文字检测模型文字框一个都检测不出来，请问这是为什么？

[1wang11lijian1]

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：
• 版本号/Version：Paddle： PaddleOCR： 问题相关组件/Related components：
• 运行指令/Command Code：
• 完整报错/Complete Error Message： 使用PP-OCRv3模型进行文字检测训练，输入图片shape为（1624，1240，3），文字检测模型测试准确率都为0，文字框一个都检测不出来，请问是不是模型输入尺寸和图片大小没匹配上，或者是为什么文字框一个都检测不出来，但是文字识别模型是有效果的。问题#9402 也是我提的问题并没有解决我的疑问

下面是文字检测的训练代码 训练的代码（加载了PP-OCRv3检测模型的权重） python tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Global.pretrain_weights=./pretrain_models/en_PP-OCRv3_det_distill_train/

以下是训练的ch_PP-OCRv3_det_cml.yml文件 Global: debug: false use_gpu: true epoch_num: 500 log_smooth_window: 20 print_batch_step: 10 save_model_dir: ./output/en_PP-OCR_v3_det/ save_epoch_step: 100 eval_batch_step: 0 400 cal_metric_during_train: false pretrained_model: null checkpoints: null save_inference_dir: null use_visualdl: false infer_img: null save_res_path: ./output/det/predicts_ppocrv3_en.txt distributed: true Architecture: name: DistillationModel algorithm: Distillation model_type: det Models: Student: pretrained: model_type: det algorithm: DB Transform: null Backbone: name: MobileNetV3 scale: 0.5 model_name: large disable_se: true Neck: name: RSEFPN out_channels: 96 shortcut: True Head: name: DBHead k: 50 Student2: pretrained: model_type: det algorithm: DB Transform: null Backbone: name: MobileNetV3 scale: 0.5 model_name: large disable_se: true Neck: name: RSEFPN out_channels: 96 shortcut: True Head: name: DBHead k: 50 Teacher: pretrained: freeze_params: true return_all_feats: false model_type: det algorithm: DB Backbone: name: ResNet_vd in_channels: 3 layers: 50 Neck: name: LKPAN out_channels: 256 Head: name: DBHead kernel_list: [7,2,2] k: 50

Loss: name: CombinedLoss loss_config_list:

DistillationDilaDBLoss: weight: 1.0 model_name_pairs: ["Student", "Teacher"] ["Student2", "Teacher"] key: maps balance_loss: true main_loss_type: DiceLoss alpha: 5 beta: 10 ohem_ratio: 3 DistillationDMLLoss: model_name_pairs: ["Student", "Student2"] maps_name: "thrink_maps" weight: 1.0 model_name_pairs: ["Student", "Student2"] key: maps DistillationDBLoss: weight: 1.0 model_name_list: ["Student", "Student2"] 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 warmup_epoch: 2 regularizer: name: L2 factor: 5.0e-05

PostProcess: name: DistillationDBPostProcess model_name: ["Student"] key: head_out thresh: 0.3 box_thresh: 0.6 max_candidates: 1000 unclip_ratio: 1.5

Metric: name: DistillationMetric base_metric_name: DetMetric main_indicator: hmean key: "Student"

Train: dataset: name: SimpleDataSet data_dir: ./train_data/icdar2015/text_localization/icdar_c4_train_imgs/ label_file_list:

• ./train_data/icdar2015/text_localization/train_icdar2015_label.txt ratio_list: [1.0] transforms:
• DecodeImage: img_mode: BGR channel_first: false
• DetLabelEncode: null
• CopyPaste:
• IaaAugment: augmenter_args:
• type: Fliplr args: p: 0.5
• type: Affine args: rotate:
• -10
• 10
• type: Resize args: size:
• 0.5
• 3
• EastRandomCropData: size:
• 960
• 960 max_tries: 50 keep_ratio: true
• MakeBorderMap: shrink_ratio: 0.4 thresh_min: 0.3 thresh_max: 0.7
• MakeShrinkMap: shrink_ratio: 0.4 min_text_size: 8
• 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: 2 Eval: dataset: name: SimpleDataSet data_dir: ./train_data/icdar2015/text_localization/ch4_test_images/ label_file_list:
• ./train_data/icdar2015/text_localization/test_icdar2015_label.txt transforms:
• DecodeImage: # load image img_mode: BGR channel_first: False
• DetLabelEncode: # Class handling label
• DetResizeForTest:
• NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc'
• ToCHWImage:
• KeepKeys: keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] loader: shuffle: False drop_last: False batch_size_per_card: 1 # must be 1 num_workers: 2

[LDOUBLEV]

python tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml -o Global.checkpoints=./pretrain_models/en_PP-OCRv3_det_distill_train/ 这样试试

[1wang11lijian1]

@LDOUBLEV 好兄弟这样改完真的有效果了，非常感谢！但是我有个问题因为本身导入的模型效果就已经很好了，我这边自己训练的究竟提升了多少的效果，有没有可能丝毫没有提升呢？再就是之前导入预训练的模型一直训练不出结果是不是也有可能是这个原因。

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