Target of the V1.0.0

This version is the first time, we formal release the SNNF(Sunplus Neural Network Framework), the original version is V1.0.0.

There are some release notes for the version which are as below：

1. There are some NN modules and Samples.

   1. lightFace;

   2. age;

   3. lightFace + age;

   4. det_10g;

   5. det_10g + w600k_r50;

   6. humanAttr;

   7. yolov5s + humanAttr;

   8. vehicleAttr;

   9. yolov8s_detection + vehicleAttr;

   10. yolov8s_detection;

   11. yolov8s_obb;

   12. yolov8s_pose;

   13. yolov8s_segment;

   14. yolov8s_classify;

   15. yolov10s_detection;

   16. yolov8n_obb_opti;

   17. yolov8n_pose_opti;

   18. yolov8n_segment_opti;

   19. yolov8n_detection_opti;

   20. yolov8n_classify;

   21. RTMDet-s;

   22. Human tracking;

   23. Human falling detection;

   24. yolov5s_detection;

   25. OCRDet；

   26. OCRCls；

   27. OCRRec；

   28. OCRDet + OCRRec;

   29. OCRDet + OCRCls + OCRRec;

   30. yolov8n_CCPD;

   31. yolov8n_CCPD + OCRRec;

   32. yolov8n_CCPD + OCRCls + OCRRec.

2. A command mechanism for model list, you can get the all the available models，combination of models and applications and so on, which showed above.

3. Provide a sample demo for users to refer to, and they can directly run the sample app to verify the NN environment of C3V.

4. Parallel operations in multi-model applications can maximize the utilization of NPU and CPU, ultimately maximizing the processing frame rate of the application.

5. Implement official and customized partitions, you can copy an offical model to customized zone for further customizing, or just put your own model in the customized zone as a new model.

6. Define some specified paths for some resource, for example, config, font, test image, model file, test video and so on.

7. Define some specified folders such as botSortTrack, imageWriter, videoWriter for assist.

8. Provide Libs and header files for users to integrate specific NN modules into their applications using the standard interfaces we provide.

9. Provide COMPILE SH to enable users to easily compile SNNF and locate generated resources in the specified release directory.

10. Open source the SNNF to facilitate users to better understand the internal operations of SNNF.

11. Optimize the system to make it more robust, and after long-term testing, the system is stable.

Resource

Please get the V1.0.0 release resource here.

Before starting any work, please carefully read the instruction files such as readme.md in the document directory.

Usage of V1.0.0

How to verify Official Demos

You can use the script we provide to start as follows：

Release folder structure

Open

• bin: nnf_nnsample. Prebuild sample programs that can run on the c3v Linux platform.

• include：header file of NN framework SDK.

• lib：libraries of NN framework SDK.

• resource

  • config: some config files for features.

  • font: ttf file for plotting sample.

  • image: image files used for test.

  • model: models to be used in the sample program.

  • video: video files used for test.

• samples：example code for using NN framework.

• snnf_run.sh：executable script for running sample code.

• thirdparty: just as its name implies.

How to run NN framework sample

1. Copy the release foler to C3V Linux.

2. Run nnf_run.sh to run the NNF sample.

How to build SNNF

1. Cross-compile for C3V environment.

2. Copy release folder to the C3V platform.

3. Setup environment variable.

4. Then, you can run snnf_run.sh for SNNF sample.

Models reference