forked from mindspore-Ecosystem/mindspore
update README for mindspore lite
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@ -57,7 +57,6 @@
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* Add 93 TFLite op.
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* Add 24 Caffe op.
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* Add 62 ONNX op.
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* Add support for windows.
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* Add 11 optimized passes, include fusion/const fold.
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* Support aware-training and Post-training quantization.
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* CPU
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@ -54,3 +54,14 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
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Load the model and perform inference. [Inference](https://www.mindspore.cn/lite/tutorial/en/master/use/runtime.html) is the process of running input data through the model to get output.
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MindSpore provides a series of pre-trained models that can be deployed on mobile device [example](#TODO).
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## MindSpore Lite benchmark test result
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Base on MindSpore r0.7, we test a couple of networks on HUAWEI Mate30 (Hisilicon Kirin990) mobile phone, and get the test results below for your reference.
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| NetWork | Thread Number | Average Run Time(ms) |
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| ------------------- | ------------- | -------------------- |
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| basic_squeezenet | 4 | 9.10 |
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| inception_v3 | 4 | 69.361 |
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| mobilenet_v1_10_224 | 4 | 7.137 |
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| mobilenet_v2_10_224 | 4 | 5.569 |
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| resnet_v2_50 | 4 | 48.691 |
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@ -64,3 +64,14 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推
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主要完成模型推理工作,即加载模型,完成模型相关的所有计算。[推理](https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/runtime.html)是通过模型运行输入数据,获取预测的过程。
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MindSpore提供了一系列预训练模型部署在智能终端的[样例](#TODO)。
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## MindSpore Lite性能参考数据
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我们在HUAWEI Mate30(Hisilicon Kirin990)手机上,基于MindSpore r0.7,测试了一组端侧常见网络的性能数据,供您参考:
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| 网络 | 线程数 | 平均推理时间(毫秒) |
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| ------------------- | ------ | ------------------ |
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| basic_squeezenet | 4 | 9.10 |
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| inception_v3 | 4 | 69.361 |
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| mobilenet_v1_10_224 | 4 | 7.137 |
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| mobilenet_v2_10_224 | 4 | 5.569 |
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| resnet_v2_50 | 4 | 48.691 |
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