...

This is the formal release V1.1.0

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

Table of Contents

style	none

Target of the V1.1.0

On the basis of 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：

...

, v1.1.0 mainly has the following updates：

Add some NN module.
- genderage: gender and age
  - Det_10g + genderage
  - LightFace + genderage
Based on the current main framework of SNNF, provide a porting tool so that users can easily add a new model to the Customized zone using the official generated nbg code as the source. The initial version of this tool is currently available, and there will be further updates in the next version.
Fix some known issues, such as:
- OCR have a probability of crashing.
- HumanAttr have a probability of crashing.
- When multiple models are connected in series, if the last model comes out, the result may have an unfinished flag fin=false.
Standardize post-process code.
Organize samples and unittest codes:
- samples is a sample code for snnf command sample which is just for snnf_run.sh.
- unittest/modelTest is just for a buildin model's test sample.
- unittest/pluginTest is just for the plugin model's test sample.
- unittest/sequntialTest is the sample for the pipeline in which several models are sequentially linked together in series.
Add NN filter, which can facilitate users to do their own private data processing at the application layer. Users only need to insert the filter directly into the corresponding position of the pipeline when creating the model flow, and then perform private and correct processing at the most appropriate location. The advantage of this is that there is no need to write a dedicated module within SNNF for this private processing, only the official interface of SNNF needs to be called. Of course, SNNF provides some commonly used private processing filters internally, such as label filtering. For example, when Object Detect+HumanAttr, the label results of Object Detect can be filtered, and only the person can be passed to HumanAttr. If the requirements of the application layer happen to match this usage, there is no need to implement it yourself at the application layer. Simply call the methods provided by SNNF.
Provide users with a mAP test sample to compare the precision of the C3V platform's model running results with the precision of the model's official PC running results. The default test model is yolov8s object detection. Users can refer to this mAP sample to rewrite the model they need to test mAP values for. Please refer the SOP.
Improve the toString() function for each model's result, and support the detection result toString JSON format.
Update some make config.
- In addition to cross compilation on the server, we also provide compilation methods on the C3V platform.
  - If it is cross compilation on the server, please confirm the config CROSS_COMPILE := ON in makefile_config.mk .
  - If compilation is required on the C3V platform, please confirm the config CROSS_COMPILE := OFF in makefile_config.mk .
- Users can choose the model plugin in makefile_config.mk that needs to be compiled according to their own needs, which can effectively reduce the code size of specific projects and shorten compilation time.

Code Block

language	plain

# ------------------------------------------------------------------------
# define which PLUGIN to build
  NN_PLUGIN_ENABLE_YOLOV5S_V2         <model>:Age        := yes

  NN_PLUGIN_ENABLE_RTMDETS           Det10g         := no
  NN_PLUGIN_ENABLE_GEDERAGE        HumanAttr           := no
  NN_PLUGIN_ENABLE_YOLOV8S_DETECTION          LightFace:= no
  NN_PLUGIN_ENABLE_YOLOV8S_OBB              OcrCls  := no
  NN_PLUGIN_ENABLE_YOLOV8S_POSE               OcrDet:= no
  NN_PLUGIN_ENABLE_YOLOV8S_SEGMENT            := no
    NN_PLUGIN_ENABLE_YOLOV10S_DETECTION   OcrRec      := no

  NN_PLUGIN_ENABLE_YOLOV8N_CCPD          VehicleAttr     := no
  NN_PLUGIN_ENABLE_YOLOV8N_OBB_OPTI       W600kR50    := no
  NN_PLUGIN_ENABLE_YOLOV8N_POSE_OPTI          := no
  NN_PLUGIN_ENABLE_YOLOV8N_SEGMENT_OPTI   Yolov5sDetection    := no
     Yolov5sV1NN_PLUGIN_ENABLE_YOLOV8N_DETECTION_OPTI     := no
  NN_PLUGIN_ENABLE_YOLOV8S_DETECTION_OPTI     := no

 Yolov5sV2
 NN_PLUGIN_ENABLE_BOTSORT_TRACK                        Yolov8nClassify            Yolov8sClassify:= no

Adjust the access of certain model header files and provide more necessary header files for the application layer to use.
Change the bbox value type from uint to float. Although this change does not have a substantial impact on the rendering of the results, it can make the precision value more accurate when calculating mAP.
In previous versions, assist modules such as imageWriter/videoWriter/JsonWriter only supported the default filename. Starting from this version, we will support filename config.
Also provide release folder so that new users can easily preview or evaluate our SNNF demo without spending too much time on environment setup and code compilation.

Resource

Please get the V1.1.0 release resource here.

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

Usage of V1.1.0

How to verify Official Demos

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

Code Block

/SNNF/release # ./snnf_run.sh
Usage: ./bin/snnf_nnsample [-m|-s|-a|-h] [-i|-v|-o|option]
        Version: 1.1.0_
        Time:
        [-m,--model <model>] run a single model
                <model>:Age                        Det10g                     HumanAttr
                        HumanFilter                LightFace                  OcrCls
                        OcrDet                     OcrRec                     VehicleAttr
           Yolov5sV3             VehicleFilter            BotSortTrackStgcn  W600kR50        Rtmdets           YoloV8sOdMap
        YoloV10sDetection                Yolov5sDetection         YoloV8nCcpd  Yolov5sV1               YoloV8nDetectionBaseOpti   YoloV8nDetectionOptiYolov8nClassify
                        YoloV8nObbOptiYolov8sClassify            stgcn YoloV8nPoseOpti            YoloV8nSegmentOpti         Yolov5sV2
               YoloV8sDetection         BotSortTrackStgcn  YoloV8sDetectionBaseOpti   YoloV8sDetectionOpti     GenderAge                  Rtmdets
 YoloV8sObb                 YoloV8sPose      YoloV10sDetection          YoloV8sSegmentYoloV8nCcpdOpti            YoloV8nDetectionBaseOpti
     example:./bin/snnf_nnsample -m Yolov5sDetection                 YoloV8nDetectionOpti       YoloV8nObbOpti ./bin/snnf_nnsample --model HumanAttr          [-s,--sequential <model1,model2,...>] run sequential modelsYoloV8nPoseOpti
                      <models>:Yolov5sDetection,HumanAttr  YoloV8nSegmentOpti         YoloV8sDetection           YoloV8sDetectionBaseOpti
  LightFace,Age                      YoloV8sDetectionOpti   OcrDet,OcrRec    YoloV8sObb                 YoloV8sPose
   OcrDet,OcrCls,OcrRec                     YoloV8sSegment
   YoloV8nCcpd,OcrRec             example:./bin/snnf_nnsample -m Yolov5sDetection
         Det10g,W600kR50               ./bin/snnf_nnsample --model HumanAttr

      YoloV8sDetection,VehicleAttr  [-s,--sequential <model1,model2,...>] run sequential models
                  YoloV8nDetectionOpti,BotSortTrack<models>:Yolov5sDetection,HumanFilter,HumanAttr
                        YoloV8nPoseOptiLightFace,BotSortTrackStgcnAge
                        YoloV8nCcpdOcrDet,OcrRec
                        YoloV8nCcpdOcrDet,OcrCls,OcrRec
                 example:./bin/snnf_nnsample -s Yolov5s,HumanAttr                         ./bin/snnf_nnsample --sequential ocrDet,ocrCls,ocrRec
YoloV8nCcpdOpti,OcrRec
                       ./bin/snnf_nnsample -s YoloV8nCcpd,OcrRec,imageWriter Det10g,W600kR50
                        ./bin/snnf_nnsample -s YoloV8nDetectionOpti,BotSortTrack,videoWriter -v resource/video/humanCount.mp4YoloV8sDetection,VehicleFilter,VehicleAttr
                        YoloV8nDetectionOpti,BotSortTrack
   ./bin/snnf_nnsample -s YoloV8nPoseOpti,BotSortTrackStgcn,videoWriter -v resource/video/person-falling.mp4          [-i,--image file] set image file to nn detection.       YoloV8nPoseOpti,BotSortTrackStgcn
                  <file>: file name    YoloV8nCcpdOpti,OcrRec
            [-c | option]: test count, this parameter is only match with -i YoloV8nCcpdOpti,OcrCls,OcrRec
                example:./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr -i filename -c testCountHumanFilter,HumanAttr
                        ./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr --image filename -c testCount

 --sequential ocrDet,ocrCls,ocrRec
       [-v,--video file] set video file to nn detection.          ./bin/snnf_nnsample -s YoloV8nCcpdOpti,OcrRec,imageWriter
    <file>: file name                  example:./bin/snnf_nnsample -s Yolov5sDetectionYoloV8sDetectionOpti,BotSortTrack,HumanAttrvideoWriter -v filenameresource/video/pk-12.mp4
                        ./bin/snnf_nnsample -s Yolov5sDetectionYoloV8nPoseOpti,BotSortTrackStgcn,HumanAttrvideoWriter --video filenamev resource/video/person-falling.mp4

         [-ai,--allimage file] runset image allfile modelto testingnn detection.
        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.

Code Block

/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

Code Block

#./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

Code Block

# ./snnf_run.sh -m YoloV8nDetectionOpti -i resource/image/person640x640.jpg
1727626580087|7fbd7de020|T|common: [app]YoloV8nDetectionOpti in
1727626582152|7fbd7de020|I|common: [nn]create model from pluginName: YoloV8nDetectionOpti takes: 2063
1727626582574|7fa569a0e0|T|common: [app]GeneralModelOutputListener detect from resource/image/person640x640.jpg, the result: (box:    0   19  614  619) --> label: 0(person), confidence: 0.87, fin: true
1727626582622|7fbd7de020|T|common: [app]YoloV8nDetectionOpti out, retVal: 0

c. Read inputs from the video file.

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

Code Block

# ./snnf_run.sh -m YoloV8nDetectionOpti -v resource/video/humanCount.mp4
1727626644381|7faf81e020|T|common: [app]streaming in
1727626645952|7faf81e020|I|common: [nn]create model from pluginName: YoloV8nDetectionOpti takes: 1388
1727626646044|7f951d80e0|T|common: [app]streaming test: runner func in
1727626646304|7f959e80e0|T|common: [app]GeneralModelOutputListener detect from , the result: (box:  671  129  307  873) --> label: 0(person), confidence: 0.90, fin: false
1727626646305|7f959e80e0|T|common: [app]GeneralModelOutputListener detect from , the result: (box:    0  364  268  461) --> label: 7(truck), confidence: 0.38, fin: true
1727626646375|7f959e80e0     <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

        [-o,--output file] specify the output file name for saving results.
                <file>: file name with extension (e.g., output.jpg, output.json, output.mp4)
                This parameter must be used in conjunction with imageWriter, jsonWriter, or videoWriter.
                example:./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr,imageWriter -i filename -o output.jpg
                        ./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr,jsonWriter -i filename -o output.json
                        ./bin/snnf_nnsample -s Yolov5sDetection,HumanAttr,videoWriter -v filename -o output.mp4
                        
        [-a,--all] run all model testing

        assist tools: imageWriter videoWriter jsonWriter BotSortTrack HumanFilter VehicleFilter

Release folder structure

...

bin: snnf_nnsample. Prebuild sample programs that can run on the c3v Linux platform.
include：header file of sunplus NN framework SDK.
lib：libraries of sunplus 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 sunplus NN framework.
snnf_run.sh：executable script for running sample code.
snnf_build_samples.sh: executable script for building sample code.
snnf_env.sh: executable script for compiling environment.
thirdparty: just as its name implies.

How to run SNNF sample

Copy the release foler to C3V Linux.

Code Block

/SNNF/release # ls -alh
total 44K
drwxr-xr-x 8 101065 11400 4.0K Jan  3  2025 .
drwxr-xr-x 5 101065 11400 4.0K Jan  3  2025 ..
drwxr-xr-x 2 101065 11400 4.0K Jan  3  2025 bin
drwxr-xr-x 6 101065 11400 4.0K Jan  3  2025 include
drwxr-xr-x 3 101065 11400 4.0K Jan  3  2025 lib
drwxr-xr-x 7 101065 11400 4.0K Jan  3  2025 resource
drwxr-xr-x 4 101065 11400 4.0K Jan  3  2025 samples
-rwxr-xr-x 1 101065 11400  223 Jan  3  2025 snnf_build_samples.sh
-rwxr-xr-x 1 101065 11400 1.6K Jan  3  2025 snnf_env.sh
-rwxr-xr-x 1 101065 11400  430 Jan  3  2025 snnf_run.sh
drwxr-xr-x 7 101065 11400 4.0K Jan  3  2025 thirdparty

Run nnf_run.sh to run the SNNF sample.

a. One-time input

./snnf_run.sh -m YoloV8sDetection

Code Block

# ./snnf_run.sh -m YoloV8sDetection
1735821845919|7f808d8a00|T|common: [app]YoloV8sDetection in
1735821846236|7f69ef80e0|T|common: [app]GeneralModelOutputListener detect from resource/image/vehicle.jpg, the result: (box:  672  127  325  873415.31 288.56 1218.38 520.88) --> label: 02(personcar), confidence: 0.9096, fin: falsetrue
17276266463751735821846284|7f959e80e07f808d8a00|T|common: [app]GeneralModelOutputListener detect from YoloV8sDetection out, the resultretVal: (box:    0  364  267  461) --> label: 7(truck), confidence: 0.39, fin: true
1727626646440|7f959e80e00

b. Read input from the image file

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

Code Block

# ./snnf_run.sh -m YoloV8nDetectionOpti -i resource/image/person640x640.jpg
1735822076622|7fbe2f5a00|T|common: [app]YoloV8nDetectionOpti in
1735822079033|7fa61da0e0|T|common: [app]GeneralModelOutputListener detect from resource/image/person640x640.jpg, the result: (box:  671  125  327  8780.00 19.00 614.75 619.44) --> label: 0(person), confidence: 0.90, fin: false
1727626646440|7f959e80e087, fin: true
1735822079075|7fbe2f5a00|T|common: [app]YoloV8nDetectionOpti out, retVal: 0

c. Read inputs from the video file.

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

Code Block

# ./snnf_run.sh -m YoloV8nDetectionOpti -v resource/video/humanCount.mp4
1735823196079|7fabb9e040|T|common: [app]GeneralModelOutputListenerstreaming detect from , the result: (box:    0  364  267  460) --> label: 7(truck), confidence: 0.41, fin: true
1727626646498|7f959e80e0in
1735823198479|7f91468080|T|common: [app]streaming test: runner func in
1735823198840|7f91c78080|T|common: [app]GeneralModelOutputListener detect from , the result: (box:  673  125  323  878672.19 131.25 303.19 873.38) --> label: 0(person), confidence: 0.90, fin: false
......
1727626682667|7f959e80e01735823198840|7f91c78080|T|common: [app]GeneralModelOutputListener detect from , the result: (box:  977  159  330  8940.56 364.31 268.12 460.69) --> label: 07(persontruck), confidence: 0.9139, fin: false
17276266826671735823198840|7f959e80e07f91c78080|T|common: [app]GeneralModelOutputListener detect from , the result: (box:  668  262  270  708 548.06 10.31 1369.88 1009.69) --> label: 06(persontrain), confidence: 0.8625, fin: falsetrue
17276266826671735823198859|7f959e80e07f91c78080|T|common: [app]GeneralModelOutputListener detect from , the result: (box: 673.88 127.88  1  363  267  457323.06 875.44) --> label: 70(truckperson), confidence: 0.3590, fin: false
17276266826671735823198859|7f959e80e07f91c78080|T|common: [app]GeneralModelOutputListener detect from , the result: (box:  465   26 1454 10030.56 364.31 268.31 460.69) --> label: 67(traintruck), confidence: 0.3240, fin: false
17276266826671735823198859|7f959e80e07f91c78080|T|common: [app]GeneralModelOutputListener detect from , the result: (box:  847  565  173  403543.00 13.31 1375.31 1009.50) --> label: 06(persontrain), confidence: 0.26, fin: true

1727626686753|7faf81e020|T|common: [app]q to quit

d. Sequential models

./snnf_run.sh -s Yolov5sDetection,HumanAttr

Code Block

# ./snnf_run.sh -s Yolov5sDetection,HumanAttr
1727627713332|7fa3285020: true
......
1735823234584|7f91c78080|T|common: [app]sequential in
1727627713501|7fa3285020|T|common: [app]input image name: resource/image/person.jpg
1727627713833|7f937c50e0|T|common: [nn]detectedInfos: 1
1727627713833|7f937c50e0GeneralModelOutputListener detect from , the result: (box: 977.81 159.75 329.81 895.69) --> label: 0(person), confidence: 0.90, fin: false
1735823234584|7f91c78080|T|common: [app]human attr(box:  606  141  274  655GeneralModelOutputListener detect from , the result: (box: 666.94 263.81 275.06 708.00) --> resultlabel: Male
Age18-60
Direct: Front
Glasses: True
Hat: False
HoldObjectsInFront: False
Bag: No bag
Upper: ShortSleeve UpperStride
Lower: Trousers
Shose: No boots
1727627713833|7f937c50e0|T|common: [nn]detectedInfos: 1
1727627713833|7f937c50e00(person), confidence: 0.87, fin: false
1735823234584|7f91c78080|T|common: [app]GeneralModelOutputListener detect from , the result: (box: 1583.39 458.81 64.27 130.03) --> label: 0(person), confidence: 0.33, fin: false
1735823234584|7f91c78080|T|common: [app]human attr(box:  308  188  207  591GeneralModelOutputListener detect from , the result: (box: 466.12 10.88 1453.88 1017.75) --> result:
Female
Age18-60
Direct: Back
Glasses: False
Hat: False
HoldObjectsInFront: False
Bag: ShoulderBag
Upper: ShortSleeve UpperLogo
Lower: LowerPattern Shorts
Shose: No boots
1727627718787|7fa3285020label: 6(train), confidence: 0.32, fin: true

1735823343756|7fabb9e040|T|common: [app]q to quit
q
1735823348373|7fabb9e040|T|common: [app]The input file: resource/video/humanCount.mp4 has 516 frames
1735823348374|7fabb9e040|T|common: [app]sequentialstreaming out, retVal: -0x0

...

-0x0

d. Sequential models

./snnf_run.sh -s YoloV8sPoseYolov5sDetection,imageWriterHumanAttr

Code Block

# ./snnf_run.sh -s YoloV8sPoseYolov5sDetection,imageWriterHumanAttr
1727627887700|7fafe27020|T|common: [app]sequential in
1727627887700|7fafe27020|T|common: [app]warning: sequential model list(not tested)
1727627887725|7fafe27020|I|common: [nn]create model from pluginName: YoloV8sPose takes: 24
1727627887858|7fafe270201735827166109|7f8a9f9040|T|common: [app]sequential in
1735827166536|7f8a9f9040|T|common: [app]input image name: resource/image/pose_inputperson.jpg
17276278887071735827166657|7f9db2a0e07f792b9080|IT|common: [nnapp]picked: 5
1727627888707|7f9db2a0e0|I|common: [nn]plot:  0  91%, [(852, 142) - (1169, 753)], person
1727627888710|7f9db2a0e0|I|common: [nn]plot:  0  89%, [(1689, 187) - (1835, 642)], person
1727627888711|7f9db2a0e0|I|common: [nn]plot:  0  89%, [(61, 123) - (232, 601)], person
1727627888711|7f9db2a0e0|I|common: [nn]plot:  0  88%, [(1337, 330) - (1441, 679)], person
1727627888712|7f9db2a0e0|I|common: [nn]plot:  0  87%, [(369, 252) - (480, 671)], person
1727627888839|7f9db2a0e0|T|common: [app]write an image: detected_1883_0931_1727627888712.jpg
1727627893526|7fafe27020human attr(box: 612.44 156.84 268.88 625.51) --> result:
age: 18-60
bag: No bag
direction: Front
gender: Male
glasses: True
hat: False
holdObjectsInFront: False
lower: Trousers
shose: No boots
upper: ShortSleeve UpperStride
1735827166660|7f792b9080|T|common: [app]human attr(box: 311.82 181.12 199.79 606.84) --> result:
age: 18-60
bag: ShoulderBag
direction: Back
gender: Female
glasses: False
hat: False
holdObjectsInFront: False
lower: LowerPattern Shorts
shose: No boots
upper: ShortSleeve

1735827171625|7f8a9f9040|T|common: [app]sequential out, retVal: -0x0

Results will e. Model inference results save to the image detected_1883_0931_1727627888712.jpg.

How to build SNNF

...

.a

.

...

/snnf_

...

run.sh

...

b. All the resource will installed to release folder.

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

a. Setting environment variables independently.

Code Block
export LD_LIBRARY_PATH=${PWD}/lib:${PWD}/thirdparty/opencv4/lib:${PWD}/thirdparty/pytorch/lib:${PWD}/thirdparty/freetype/lib:${PWD}/thirdparty/libpng/lib:${LD_LIBRARY_PATH}

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

...

https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/models/onnx/version-slim-320.onnx

...

Optical character recognition

...

ch_PP-OCRv3_det_infer
ch_ppocr_mobile_v2.0_cls_infer
ch_PP-OCRv3_rec_infer

...

Age recognition

...

https://gitcode.com/smahesh29/Gender-and-Age-Detection/commits/master 7c024d9d453c9b35a72a984d8821b5832ef17401

...

Yolov8 Detection

...

https://github.com/ultralytics/ultralytics

...

Yolov8 Pose

...

https://github.com/ultralytics/ultralytics

...

Yolov8 OBB

...

https://github.com/ultralytics/ultralytics

...

Yolov8 Segmentation

...

https://github.com/ultralytics/ultralytics

...

Yolov8 Classification

...

https://github.com/ultralytics/ultralytics

...

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

...

-s YoloV8sPose,imageWriter

Code Block

# ./snnf_run.sh -s YoloV8sPose,imageWriter
1735827675908|7fba745040|T|common: [app]sequential in
1735827675908|7fba745040|T|common: [app]warning: sequential model list(not tested)
1735827676344|7fba745040|T|common: [app]input image name: resource/image/pose_input.jpg
1735827676641|7fa3fff080|T|common: [app]write an image: detected_1883_0931_1735827676535.jpg
1735827681383|7fba745040|T|common: [app]sequential out, retVal: -0x0

Results will save to the image detected_1883_0931_1735827676535.jpg.

f. Model inference results save to json file.

./snnf_run.sh -s YoloV8sPose,jsonWriter -o yolov8PoseResults.json

Code Block

 # ./snnf_run.sh -s YoloV8sPose,jsonWriter -o yolov8PoseResults.json
 1736129305551|7fb7dfae30|T|common: [app]sequential in
 1736129305551|7fb7dfae30|T|common: [app]warning: sequential model list(not tested)
 1736129305738|7fb7dfae30|T|common: [app]input image name: resource/image/pose_input.jpg
 1736129310785|7fb7dfae30|T|common: [app]sequential out, retVal: -0x0

Results will save to yolov8PoseResults.json. If the - o option is unused, it will be saved as a file by default: default_result.json.

How to build SNNF

Cross-compile for C3V environment.

a. Please use snnf_build.sh for SNNF compiling.

b. All the resource will be installed to the release folder.

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

a. Setting environment variables independently.

Code Block
export LD_LIBRARY_PATH=${PWD}/lib:${PWD}/thirdparty/opencv4/lib:${PWD}/thirdparty/pytorch/lib:${PWD}/thirdparty/freetype/lib:${PWD}/thirdparty/libpng/lib:${LD_LIBRARY_PATH}

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

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

Models reference

STGCN

Model Name	Version or Path
genderage	https://github.com/deepinsight/insightface
Object tracking	BotSort
Falling Recognition

User API

Please refer to API DOC v2.0 .

Version	Old Version 1	New Version Current
Changes made by	杨彬	杨彬
Saved on	Jan 07, 2025	Jan 15, 2025

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How to verify Official Demos

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Release folder structure

How to run SNNF sample

How to build SNNF

Models reference

How to build SNNF

Models reference

User API

Page Comparison

Versions Compared

Key

Target of the V1.1.0

Resource

Usage of V1.0.0

How to verify Official Demos

Resource

Usage of V1.1.0

How to verify Official Demos

Release folder structure

How to run NN framework sample

Release folder structure

How to run SNNF sample

How to build SNNF

Models reference

How to build SNNF

Models reference

User API