# 识别结果 参数基本采用默认参数，都训练 **10轮** 。 ## PP-OCRv5_server (极好) 识别效果：**极好** ，基本都是别出来了，并且正确。 ![image-20250630141231138](./assets/%E9%9B%86%E8%A3%85%E7%AE%B1%E5%8F%B7%E8%AF%86%E5%88%AB/image-20250630141231138.png) ### PP-OCRv5_server_det.yml ```yaml Global: model_name: PP-OCRv5_server_det # To use static model for inference. debug: false use_gpu: true epoch_num: &epoch_num 10 log_smooth_window: 20 print_batch_step: 10 save_model_dir: ./output-v5server/PP-OCRv5_server_det save_epoch_step: 200 eval_batch_step: - 0 - 200 cal_metric_during_train: false checkpoints: pretrained_model: ./pretrain_models/PP-OCRv5_server_det_pretrained.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: false Architecture: model_type: det algorithm: DB Transform: null Backbone: name: PPHGNetV2_B4 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: 0.001 #(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: ./data/input0/images #图片文件夹路径 label_file_list: - ./det_train_label.txt #标签路径 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: 10 num_workers: 1 Eval: dataset: name: SimpleDataSet data_dir: ./data/input0/images label_file_list: - ./det_eval_label.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: 1 profiler_options: null ``` ### PP-OCRv5_server_rec.yml ```yaml Global: model_name: PP-OCRv5_server_rec # To use static model for inference. debug: false use_gpu: true epoch_num: 10 log_smooth_window: 20 print_batch_step: 10 save_model_dir: ./output-v5server/PP-OCRv5_server_rec save_epoch_step: 200 eval_batch_step: [0, 200] cal_metric_during_train: true calc_epoch_interval: 1 pretrained_model: ./pretrain_models/PP-OCRv5_server_rec_pretrained.pdparams checkpoints: save_inference_dir: use_visualdl: false infer_img: doc/imgs_words/ch/word_1.jpg character_dict_path: ./ppocr/utils/dict/ppocrv5_dict.txt max_text_length: &max_text_length 25 infer_mode: false use_space_char: true distributed: true save_res_path: ./output-v5server/rec/predicts_ppocrv5.txt d2s_train_image_shape: [3, 48, 320] Optimizer: name: Adam beta1: 0.9 beta2: 0.999 lr: name: Cosine learning_rate: 0.0005 warmup_epoch: 1 regularizer: name: L2 factor: 3.0e-05 Architecture: model_type: rec algorithm: SVTR_HGNet Transform: Backbone: name: PPHGNetV2_B4 text_rec: True Head: name: MultiHead head_list: - CTCHead: Neck: name: svtr dims: 120 depth: 2 hidden_dims: 120 kernel_size: [1, 3] use_guide: True Head: fc_decay: 0.00001 - NRTRHead: nrtr_dim: 384 max_text_length: *max_text_length Loss: name: MultiLoss loss_config_list: - CTCLoss: - NRTRLoss: PostProcess: name: CTCLabelDecode Metric: name: RecMetric main_indicator: acc Train: dataset: name: MultiScaleDataSet ds_width: false data_dir: ./RecTrainData/ ext_op_transform_idx: 1 label_file_list: - ./rec_train_label.txt transforms: - DecodeImage: img_mode: BGR channel_first: false - RecAug: - MultiLabelEncode: gtc_encode: NRTRLabelEncode - KeepKeys: keep_keys: - image - label_ctc - label_gtc - length - valid_ratio sampler: name: MultiScaleSampler scales: [[320, 32], [320, 48], [320, 64]] first_bs: &bs 12 fix_bs: false divided_factor: [8, 16] # w, h is_training: True loader: shuffle: true batch_size_per_card: 10 drop_last: true num_workers: 1 Eval: dataset: name: SimpleDataSet data_dir: ./RecEvalData/ label_file_list: - ./rec_eval_label.txt transforms: - DecodeImage: img_mode: BGR channel_first: false - MultiLabelEncode: gtc_encode: NRTRLabelEncode - RecResizeImg: image_shape: [3, 48, 320] - KeepKeys: keep_keys: - image - label_ctc - label_gtc - length - valid_ratio loader: shuffle: false drop_last: false batch_size_per_card: 1 num_workers: 1 ``` ## PP-OCRv5_mobile ### PP-OCRv5_mobile_det.yml ```yaml Global: model_name: PP-OCRv5_mobile_det # To use static model for inference. debug: false use_gpu: true epoch_num: &epoch_num 100 log_smooth_window: 20 print_batch_step: 100 save_model_dir: ./output-v5mobile/PP-OCRv5_mobile_det save_epoch_step: 200 eval_batch_step: - 0 - 200 cal_metric_during_train: false checkpoints: pretrained_model: https://paddleocr.bj.bcebos.com/pretrained/PPLCNetV3_x0_75_ocr_det.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 d2s_train_image_shape: [3, 640, 640] distributed: true Architecture: model_type: det algorithm: DB Transform: null Backbone: name: PPLCNetV3 scale: 0.75 det: True Neck: name: RSEFPN out_channels: 96 shortcut: True Head: name: DBHead k: 50 fix_nan: True 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: 0.001 #(8*8c) warmup_epoch: 2 regularizer: name: L2 factor: 5.0e-05 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: ./data/input0/images #图片文件夹路径 label_file_list: - ./det_train_label.txt #标签路径 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: 14 num_workers: 1 Eval: dataset: name: SimpleDataSet data_dir: ./data/input0/images label_file_list: - ./det_eval_label.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: 1 profiler_options: null ``` ### PP-OCRv5_mobile_rec.yml ```yaml Global: model_name: PP-OCRv5_mobile_rec # To use static model for inference. debug: false use_gpu: true epoch_num: 75 log_smooth_window: 20 print_batch_step: 10 save_model_dir: ./output/PP-OCRv5_mobile_rec save_epoch_step: 10 eval_batch_step: [0, 2000] cal_metric_during_train: true pretrained_model: https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_rec_pretrained.pdparams checkpoints: save_inference_dir: use_visualdl: false infer_img: doc/imgs_words/ch/word_1.jpg character_dict_path: ./ppocr/utils/dict/ppocrv5_dict.txt max_text_length: &max_text_length 25 infer_mode: false use_space_char: true distributed: true save_res_path: ./output/rec/predicts_ppocrv5.txt d2s_train_image_shape: [3, 48, 320] Optimizer: name: Adam beta1: 0.9 beta2: 0.999 lr: name: Cosine learning_rate: 0.0005 warmup_epoch: 5 regularizer: name: L2 factor: 3.0e-05 Architecture: model_type: rec algorithm: SVTR_LCNet Transform: Backbone: name: PPLCNetV3 scale: 0.95 Head: name: MultiHead head_list: - CTCHead: Neck: name: svtr dims: 120 depth: 2 hidden_dims: 120 kernel_size: [1, 3] use_guide: True Head: fc_decay: 0.00001 - NRTRHead: nrtr_dim: 384 max_text_length: *max_text_length Loss: name: MultiLoss loss_config_list: - CTCLoss: - NRTRLoss: PostProcess: name: CTCLabelDecode Metric: name: RecMetric main_indicator: acc Train: dataset: name: MultiScaleDataSet ds_width: false data_dir: ./RecTrainData/ ext_op_transform_idx: 1 label_file_list: - ./rec_train_label.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: gtc_encode: NRTRLabelEncode - KeepKeys: keep_keys: - image - label_ctc - label_gtc - length - valid_ratio sampler: name: MultiScaleSampler scales: [[320, 32], [320, 48], [320, 64]] first_bs: &bs 128 fix_bs: false divided_factor: [8, 16] # w, h is_training: True loader: shuffle: true batch_size_per_card: 14 drop_last: true num_workers: 1 Eval: dataset: name: SimpleDataSet data_dir: ./RecEvalData/ label_file_list: - ./rec_eval_label.txt transforms: - DecodeImage: img_mode: BGR channel_first: false - MultiLabelEncode: gtc_encode: NRTRLabelEncode - RecResizeImg: image_shape: [3, 48, 320] - KeepKeys: keep_keys: - image - label_ctc - label_gtc - length - valid_ratio loader: shuffle: false drop_last: false batch_size_per_card: 14 num_workers: 1 ```