update link in doc
This commit is contained in:
WenmuZhou
parent
569deedc41
commit
b5f9a7ec5b
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@ -1,130 +0,0 @@
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Global:
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use_gpu: true
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epoch_num: 1200
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log_smooth_window: 20
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print_batch_step: 2
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save_model_dir: ./output/det_r50_vd/
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save_epoch_step: 1200
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: 8
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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load_static_weights: True
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cal_metric_during_train: False
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pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
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checkpoints:
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save_inference_dir:
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use_visualdl: True
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infer_img: doc/imgs_en/img_10.jpg
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save_res_path: ./output/det_db/predicts_db.txt
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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learning_rate:
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lr: 0.001
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regularizer:
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name: 'L2'
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factor: 0
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Architecture:
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type: det
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algorithm: DB
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Transform:
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Backbone:
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name: ResNet
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layers: 50
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Neck:
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name: FPN
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out_channels: 256
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Head:
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name: DBHead
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k: 50
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Loss:
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name: DBLoss
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balance_loss: true
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main_loss_type: DiceLoss
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alpha: 5
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beta: 10
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ohem_ratio: 3
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PostProcess:
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name: DBPostProcess
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thresh: 0.3
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box_thresh: 0.6
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max_candidates: 1000
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unclip_ratio: 1.5
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Metric:
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name: DetMetric
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main_indicator: hmean
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TRAIN:
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dataset:
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name: SimpleDataSet
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data_dir: ./detection/
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file_list:
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- ./detection/train_icdar2015_label.txt # dataset1
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ratio_list: [1.0]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- DetLabelEncode: # Class handling label
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- IaaAugment:
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augmenter_args:
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- { 'type': Fliplr, 'args': { 'p': 0.5 } }
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- { 'type': Affine, 'args': { 'rotate': [ -10,10 ] } }
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- { 'type': Resize,'args': { 'size': [ 0.5,3 ] } }
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- EastRandomCropData:
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size: [ 640,640 ]
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max_tries: 50
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keep_ratio: true
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- MakeBorderMap:
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shrink_ratio: 0.4
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thresh_min: 0.3
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thresh_max: 0.7
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- MakeShrinkMap:
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shrink_ratio: 0.4
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min_text_size: 8
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- NormalizeImage:
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scale: 1./255.
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mean: [ 0.485, 0.456, 0.406 ]
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std: [ 0.229, 0.224, 0.225 ]
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order: 'hwc'
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- ToCHWImage:
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- keepKeys:
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keep_keys: ['image','threshold_map','threshold_mask','shrink_map','shrink_mask'] # dataloader will return list in this order
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loader:
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shuffle: True
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drop_last: False
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batch_size: 16
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num_workers: 8
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EVAL:
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dataset:
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name: SimpleDataSet
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data_dir: ./detection/
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file_list:
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- ./detection/test_icdar2015_label.txt
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- DetLabelEncode: # Class handling label
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- DetResizeForTest:
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image_shape: [736,1280]
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- NormalizeImage:
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scale: 1./255.
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mean: [ 0.485, 0.456, 0.406 ]
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std: [ 0.229, 0.224, 0.225 ]
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order: 'hwc'
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- ToCHWImage:
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- keepKeys:
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keep_keys: ['image','shape','polys','ignore_tags']
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loader:
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shuffle: False
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drop_last: False
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batch_size: 1 # must be 1
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num_workers: 8
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@ -1,106 +0,0 @@
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Global:
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use_gpu: false
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epoch_num: 500
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
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save_epoch_step: 500
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: 127
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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load_static_weights: True
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cal_metric_during_train: True
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: doc/imgs_words/ch/word_1.jpg
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# for data or label process
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max_text_length: 80
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character_dict_path: ppocr/utils/ppocr_keys_v1.txt
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character_type: 'ch'
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use_space_char: False
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infer_mode: False
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use_tps: False
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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learning_rate:
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lr: 0.001
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regularizer:
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name: 'L2'
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factor: 0.00001
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Architecture:
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type: rec
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algorithm: CRNN
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Transform:
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Backbone:
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name: MobileNetV3
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scale: 0.5
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model_name: small
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small_stride: [ 1, 2, 2, 2 ]
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Neck:
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name: SequenceEncoder
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encoder_type: fc
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hidden_size: 96
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Head:
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name: CTC
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fc_decay: 0.00001
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Loss:
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name: CTCLoss
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PostProcess:
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name: CTCLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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TRAIN:
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dataset:
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name: SimpleDataSet
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data_dir: ./rec
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file_list:
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- ./rec/train.txt # dataset1
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ratio_list: [ 0.4,0.6 ]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- CTCLabelEncode: # Class handling label
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- RecAug:
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- RecResizeImg:
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image_shape: [ 3,32,320 ]
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- keepKeys:
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keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
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loader:
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batch_size: 256
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shuffle: True
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drop_last: True
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num_workers: 8
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EVAL:
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dataset:
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name: SimpleDataSet
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data_dir: ./rec
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file_list:
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- ./rec/val.txt
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- CTCLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [ 3,32,320 ]
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- keepKeys:
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keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
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loader:
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shuffle: False
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drop_last: False
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batch_size: 256
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num_workers: 8
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@ -1,104 +0,0 @@
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Global:
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use_gpu: false
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epoch_num: 500
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec/res34_none_bilstm_ctc/
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save_epoch_step: 500
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: 127
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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load_static_weights: True
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cal_metric_during_train: True
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: doc/imgs_words/ch/word_1.jpg
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# for data or label process
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max_text_length: 80
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character_dict_path: ppocr/utils/ppocr_keys_v1.txt
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character_type: 'ch'
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use_space_char: False
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infer_mode: False
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use_tps: False
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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learning_rate:
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lr: 0.001
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regularizer:
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name: 'L2'
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factor: 0.00001
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Architecture:
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type: rec
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algorithm: CRNN
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Transform:
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Backbone:
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name: ResNet
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layers: 34
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Neck:
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name: SequenceEncoder
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encoder_type: fc
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hidden_size: 96
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Head:
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name: CTC
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fc_decay: 0.00001
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Loss:
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name: CTCLoss
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PostProcess:
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name: CTCLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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TRAIN:
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dataset:
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name: SimpleDataSet
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data_dir: ./rec
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file_list:
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- ./rec/train.txt # dataset1
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ratio_list: [ 0.4,0.6 ]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- CTCLabelEncode: # Class handling label
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- RecAug:
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- RecResizeImg:
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image_shape: [ 3,32,320 ]
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- keepKeys:
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keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
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loader:
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batch_size: 256
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shuffle: True
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drop_last: True
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num_workers: 8
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EVAL:
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dataset:
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name: SimpleDataSet
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data_dir: ./rec
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file_list:
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- ./rec/val.txt
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- CTCLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [ 3,32,320 ]
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- keepKeys:
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keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
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loader:
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shuffle: False
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drop_last: False
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batch_size: 256
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num_workers: 8
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@ -1,103 +0,0 @@
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Global:
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use_gpu: false
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epoch_num: 500
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec/res34_none_none_ctc/
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save_epoch_step: 500
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: 127
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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load_static_weights: True
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cal_metric_during_train: True
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: doc/imgs_words/ch/word_1.jpg
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# for data or label process
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max_text_length: 80
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character_dict_path: ppocr/utils/ppocr_keys_v1.txt
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character_type: 'ch'
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use_space_char: False
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infer_mode: False
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use_tps: False
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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learning_rate:
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lr: 0.001
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regularizer:
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name: 'L2'
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factor: 0.00001
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Architecture:
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type: rec
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algorithm: CRNN
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Transform:
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Backbone:
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name: ResNet
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layers: 34
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Neck:
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name: SequenceEncoder
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encoder_type: reshape
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Head:
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name: CTC
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fc_decay: 0.00001
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Loss:
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name: CTCLoss
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PostProcess:
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name: CTCLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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TRAIN:
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dataset:
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name: SimpleDataSet
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data_dir: ./rec
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file_list:
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- ./rec/train.txt # dataset1
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ratio_list: [ 0.4,0.6 ]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- CTCLabelEncode: # Class handling label
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- RecAug:
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- RecResizeImg:
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image_shape: [ 3,32,320 ]
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- keepKeys:
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keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
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loader:
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batch_size: 256
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shuffle: True
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drop_last: True
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num_workers: 8
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EVAL:
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dataset:
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name: SimpleDataSet
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data_dir: ./rec
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file_list:
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- ./rec/val.txt
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- CTCLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [ 3,32,320 ]
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- keepKeys:
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keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
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loader:
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shuffle: False
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drop_last: False
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batch_size: 256
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num_workers: 8
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@ -41,7 +41,7 @@ inference 模型（`paddle.jit.save`保存的模型）
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下载超轻量级中文检测模型：
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```
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wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
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wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
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```
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上述模型是以MobileNetV3为backbone训练的DB算法，将训练好的模型转换成inference模型只需要运行如下命令：
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```
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@ -51,9 +51,9 @@ wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar
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# Global.load_static_weights 参数需要设置为 False。
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# Global.save_inference_dir参数设置转换的模型将保存的地址。
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python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
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python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
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```
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转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.checkpoints`参数，其指向训练中保存的模型参数文件。
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转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.pretrained_model`参数，其指向训练中保存的模型参数文件。
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转换成功后，在模型保存目录下有三个文件：
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```
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inference/det_db/
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@ -67,7 +67,7 @@ inference/det_db/
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下载超轻量中文识别模型：
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```
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wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
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wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
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```
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识别模型转inference模型与检测的方式相同，如下：
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@ -78,7 +78,7 @@ wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar
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# Global.load_static_weights 参数需要设置为 False。
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# Global.save_inference_dir参数设置转换的模型将保存的地址。
|
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python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
|
||||
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
|
||||
```
|
||||
|
||||
**注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
|
||||
|
|
@ -96,7 +96,7 @@ python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_trai
|
|||
|
||||
下载方向分类模型：
|
||||
```
|
||||
wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
|
||||
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
|
||||
```
|
||||
|
||||
方向分类模型转inference模型与检测的方式相同，如下：
|
||||
|
|
@ -107,7 +107,7 @@ wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar
|
|||
# Global.load_static_weights 参数需要设置为 False。
|
||||
# Global.save_inference_dir参数设置转换的模型将保存的地址。
|
||||
|
||||
python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
|
||||
python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
|
||||
```
|
||||
|
||||
转换成功后，在目录下有三个文件：
|
||||
|
|
@ -152,10 +152,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di
|
|||
<a name="DB文本检测模型推理"></a>
|
||||
### 2. DB文本检测模型推理
|
||||
|
||||
首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换：
|
||||
首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar) )，可以使用如下命令进行转换：
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints=./det_r50_vd_db_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
|
||||
```
|
||||
|
||||
DB文本检测模型推理，可以执行如下命令：
|
||||
|
|
@ -173,10 +173,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
|
|||
<a name="EAST文本检测模型推理"></a>
|
||||
### 3. EAST文本检测模型推理
|
||||
|
||||
首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换：
|
||||
首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址 (coming soon)](link) )，可以使用如下命令进行转换：
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints=./det_r50_vd_east_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
|
||||
```
|
||||
|
||||
**EAST文本检测模型推理，需要设置参数`--det_algorithm="EAST"`**，可以执行如下命令：
|
||||
|
|
@ -194,9 +194,9 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
|
|||
<a name="SAST文本检测模型推理"></a>
|
||||
### 4. SAST文本检测模型推理
|
||||
#### (1). 四边形文本检测模型（ICDAR2015）
|
||||
首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](link))，可以使用如下命令进行转换：
|
||||
首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址(coming soon)](link))，可以使用如下命令进行转换：
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints=./det_r50_vd_sast_icdar15_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
|
||||
|
||||
```
|
||||
**SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`**，可以执行如下命令：
|
||||
|
|
@ -208,10 +208,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
|
|||
|
||||
|
||||
#### (2). 弯曲文本检测模型（Total-Text）
|
||||
首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换：
|
||||
首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址(coming soon)](link))，可以使用如下命令进行转换：
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints=./det_r50_vd_sast_totaltext_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -254,10 +254,10 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
|
|||
我们以 CRNN 为例，介绍基于CTC损失的识别模型推理。 Rosetta 使用方式类似，不用设置识别算法参数rec_algorithm。
|
||||
|
||||
首先将 Rosetta 文本识别训练过程中保存的模型，转换成inference model。以基于Resnet34_vd骨干网络，使用MJSynth和SynthText两个英文文本识别合成数据集训练
|
||||
的模型为例（[模型下载地址](link))，可以使用如下命令进行转换：
|
||||
的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) )，可以使用如下命令进行转换：
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.checkpoints=./rec_r34_vd_none_bilstm_ctc_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
|
||||
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -313,9 +313,9 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
|
|||
|
||||
执行命令后，上图的预测结果为：
|
||||
``` text
|
||||
2020-09-19 16:15:05,076-INFO: index: [205 206 38 39]
|
||||
2020-09-19 16:15:05,077-INFO: word : 바탕으로
|
||||
2020-09-19 16:15:05,077-INFO: score: 0.9171358942985535
|
||||
2020-09-19 16:15:05,076-INFO: index: [205 206 38 39]
|
||||
2020-09-19 16:15:05,077-INFO: word : 바탕으로
|
||||
2020-09-19 16:15:05,077-INFO: score: 0.9171358942985535
|
||||
```
|
||||
|
||||
<a name="方向分类模型推理"></a>
|
||||
|
|
@ -337,8 +337,7 @@ python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
|
|||
执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
|
||||
|
||||
```
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
result: ('0', 0.9998784)
|
||||
Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
|
||||
```
|
||||
|
||||
<a name="文本检测、方向分类和文字识别串联推理"></a>
|
||||
|
|
|
|||
|
|
@ -9,12 +9,12 @@
|
|||
|
||||
## 2.inference模型下载
|
||||
|
||||
* 移动端和服务器端的检测与识别模型如下，更多模型下载（包括多语言），可以参考[PP-OCR v1.1 系列模型下载](../doc_ch/models_list.md)
|
||||
* 移动端和服务器端的检测与识别模型如下，更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](../doc_ch/models_list.md)
|
||||
|
||||
| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 |
|
||||
| ------------ | --------------- | ----------------|---- | ---------- | -------- |
|
||||
| 中英文超轻量OCR模型（xM） | |移动端&服务器端|[推理模型](link) / [预训练模型](link)|[推理模型]({}) / [预训练模型]({}) |[推理模型]({}) / [预训练模型]({}) |
|
||||
| 中英文通用OCR模型（xM） | |服务器端 |[推理模型]({}) / [预训练模型]({}) |[推理模型]({}) / [预训练模型]({}) |[推理模型]({}) / [预训练模型]({}}) |
|
||||
| 中英文超轻量OCR模型（8.6M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) |
|
||||
| 中英文通用OCR模型（146.4M） | ch_ppocr_server_v2.0_xx |服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) |
|
||||
|
||||
|
||||
* windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
|
||||
|
|
@ -37,28 +37,29 @@ cd ..
|
|||
```
|
||||
mkdir inference && cd inference
|
||||
# 下载超轻量级中文OCR模型的检测模型并解压
|
||||
wget {} && tar xf ch_ppocr_mobile_v1.1_det_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
# 下载超轻量级中文OCR模型的识别模型并解压
|
||||
wget {} && tar xf ch_ppocr_mobile_v1.1_rec_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
|
||||
# 下载超轻量级中文OCR模型的文本方向分类器模型并解压
|
||||
wget {} && tar xf ch_ppocr_mobile_v1.1_cls_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
|
||||
cd ..
|
||||
```
|
||||
|
||||
解压完毕后应有如下文件结构：
|
||||
|
||||
```
|
||||
|-inference
|
||||
|-ch_ppocr_mobile_v1.1_det_infer
|
||||
|- model
|
||||
|- params
|
||||
|-ch_ppocr_mobile_v1.1_rec_infer
|
||||
|- model
|
||||
|- params
|
||||
|-ch_ppocr_mobile-v1.1_cls_infer
|
||||
|- model
|
||||
|- params
|
||||
...
|
||||
├── ch_ppocr_mobile_v2.0_cls_infer
|
||||
│ ├── inference.pdiparams
|
||||
│ ├── inference.pdiparams.info
|
||||
│ └── inference.pdmodel
|
||||
├── ch_ppocr_mobile_v2.0_det_infer
|
||||
│ ├── inference.pdiparams
|
||||
│ ├── inference.pdiparams.info
|
||||
│ └── inference.pdmodel
|
||||
├── ch_ppocr_mobile_v2.0_rec_infer
|
||||
├── inference.pdiparams
|
||||
├── inference.pdiparams.info
|
||||
└── inference.pdmodel
|
||||
```
|
||||
|
||||
## 3.单张图像或者图像集合预测
|
||||
|
|
@ -68,13 +69,13 @@ cd ..
|
|||
```bash
|
||||
|
||||
# 预测image_dir指定的单张图像
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
|
||||
# 预测image_dir指定的图像集合
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
|
||||
# 如果想使用CPU进行预测，需设置use_gpu参数为False
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
|
||||
```
|
||||
|
||||
- 通用中文OCR模型
|
||||
|
|
@ -83,7 +84,7 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_mode
|
|||
|
||||
```bash
|
||||
# 预测image_dir指定的单张图像
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_server_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v2.0_det_infer/" --rec_model_dir="./inference/ch_ppocr_server_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
```
|
||||
|
||||
* 注意：
|
||||
|
|
|
|||
|
|
@ -43,21 +43,21 @@ Next, we first introduce how to convert a trained model into an inference model,
|
|||
|
||||
Download the lightweight Chinese detection model:
|
||||
```
|
||||
wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
|
||||
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
|
||||
```
|
||||
|
||||
The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
|
||||
```
|
||||
# -c Set the training algorithm yml configuration file
|
||||
# -o Set optional parameters
|
||||
# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
|
||||
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
|
||||
# Global.load_static_weights needs to be set to False
|
||||
# Global.save_inference_dir Set the address where the converted model will be saved.
|
||||
|
||||
python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
|
||||
python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
|
||||
```
|
||||
|
||||
When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` parameter in the configuration file.
|
||||
When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.pretrained_model` parameter in the configuration file.
|
||||
After the conversion is successful, there are three files in the model save directory:
|
||||
```
|
||||
inference/det_db/
|
||||
|
|
@ -71,18 +71,18 @@ inference/det_db/
|
|||
|
||||
Download the lightweight Chinese recognition model:
|
||||
```
|
||||
wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
|
||||
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
|
||||
```
|
||||
|
||||
The recognition model is converted to the inference model in the same way as the detection, as follows:
|
||||
```
|
||||
# -c Set the training algorithm yml configuration file
|
||||
# -o Set optional parameters
|
||||
# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
|
||||
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
|
||||
# Global.load_static_weights needs to be set to False
|
||||
# Global.save_inference_dir Set the address where the converted model will be saved.
|
||||
|
||||
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
|
||||
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
|
||||
```
|
||||
|
||||
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
|
||||
|
|
@ -100,18 +100,18 @@ inference/det_db/
|
|||
|
||||
Download the angle classification model:
|
||||
```
|
||||
wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
|
||||
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
|
||||
```
|
||||
|
||||
The angle classification model is converted to the inference model in the same way as the detection, as follows:
|
||||
```
|
||||
# -c Set the training algorithm yml configuration file
|
||||
# -o Set optional parameters
|
||||
# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
|
||||
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
|
||||
# Global.load_static_weights needs to be set to False
|
||||
# Global.save_inference_dir Set the address where the converted model will be saved.
|
||||
|
||||
python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
|
||||
python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
|
||||
```
|
||||
|
||||
After the conversion is successful, there are two files in the directory:
|
||||
|
|
@ -158,10 +158,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di
|
|||
<a name="DB_DETECTION"></a>
|
||||
### 2. DB TEXT DETECTION MODEL INFERENCE
|
||||
|
||||
First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
|
||||
First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert:
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints=./det_r50_vd_db_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
|
||||
```
|
||||
|
||||
DB text detection model inference, you can execute the following command:
|
||||
|
|
@ -179,10 +179,10 @@ The visualized text detection results are saved to the `./inference_results` fol
|
|||
<a name="EAST_DETECTION"></a>
|
||||
### 3. EAST TEXT DETECTION MODEL INFERENCE
|
||||
|
||||
First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
|
||||
First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link (coming soon)](link)), you can use the following command to convert:
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints=./det_r50_vd_east_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
|
||||
```
|
||||
**For EAST text detection model inference, you need to set the parameter ``--det_algorithm="EAST"``**, run the following command:
|
||||
|
||||
|
|
@ -200,10 +200,10 @@ The visualized text detection results are saved to the `./inference_results` fol
|
|||
<a name="SAST_DETECTION"></a>
|
||||
### 4. SAST TEXT DETECTION MODEL INFERENCE
|
||||
#### (1). Quadrangle text detection model (ICDAR2015)
|
||||
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
|
||||
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link (coming soon)](link)), you can use the following command to convert:
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints=./det_r50_vd_sast_icdar15_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
|
||||
```
|
||||
|
||||
**For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command:
|
||||
|
|
@ -217,10 +217,10 @@ The visualized text detection results are saved to the `./inference_results` fol
|
|||
|
||||
|
||||
#### (2). Curved text detection model (Total-Text)
|
||||
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
|
||||
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link (coming soon)](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints=./det_r50_vd_sast_totaltext_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
|
||||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
|
||||
```
|
||||
|
||||
**For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command:
|
||||
|
|
@ -262,10 +262,10 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
|
|||
|
||||
Taking CRNN as an example, we introduce the recognition model inference based on CTC loss. Rosetta and Star-Net are used in a similar way, No need to set the recognition algorithm parameter rec_algorithm.
|
||||
|
||||
First, convert the model saved in the CRNN text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](link)). It can be converted as follow:
|
||||
First, convert the model saved in the CRNN text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)). It can be converted as follow:
|
||||
|
||||
```
|
||||
python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.checkpoints=./rec_r34_vd_none_bilstm_ctc_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
|
||||
python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
|
||||
```
|
||||
|
||||
For CRNN text recognition model inference, execute the following commands:
|
||||
|
|
@ -335,7 +335,7 @@ The following will introduce the angle classification model inference.
|
|||
For angle classification model inference, you can execute the following commands:
|
||||
|
||||
```
|
||||
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
|
||||
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="./inference/cls/"
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -343,8 +343,7 @@ python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
|
|||
After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
|
||||
|
||||
```
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
result: ('0', 0.9999995)
|
||||
Predicts of ./doc/imgs_words_en/word_10.png:['0', 0.9999995]
|
||||
```
|
||||
|
||||
<a name="CONCATENATION"></a>
|
||||
|
|
|
|||
|
|
@ -9,13 +9,13 @@ Please refer to [quick installation](./installation_en.md) to configure the Padd
|
|||
|
||||
## 2.inference models
|
||||
|
||||
The detection and recognition models on the mobile and server sides are as follows. For more models (including multiple languages), please refer to [PP-OCR v1.1 series model list](../doc_ch/models_list.md)
|
||||
The detection and recognition models on the mobile and server sides are as follows. For more models (including multiple languages), please refer to [PP-OCR v2.0 series model list](../doc_ch/models_list.md)
|
||||
|
||||
|
||||
| Model introduction | Model name | Recommended scene | Detection model | Direction Classifier | Recognition model |
|
||||
| Model introduction | Model name | Recommended scene | Detection model | Direction Classifier | Recognition model |
|
||||
| ------------ | --------------- | ----------------|---- | ---------- | -------- |
|
||||
| Ultra-lightweight Chinese OCR model（xM） | ch_ppocr_mobile_v1.1_xx |Mobile-side/Server-side|[inference model](link) / [pretrained model](link)|[inference model](link) / [pretrained model](link) |[inference model](link) / [pretrained model](link) |
|
||||
| Universal Chinese OCR model（xM） |ch_ppocr_server_v1.1_xx|Server-side |[inference model](link) / [pretrained model](link) |[inference model](link) / [pretrained model](link) |[inference model](link) / [pretrained model](link) |
|
||||
| Ultra-lightweight Chinese OCR model（8.6M） | ch_ppocr_mobile_v2.0_xx |Mobile-side/Server-side|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) |
|
||||
| Universal Chinese OCR model（146.4M） | ch_ppocr_server_v2.0_xx |Server-side |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) |
|
||||
|
||||
|
||||
* If `wget` is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, then uncompress it and place it in the corresponding directory.
|
||||
|
||||
|
|
@ -37,28 +37,29 @@ Take the ultra-lightweight model as an example:
|
|||
```
|
||||
mkdir inference && cd inference
|
||||
# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
wget link && tar xf ch_ppocr_mobile_v1.1_det_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
wget link && tar xf ch_ppocr_mobile_v1.1_rec_infer.tar
|
||||
# Download the direction classifier model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
wget link && tar xf ch_ppocr_mobile_v1.1_cls_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
|
||||
# Download the angle classifier model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
|
||||
cd ..
|
||||
```
|
||||
|
||||
After decompression, the file structure should be as follows:
|
||||
|
||||
```
|
||||
|-inference
|
||||
|-ch_ppocr_mobile_v1.1_det_infer
|
||||
|- model
|
||||
|- params
|
||||
|-ch_ppocr_mobile_v1.1_rec_infer
|
||||
|- model
|
||||
|- params
|
||||
|-ch_ppocr_mobile_v1.1_cls_infer
|
||||
|- model
|
||||
|- params
|
||||
...
|
||||
├── ch_ppocr_mobile_v2.0_cls_infer
|
||||
│ ├── inference.pdiparams
|
||||
│ ├── inference.pdiparams.info
|
||||
│ └── inference.pdmodel
|
||||
├── ch_ppocr_mobile_v2.0_det_infer
|
||||
│ ├── inference.pdiparams
|
||||
│ ├── inference.pdiparams.info
|
||||
│ └── inference.pdmodel
|
||||
├── ch_ppocr_mobile_v2.0_rec_infer
|
||||
├── inference.pdiparams
|
||||
├── inference.pdiparams.info
|
||||
└── inference.pdmodel
|
||||
```
|
||||
|
||||
## 3. Single image or image set prediction
|
||||
|
|
@ -70,13 +71,13 @@ After decompression, the file structure should be as follows:
|
|||
```bash
|
||||
|
||||
# Predict a single image specified by image_dir
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
|
||||
# Predict imageset specified by image_dir
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
|
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# If you want to use the CPU for prediction, you need to set the use_gpu parameter to False
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/" --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
|
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```
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- Universal Chinese OCR model
|
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|
|
@ -85,7 +86,7 @@ Please follow the above steps to download the corresponding models and update th
|
|||
|
||||
```
|
||||
# Predict a single image specified by image_dir
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v1.1_det_infer/" --rec_model_dir="./inference/ch_ppocr_server_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v2.0_det_infer/" --rec_model_dir="./inference/ch_ppocr_server_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
|
||||
```
|
||||
|
||||
* Note
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ from ppocr.modeling.architectures import build_model
|
|||
from ppocr.postprocess import build_post_process
|
||||
from ppocr.utils.save_load import init_model
|
||||
from ppocr.utils.logging import get_logger
|
||||
from tools.program import load_config, merge_config,ArgsParser
|
||||
from tools.program import load_config, merge_config, ArgsParser
|
||||
|
||||
|
||||
def main():
|
||||
|
|
@ -36,7 +36,6 @@ def main():
|
|||
config = load_config(FLAGS.config)
|
||||
merge_config(FLAGS.opt)
|
||||
logger = get_logger()
|
||||
print(config)
|
||||
# build post process
|
||||
|
||||
post_process_class = build_post_process(config['PostProcess'],
|
||||
|
|
|
|||
|
|
@ -113,7 +113,6 @@ def merge_config(config):
|
|||
global_config.keys(), sub_keys[0])
|
||||
cur = global_config[sub_keys[0]]
|
||||
for idx, sub_key in enumerate(sub_keys[1:]):
|
||||
assert (sub_key in cur)
|
||||
if idx == len(sub_keys) - 2:
|
||||
cur[sub_key] = value
|
||||
else:
|
||||
|
|
|
|||
Loading…
Reference in New Issue