ctpn.pytorch
Pytorch implementation of CTPN (Detecting Text in Natural Image with Connectionist Text Proposal Network)
Paper
https://arxiv.org/pdf/1609.03605.pdf
环境准备
请使用apt-get install zip或yum install zip安装压缩工具zip
数据准备
请下载 icdar13 dataset并解压到${ROOT}/data/icdar13/，并将其中的gt.zip标签文件移动到${ROOT}/下
Training
# training 1p accuracy
bash ./test/train_full_1p.sh --data_path=real_data_path
# training 1p performance
bash ./test/train_performance_1p.sh --data_path=real_data_path
# training 8p accuracy
bash ./test/train_full_8p.sh --data_path=real_data_path
# training 8p performance
bash ./test/train_performance_8p.sh --data_path=real_data_path
Test
测试的权重采用最后一个epoch的权重文件，即当epoch=200时，权重路径为output_models/checkpoint-200.pth.tar
# test 8p accuracy
bash test/train_eval.sh --data_path=real_data_path --pth_path=output_models/checkpoint-200.pth.tar
测试精度包含三个部分，hmean为用于比对的精度
Calculated!{"precision": 0.7331386861313869, "recall": 0.7094063926940639, "hmean": 0.7210773213359865}
CTPN training result
| 名称 | 精度 | 性能 |
|---|---|---|
| NPU-8p | 72.1 | 17.66fps |
| GPU-8p | 72.4 | 13.25fps |
| NPU-1p | 1.695fps | |
| GPU-1p | 6.295fps |