Introduction

Synet is a small framework to inference neural network on CPU. Synet uses models previously trained by other deep neural network frameworks.

The main advantages of Synet are:

• Synet is faster then most other DNN original frameworks (has great single thread CPU performance).
• Synet is header only, small C++ library.
• Synet has only one external dependence - Simd Library.

Building of test applications for Linux

To build test applications you can run following bash script:

git clone -b master --recurse-submodules -v https://github.com/ermig1979/Synet.git clone
cd clone
./build.sh

And applications test_darknet, test_inference_engine, test_onnx, test_precision, test_quantization, use_face_detection will be created in directory build. There is a detail description of these test applications below.

Darknet test application

The test application test_darknet is used for Darknet to Synet model conversion:

./build/test_darknet -m convert -fm darknet_model.cfg -fw darknet_weigths.dat -sm synet_model.xml -sw synet_weigths.bin

Also it is used in order to compare performance and accuracy of Darknet and Synet frameworks. There are several test scripts:

• For manual testing you can use ./test.sh (in the file you have to manually uncomment unit test that you need).
• Script ./check.sh checks correctness of all tests.
• Script ./perf.sh measures performance of Synet compare to Darknet.

OpenVINO test application

The test application test_inference_engine is used for OpenVINO to Synet model conversion:

/build/test_inference_engine -m convert -fm ie_model.xml -fw ie_weigths.bin -sm synet_model.xml -sw synet_weigths.bin

Also it is used in order to compare performance and accuracy of OpenVINO and Synet frameworks. There are several test scripts:

• For manual testing you can use ./test.sh (in the file you have to manually uncomment unit test that you need).
• Script ./check.sh checks correctness of all tests.
• Script ./perf.sh measures performance of Synet compare to OpenVINO.

ONNX test application

The test application test_onnx is used for ONNX to Synet model conversion:

/build/test_onnx -m convert -fw onnx_model.onnx -sm synet_model.xml -sw synet_weigths.bin

Also it is used in order to compare performance and accuracy of OpenVINO (it is used to infer ONNX models) and Synet frameworks. There are several test scripts:

• For manual testing you can use ./test.sh (in the file you have to manually uncomment unit test that you need).
• Script ./check.sh checks correctness of all tests.
• Script ./perf.sh measures performance of Synet compare to OpenVINO.

Precision test application

The precision test application test_precision is used for independent accuracy testing of quantized Synet and OpenVINO models. There is ./prec.sh test script (in the file you have to manually uncomment unit test that you need).

Quantization test application

The quantization test application test_quantization is used for INT8 quantization of FP32 Synet models and testing of them. There is ./quant.sh test script (in the file you have to manually uncomment unit test that you need).

Using samples

The application use_face_detection is an example of using of Synet framework to face detection.