@ -41,7 +41,7 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
2. Model converter and optimization
If you use MindSpore or a third-party model, you need to use [MindSpore Lite Model Converter Tool](https://www.mindspore.cn/tutorial/lite/en/master/use/convert_model.html) to convert the model into MindSpore Lite model. The MindSpore Lite model converter tool provides the converter of TensorFlow Lite, Caffe, ONNX to MindSpore Lite model, fusion and quantization could be introduced during convert procedure.
If you use MindSpore or a third-party model, you need to use [MindSpore Lite Model Converter Tool](https://www.mindspore.cn/tutorial/lite/en/master/use/converter_tool.html) to convert the model into MindSpore Lite model. The MindSpore Lite model converter tool provides the converter of TensorFlow Lite, Caffe, ONNX to MindSpore Lite model, fusion and quantization could be introduced during convert procedure.
MindSpore also provides a tool to convert models running on IoT devices .
@ -56,6 +56,7 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
MindSpore provides pre-trained model that can be deployed on mobile device [example](https://www.mindspore.cn/lite/examples/en).
## MindSpore Lite benchmark test result
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.
| NetWork | Thread Number | Average Run Time(ms) |