 22:27:29
[2023/10/06 13:30:51] ppocr INFO: epoch: [2/500], global_step: 850, lr: 0.000224, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.52712 s, avg_samples: 128.0, ips: 242.82905 samples/s, eta: 2 days, 22:16:16
[2023/10/06 13:30:57] ppocr INFO: epoch: [2/500], global_step: 860, lr: 0.000227, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.53367 s, avg_samples: 128.0, ips: 239.84981 samples/s, eta: 2 days, 22:05:46
[2023/10/06 13:31:02] ppocr INFO: epoch: [2/500], global_step: 870, lr: 0.000230, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52799 s, avg_samples: 128.0, ips: 242.42656 samples/s, eta: 2 days, 21:55:06
[2023/10/06 13:31:07] ppocr INFO: epoch: [2/500], global_step: 880, lr: 0.000232, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.52588 s, avg_samples: 128.0, ips: 243.40093 samples/s, eta: 2 days, 21:44:32
[2023/10/06 13:31:13] ppocr INFO: epoch: [2/500], global_step: 890, lr: 0.000235, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54086 s, avg_samples: 128.0, ips: 236.66026 samples/s, eta: 2 days, 21:35:15
[2023/10/06 13:31:18] ppocr INFO: epoch: [2/500], global_step: 900, lr: 0.000238, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54240 s, avg_samples: 128.0, ips: 235.98816 samples/s, eta: 2 days, 21:26:16
[2023/10/06 13:31:23] ppocr INFO: epoch: [2/500], global_step: 910, lr: 0.000240, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.53966 s, avg_samples: 128.0, ips: 237.18535 samples/s, eta: 2 days, 21:17:18
[2023/10/06 13:31:29] ppocr INFO: epoch: [2/500], global_step: 920, lr: 0.000243, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.51186 s, avg_samples: 128.0, ips: 250.06955 samples/s, eta: 2 days, 21:06:39
[2023/10/06 13:31:34] ppocr INFO: epoch: [2/500], global_step: 930, lr: 0.000246, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.51984 s, avg_samples: 128.0, ips: 246.22771 samples/s, eta: 2 days, 20:56:45
[2023/10/06 13:31:39] ppocr INFO: epoch: [2/500], global_step: 940, lr: 0.000248, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.54609 s, avg_samples: 128.0, ips: 234.39172 samples/s, eta: 2 days, 20:48:48
[2023/10/06 13:31:45] ppocr INFO: epoch: [2/500], global_step: 950, lr: 0.000251, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.54968 s, avg_samples: 128.0, ips: 232.86372 samples/s, eta: 2 days, 20:41:15
[2023/10/06 13:31:50] ppocr INFO: epoch: [2/500], global_step: 960, lr: 0.000254, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.52278 s, avg_samples: 128.0, ips: 244.84689 samples/s, eta: 2 days, 20:32:07
[2023/10/06 13:31:55] ppocr INFO: epoch: [2/500], global_step: 970, lr: 0.000256, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54493 s, avg_samples: 128.0, ips: 234.89217 samples/s, eta: 2 days, 20:24:36
[2023/10/06 13:32:01] ppocr INFO: epoch: [2/500], global_step: 980, lr: 0.000259, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54489 s, avg_samples: 128.0, ips: 234.90906 samples/s, eta: 2 days, 20:17:13
[2023/10/06 13:32:06] ppocr INFO: epoch: [2/500], global_step: 990, lr: 0.000262, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.53534 s, avg_samples: 128.0, ips: 239.10099 samples/s, eta: 2 days, 20:09:23
[2023/10/06 13:32:12] ppocr INFO: epoch: [2/500], global_step: 1000, lr: 0.000264, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53432 s, avg_samples: 128.0, ips: 239.55743 samples/s, eta: 2 days, 20:01:38
[2023/10/06 13:32:17] ppocr INFO: epoch: [2/500], global_step: 1010, lr: 0.000267, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.56337 s, avg_samples: 128.0, ips: 227.20421 samples/s, eta: 2 days, 19:55:50
[2023/10/06 13:32:23] ppocr INFO: epoch: [2/500], global_step: 1020, lr: 0.000270, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54073 s, avg_samples: 128.0, ips: 236.71596 samples/s, eta: 2 days, 19:48:46
[2023/10/06 13:32:28] ppocr INFO: epoch: [2/500], global_step: 1030, lr: 0.000272, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53771 s, avg_samples: 128.0, ips: 238.04639 samples/s, eta: 2 days, 19:41:39
[2023/10/06 13:32:34] ppocr INFO: epoch: [2/500], global_step: 1040, lr: 0.000275, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.55312 s, avg_samples: 128.0, ips: 231.41354 samples/s, eta: 2 days, 19:35:36
[2023/10/06 13:32:39] ppocr INFO: epoch: [2/500], global_step: 1050, lr: 0.000278, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53664 s, avg_samples: 128.0, ips: 238.52127 samples/s, eta: 2 days, 19:28:41
[2023/10/06 13:32:44] ppocr INFO: epoch: [2/500], global_step: 1060, lr: 0.000280, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.51724 s, avg_samples: 128.0, ips: 247.46538 samples/s, eta: 2 days, 19:20:45
[2023/10/06 13:32:49] ppocr INFO: epoch: [2/500], global_step: 1070, lr: 0.000283, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53816 s, avg_samples: 128.0, ips: 237.84742 samples/s, eta: 2 days, 19:14:11
[2023/10/06 13:32:55] ppocr INFO: epoch: [2/500], global_step: 1080, lr: 0.000286, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53815 s, avg_samples: 128.0, ips: 237.85251 samples/s, eta: 2 days, 19:07:44
[2023/10/06 13:33:00] ppocr INFO: epoch: [2/500], global_step: 1090, lr: 0.000288, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54463 s, avg_samples: 128.0, ips: 235.01980 samples/s, eta: 2 days, 19:01:46
[2023/10/06 13:33:06] ppocr INFO: epoch: [2/500], global_step: 1100, lr: 0.000291, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54392 s, avg_samples: 128.0, ips: 235.32664 samples/s, eta: 2 days, 18:55:52
[2023/10/06 13:33:11] ppocr INFO: epoch: [2/500], global_step: 1110, lr: 0.000294, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54504 s, avg_samples: 128.0, ips: 234.84665 samples/s, eta: 2 days, 18:50:09
[2023/10/06 13:33:17] ppocr INFO: epoch: [2/500], global_step: 1120, lr: 0.000296, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.56626 s, avg_samples: 128.0, ips: 226.04642 samples/s, eta: 2 days, 18:45:42
[2023/10/06 13:33:22] ppocr INFO: epoch: [2/500], global_step: 1130, lr: 0.000299, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00015 s, avg_batch_cost: 0.54136 s, avg_samples: 128.0, ips: 236.44196 samples/s, eta: 2 days, 18:39:57
[2023/10/06 13:33:28] ppocr INFO: epoch: [2/500], global_step: 1140, lr: 0.000302, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54374 s, avg_samples: 128.0, ips: 235.40616 samples/s, eta: 2 days, 18:34:26
[2023/10/06 13:33:33] ppocr INFO: epoch: [2/500], global_step: 1150, lr: 0.000304, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.54877 s, avg_samples: 128.0, ips: 233.25011 samples/s, eta: 2 days, 18:29:17
[2023/10/06 13:33:39] ppocr INFO: epoch: [2/500], global_step: 1160, lr: 0.000307, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.54821 s, avg_samples: 128.0, ips: 233.48869 samples/s, eta: 2 days, 18:24:12
[2023/10/06 13:33:44] ppocr INFO: epoch: [2/500], global_step: 1170, lr: 0.000310, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.55393 s, avg_samples: 128.0, ips: 231.07563 samples/s, eta: 2 days, 18:19:30
[2023/10/06 13:33:50] ppocr INFO: epoch: [2/500], global_step: 1180, lr: 0.000312, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00014 s, avg_batch_cost: 0.53854 s, avg_samples: 128.0, ips: 237.68089 samples/s, eta: 2 days, 18:14:04
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[2023/10/06 14:56:57] ppocr INFO: epoch: [478/500], global_step: 10440, lr: 0.000999, acc: 0.000000, norm_edit_dis: 0.000000, CTCLoss: nanxxx, SARLoss: nanxxx, loss: nanxxx, avg_reader_cost: 0.00013 s, avg_batch_cost: 0.47577 s, avg_samples: 128.0, ips: 269.03580 samples/s, eta: 1 days, 7:16:19

I'm feeling a bit confused, and I wonder if I might be doing something incorrectly. I've been looking into several GitHub issues related to PaddleOCR, and most of them seem to be about achieving an accuracy of 0. The contributors to PaddleOCR have suggested that increasing the number of epochs can resolve this issue. I've observed many of these discussions, and they all share a common pattern. The loss starts decreasing from the very first epoch, and there are no instances of loss being "nanxxx." But in my case I am getting loss: nanxxx from start of epoch. I train it for 1500 epoches and it take 3 days, but didn't found any change in the loss and accuracy.

I'm seeking assistance and advice from anyone who might be able to shed light on this matter.

Oct 09 '23 13:10 IbrarBabar009

PaddleOCR识别应该都是一行一行识别的吧，感觉你这样标注并不会把后面的连起来

好的，多谢，我试一下其他的格式

请问你最后标注格式是什么样的？

Apr 24 '24 02:04 dirac472

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小白提问：使用ch_PP-OCRv3_rec_train中英文识别模型，使用个人数据对模型进行训练最后acc为0

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