MixNet

This is the official code for MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild

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Evaluation Result on Benchmark

|Datasets | Prec. (%)| Recall (%) | F1-score (%) | weight (.pth) |-----|--------|--------------|----------|-------------------| |Total-Text|93.0|88.1|90.5|model/Google| |MSRA-TD500|90.7|88.1|89.4|model/Google| |ICDAR-ArT|83.0|76.7|79.7|model/Google| |CTW1500 |91.4|88.3|89.8|model/Google|

Evaluation Result on CTW1500

This section elucidates the performance evaluation on the CTW1500 dataset.

When utilizing the TIoU-metric-python3 scoring code, our model's scores are as presented below: |Datasets | Prec. (%)| Recall (%) | F1-score (%) | |-----|--------|--------------|----------| |CTW1500 |90.3|84.8|87.5|

However, upon inputting MixNet's output into the DPText-DETR's calculation program, the ensuing results differ: |Datasets | Prec. (%)| Recall (%) | F1-score (%) | |-----|--------|--------------|----------| |CTW1500 |91.4|88.3|89.8|

I'm not sure why the data is inconsistent. Therefore, I've provided the scores obtained from both calculations for reference.

Eval

  # Total-Text
  python3 eval_mixNet.py --net FSNet_M --scale 1 --exp_name Totaltext_mid --checkepoch 622 --test_size 640 1024 --dis_threshold 0.3 --cls_threshold 0.85 --mid True
  # CTW1500
  python3 eval_mixNet.py --net FSNet_hor --scale 1 --exp_name Ctw1500 --checkepoch 925 --test_size 640 1024 --dis_threshold 0.3 --cls_threshold 0.85
  # MSRA-TD500
  python3 eval_mixNet.py --net FSNet_M --scale 1 --exp_name TD500HUST_mid --checkepoch 284 --test_size 640 1024 --dis_threshold 0.3 --cls_threshold 0.85 --mid True
  # ArT
  python3 eval_mixNet.py --net FSNet_M --scale 1 --exp_name ArT_mid --checkepoch 160 --test_size 960 2880 --dis_threshold 0.4 --cls_threshold 0.8 --mid True

Acknowledgement

This code has been modified based on the foundation laid by TextBPN++. <br> We use code from Connected_components_PyTorch as post-processing. <br> Thanks for their great work! <br>