ocr训练(检测+识别)，best_accuracy模型都没有生成的问题

[zyj0721]

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：windows
• 版本号/Version：Paddle：2.5.2 PaddleOCR：2.7.0.3
• 运行指令/Command Code：python tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml
• 完整报错/Complete Error Message：没有报错

`Global: debug: false use_gpu: false epoch_num: 10 log_smooth_window: 2 print_batch_step: 1 save_model_dir: ./output/rec_ppocr_v3 save_epoch_step: 3 eval_batch_step: [0, 2] cal_metric_during_train: true pretrained_model: ./pretrain_models/ch_ppocr_server_v2.0_rec_train/best_accuracy.pdparams checkpoints: save_inference_dir: use_visualdl: false infer_img: doc/imgs_words/ch/word_1.jpg character_dict_path: ppocr/utils/ppocr_keys_v1.txt max_text_length: &max_text_length 250 infer_mode: false use_space_char: true distributed: true save_res_path: ./output/rec/predicts_ppocrv3.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: ./train_data/ ext_op_transform_idx: 1 label_file_list:

• ./train_data/rec/train.txt transforms:
• DecodeImage: img_mode: BGR channel_first: false
• RecConAug: prob: 0.5 ext_data_num: 2 image_shape: [48, 320, 3] max_text_length: *max_text_length
• 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: ./train_data label_file_list:
• ./train_data/rec/val.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 `

能够正常生成iter_epoch_n.文件，但是一直没有best_accuracy.，请问这是什么原因呢？

[xiongwujun]

同问

[laokamo]

我也遇到这样的问题

• 系统环境/System Environment：windows
• 版本号/Version：PaddleOCR-release-2.6.1
• 运行指令/Command Code：python tools/train.py -c configs/det/ch_PP-OCRv4/ch_PP-OCRv4_det_danju.yml

• 完整报错/Complete Error Message：没有报错

  Global: debug: true use_gpu: true epoch_num: &epoch_num 2400 log_smooth_window: 20 print_batch_step: 100 save_model_dir: ./output/ch_PP-OCRv4 save_epoch_step: 100 eval_batch_step:

  • 0
  • 100 cal_metric_during_train: false checkpoints: pretrained_model: ./pretrain_models/ch_PP-OCRv4_det_server_train/best_accuracy.pdparams save_inference_dir: null use_visualdl: false infer_img: doc/imgs_en/img_10.jpg save_res_path: ./checkpoints/det_db/predicts_db.txt distributed: true

Architecture: model_type: det algorithm: DB Transform: null Backbone: name: PPHGNet_small det: True Neck: name: LKPAN out_channels: 256 intracl: true Head: name: PFHeadLocal k: 50 mode: "large"

Loss: name: DBLoss balance_loss: true main_loss_type: DiceLoss alpha: 5 beta: 10 ohem_ratio: 3

Optimizer: name: Adam beta1: 0.9 beta2: 0.999 lr: name: Cosine learning_rate: 0.01 #(8*8c) warmup_epoch: 2 regularizer: name: L2 factor: 1e-6

PostProcess: name: DBPostProcess thresh: 0.3 box_thresh: 0.6 max_candidates: 1000 unclip_ratio: 1.5

Metric: name: DetMetric main_indicator: hmean

Train: dataset: name: SimpleDataSet data_dir: ./train_data/ label_file_list:

• ./train_data/det/train.txt ratio_list: [1.0] transforms:
  • DecodeImage: img_mode: BGR channel_first: false
  • DetLabelEncode: null
  • CopyPaste: null
  • IaaAugment: augmenter_args:
    • type: Fliplr args: p: 0.5
    • type: Affine args: rotate:
      • -10
      • 10
    • type: Resize args: size:
      • 0.5
      • 3
  • EastRandomCropData: size:
    • 640
    • 640 max_tries: 50 keep_ratio: true
  • MakeBorderMap: shrink_ratio: 0.4 thresh_min: 0.3 thresh_max: 0.7 total_epoch: *epoch_num
  • MakeShrinkMap: shrink_ratio: 0.4 min_text_size: 8 total_epoch: *epoch_num
  • NormalizeImage: scale: 1./255. mean:
    • 0.485
    • 0.456
    • 0.406 std:
    • 0.229
    • 0.224
    • 0.225 order: hwc
  • ToCHWImage: null
  • KeepKeys: keep_keys:
    • image
    • threshold_map
    • threshold_mask
    • shrink_map
    • shrink_mask loader: shuffle: true drop_last: false batch_size_per_card: 8 num_workers: 8

Eval: dataset: name: SimpleDataSet data_dir: ./train_data/ label_file_list:

• ./train_data/det/val.txt transforms:
  • DecodeImage: img_mode: BGR channel_first: false
  • DetLabelEncode: null
  • DetResizeForTest:
  • NormalizeImage: scale: 1./255. mean:
    • 0.485
    • 0.456
    • 0.406 std:
    • 0.229
    • 0.224
    • 0.225 order: hwc
  • ToCHWImage: null
  • KeepKeys: keep_keys:
    • image
    • shape
    • polys
    • ignore_tags loader: shuffle: false drop_last: false batch_size_per_card: 1 num_workers: 2 profiler_options: null