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
MSRA-TD500	90.7	88.1	89.4	model
ICDAR-ArT	83.0	76.7	79.7	model
CTW1500	91.4	88.3	89.8	model

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++.
We use code from Connected_components_PyTorch as post-processing.
Thanks for their great work!