Global: use_gpu: true epoch_num: 600 log_smooth_window: 20 print_batch_step: 10 save_model_dir: ./output/det_ct/ save_epoch_step: 10 # evaluation is run every 2000 iterations eval_batch_step: [0,1000] cal_metric_during_train: False pretrained_model: ./pretrain_models/ResNet18_vd_pretrained.pdparams checkpoints: save_inference_dir: use_visualdl: False infer_img: doc/imgs_en/img623.jpg save_res_path: ./output/det_ct/predicts_ct.txt Architecture: model_type: det algorithm: CT Transform: Backbone: name: ResNet_vd layers: 18 Neck: name: CTFPN Head: name: CT_Head in_channels: 512 hidden_dim: 128 num_classes: 3 Loss: name: CTLoss Optimizer: name: Adam lr: #PolynomialDecay name: Linear learning_rate: 0.001 end_lr: 0. epochs: 600 step_each_epoch: 1254 power: 0.9 PostProcess: name: CTPostProcess box_type: poly Metric: name: CTMetric main_indicator: f_score Train: dataset: name: SimpleDataSet data_dir: ./train_data/total_text/train label_file_list: - ./train_data/total_text/train/train.txt ratio_list: [1.0] transforms: - DecodeImage: img_mode: RGB channel_first: False - CTLabelEncode: # Class handling label - RandomScale: - MakeShrink: - GroupRandomHorizontalFlip: - GroupRandomRotate: - GroupRandomCropPadding: - MakeCentripetalShift: - ColorJitter: brightness: 0.125 saturation: 0.5 - ToCHWImage: - NormalizeImage: - KeepKeys: keep_keys: ['image', 'gt_kernel', 'training_mask', 'gt_instance', 'gt_kernel_instance', 'training_mask_distance', 'gt_distance'] # the order of the dataloader list loader: shuffle: True drop_last: True batch_size_per_card: 4 num_workers: 8 Eval: dataset: name: SimpleDataSet data_dir: ./train_data/total_text/test label_file_list: - ./train_data/total_text/test/test.txt ratio_list: [1.0] transforms: - DecodeImage: img_mode: RGB channel_first: False - CTLabelEncode: # Class handling label - ScaleAlignedShort: - NormalizeImage: order: 'hwc' - ToCHWImage: - KeepKeys: keep_keys: ['image', 'shape', 'polys', 'texts'] # the order of the dataloader list loader: shuffle: False drop_last: False batch_size_per_card: 1 num_workers: 2