This is the official code for MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild
| 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 |
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.
# 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 TrueThis 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!
