37 lines
2.1 KiB
Markdown
37 lines
2.1 KiB
Markdown
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# BENCHMARK
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This document gives the prediction time-consuming benchmark of PaddleOCR Ultra Lightweight Chinese Model (8.6M) on each platform.
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## TEST DATA
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* 500 images were randomly sampled from the Chinese public data set [ICDAR2017-RCTW](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#ICDAR2017-RCTW-17).
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Most of the pictures in the set were collected in the wild through mobile phone cameras.
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Some are screenshots.
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These pictures show various scenes, including street scenes, posters, menus, indoor scenes and screenshots of mobile applications.
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## MEASUREMENT
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The predicted time-consuming indicators on the four platforms are as follows:
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| Long size(px) | T4(s) | V100(s) | Intel Xeon 6148(s) | Snapdragon 855(s) |
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| :---------: | :-----: | :-------: | :------------------: | :-----------------: |
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| 960 | 0.092 | 0.057 | 0.319 | 0.354 |
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| 640 | 0.067 | 0.045 | 0.198 | 0.236 |
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| 480 | 0.057 | 0.043 | 0.151 | 0.175 |
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Explanation:
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* The evaluation time-consuming stage is the complete stage from image input to result output, including image
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pre-processing and post-processing.
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* ```Intel Xeon 6148``` is the server-side CPU model. Intel MKL-DNN is used in the test to accelerate the CPU prediction speed.
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To use this operation, you need to:
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* Update to the latest version of PaddlePaddle: https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev
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Please select the corresponding mkl version wheel package according to the CUDA version and Python version of your environment,
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for example, CUDA10, Python3.7 environment, you should:
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```
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# Obtain the installation package
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wget https://paddle-wheel.bj.bcebos.com/0.0.0-gpu-cuda10-cudnn7-mkl/paddlepaddle_gpu-0.0.0-cp37-cp37m-linux_x86_64.whl
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# Installation
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pip3.7 install paddlepaddle_gpu-0.0.0-cp37-cp37m-linux_x86_64.whl
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```
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* Use parameters ```--enable_mkldnn True``` to turn on the acceleration switch when making predictions
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* ```Snapdragon 855``` is a mobile processing platform model.
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