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：

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.
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.
Provide a sample demo for users to refer to, and they can directly run the sample app to verify the NN environment of C3V.
Parallel operations in multi-model applications can maximize the utilization of NPU and CPU, ultimately maximizing the processing frame rate of the application.
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.
Define some specified paths for some resource, for example, config, font, test image, model file, test video and so on.
Define some specified folders such as botSortTrack, imageWriter, videoWriter for assist.
Provide Libs and header files for users to integrate specific NN modules into their applications using the standard interfaces we provide.
Provide COMPILE SH to enable users to easily compile SNNF and locate generated resources in the specified release directory.
Open source the SNNF to facilitate users to better understand the internal operations of SNNF.
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：

/SNNF/release # ./snnf_run.sh
Usage: ./bin/snnf_nnsample [-m|-s|-a|-h] [-i|-v|option]
        Version: 1.0.0_
        Time:
        [-m,--model <model>] run a single model
                <model>:Age                        Det10g                     HumanAttr
                        LightFace                  OcrCls                     OcrDet
                        OcrRec                     VehicleAttr                W600kR50
                        Yolov5sDetection           Yolov5sV1                  Yolov5sV2
                        Yolov8nClassify            Yolov8sClassify            Yolov5sV3
                        BotSortTrackStgcn          Rtmdets                    YoloV10sDetection
                        YoloV8nCcpd                YoloV8nDetectionBaseOpti   YoloV8nDetectionOpti
                        YoloV8nObbOpti             YoloV8nPoseOpti            YoloV8nSegmentOpti
                        YoloV8sDetection           YoloV8sDetectionBaseOpti   YoloV8sDetectionOpti
                        YoloV8sObb                 YoloV8sPose                YoloV8sSegment

                example:./bin/snnf_nnsample -m Yolov5sDetection
                        ./bin/snnf_nnsample --model HumanAttr

        [-s,--sequential <model1,model2,...>] run sequential models
                <models>:Yolov5sDetection,HumanAttr
                        LightFace,Age
                        OcrDet,OcrRec
                        OcrDet,OcrCls,OcrRec
                        YoloV8nCcpd,OcrRec
                        Det10g,W600kR50
                        YoloV8sDetection,VehicleAttr
                        YoloV8nDetectionOpti,BotSortTrack
                        YoloV8nPoseOpti,BotSortTrackStgcn
                        YoloV8nCcpd,OcrRec
                        YoloV8nCcpd,OcrCls,OcrRec

                example:./bin/snnf_nnsample -s Yolov5s,HumanAttr
                        ./bin/snnf_nnsample --sequential ocrDet,ocrCls,ocrRec
                        ./bin/snnf_nnsample -s YoloV8nCcpd,OcrRec,imageWriter
                        ./bin/snnf_nnsample -s YoloV8nDetectionOpti,BotSortTrack,videoWriter -v resource/video/humanCount.mp4
                        ./bin/snnf_nnsample -s YoloV8nPoseOpti,BotSortTrackStgcn,videoWriter -v resource/video/person-falling.mp4

        [-i,--image file] set image file to nn detection.
                <file>: file name
                [-c | option]: test count, this parameter is only match with -i

                example:./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr -i filename -c testCount
                        ./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr --image filename -c testCount

        [-v,--video file] set video file to nn detection.
                <file>: file name

                example:./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr -v filename
                        ./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr --video filename

        [-a,--all] run all model testing

        assist tools: imageWriter videoWriter BotSortTrack

Release folder structure

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

Copy the release foler to C3V Linux.

/SNNF/release # ls -alh
total 36
drwxr-xr-x  8 xxx B400 4096 Sep 30 14:23 ./
drwxr-xr-x 15 xxx B400 4096 Sep 30 14:15 ../
drwxr-xr-x  2 xxx B400 4096 Sep 30 14:15 bin/
drwxr-xr-x  6 xxx B400 4096 Sep 30 14:15 include/
drwxr-xr-x  3 xxx B400 4096 Sep 30 14:15 lib/
drwxr-xr-x  7 xxx B400 4096 Sep 30 14:15 resource/
drwxr-xr-x  4 xxx B400 4096 Sep 30 14:15 samples/
-rwxr-xr-x  1 xxx B400  262 Sep 30 14:15 snnf_run.sh*
drwxr-xr-x  7 xxx B400 4096 Sep 30 14:15 thirdparty/

Run nnf_run.sh to run the NNF sample.

a. One-time input

./snnf_run.sh -m YoloV8sDetection

#./snnf_run.sh -m YoloV8sDetection
1727625905242|7fad06c020|T|common: [app]YoloV8sDetection in
1727625905261|7fad06c020|I|common: [nn]create model from pluginName: YoloV8sDetection takes: 17
1727625905722|7f96fdf0e0|I|common: [nn]picked: 5
1727625905722|7f96fdf0e0|T|common: [app]GeneralModelOutputListener detect from resource/image/objectDetect.jpg, the result: (box:   35  196  158  403) --> label: 0(person), confidence: 0.89, fin: false
1727625905722|7f96fdf0e0|T|common: [app]GeneralModelOutputListener detect from resource/image/objectDetect.jpg, the result: (box:  526  185  112  392) --> label: 0(person), confidence: 0.87, fin: false
1727625905722|7f96fdf0e0|T|common: [app]GeneralModelOutputListener detect from resource/image/objectDetect.jpg, the result: (box:  173  203   97  360) --> label: 0(person), confidence: 0.86, fin: false
1727625905722|7f96fdf0e0|T|common: [app]GeneralModelOutputListener detect from resource/image/objectDetect.jpg, the result: (box:    0  321   44  253) --> label: 0(person), confidence: 0.75, fin: false
1727625905722|7f96fdf0e0|T|common: [app]GeneralModelOutputListener detect from resource/image/objectDetect.jpg, the result: (box:   12   66  625  403) --> label: 5(bus), confidence: 0.74, fin: true
1727625905781|7fad06c020|T|common: [app]YoloV8sDetection out, retVal: 0

b. Read input from the image file

./snnf_run.sh -m YoloV8nDetectionOpti -i resource/image/person640x640.jpg

c. Read inputs from the video file.

./snnf_run.sh -m YoloV8nDetectionOpti -v resource/video/humanCount.mp4

d. Sequential models

./snnf_run.sh -s Yolov5sDetection,HumanAttr

e. Model inference results save to image.

./snnf_run.sh -s YoloV8sPose,imageWriter

Results will save to the image detected_1883_0931_1727627888712.jpg.

How to build SNNF

Cross-compile for C3V environment.

a. Please use snnf_build.sh for SNNF compiling.

b. All the resource will installed to release folder.

Copy release folder to the C3V platform.
Setup environment variable.

a. Setting environment variables independently.

b. Run snnf_run.sh will auto set environment variables.

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

Models reference

Model Name	Version or Path
Yolov5s	https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt
Human Attributes	https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.zip
Light Face	Ultra-Light-Fast-Generic-Face-Detector-1MB/models/onnx/version-slim-320.onnx at master · Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB
Optical character recognition	ch_PP-OCRv3_det_infer ch_ppocr_mobile_v2.0_cls_infer ch_PP-OCRv3_rec_infer
Age recognition	GitCode - 全球开发者的开源社区,开源代码托管平台 7c024d9d453c9b35a72a984d8821b5832ef17401
Yolov8 Detection	GitHub - ultralytics/ultralytics: Ultralytics YOLO11 🚀
Yolov8 Pose	GitHub - ultralytics/ultralytics: Ultralytics YOLO11 🚀
Yolov8 OBB	GitHub - ultralytics/ultralytics: Ultralytics YOLO11 🚀
Yolov8 Segmentation	GitHub - ultralytics/ultralytics: Ultralytics YOLO11 🚀
Yolov8 Classification	GitHub - ultralytics/ultralytics: Ultralytics YOLO11 🚀
Vehicle attributes	https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip
License plate recognition	CCPD2020 Yolov8 CCPD detection OCR
Yolov10 Detection	https://github.com/THU-MIG/yolov10
RTMDet	https://github.com/open-mmlab/mmdetection
Face Recognition	https://github.com/deepinsight/insightface
Object tracking	BotSort
Falling Recognition	STGCN

C3V Documentation

V1.0.0 of SNNF