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About the testing time comsumption #49
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I guess there should be two reasons,
If you want to confirm the first point, you can run our TF code on a single NVIDIA TITAN Xp GPU with different beam widths. |
Thanks. I found the slow test speed is caused by the first test sample. It took a long time to recognize the first sample, and the following samples will become faster. I ignore the test time of the first sample, and the average test time is 48ms (with beam width 1 and max_len 25). Although it is still larger than 20ms, it is acceptable. |
Hello, can the training and test code run through git bash, but can't run on the windows software platform? Thank you very much. |
Sorry, it only runs on Linux. |
请问您知道如何在测试时候输出矫正图像吗? |
According to the original paper, the recognition time for each image is 20ms. I modified demo.py, let it recognize all the images on the ICDAR2003 test set, and computed the average recognition time.
The following is my modification:
and the output is
average time = 168.360ms
which is larger than 20ms. I did the recognition on a single NVIDIA Tesla V100 GPU. I don't think using PyTorch will cause so much extra time consumption (I even ignore the time of reading image and preprocessing). So I wonder what is the problem.
Thanks very much.
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