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Target of the V1.1.0

This is the formal release V1.1.0 of SNNF(Sunplus Neural Network Framework).

Table of Contents
stylenone

Target of the V1.1.0

On the basis of v1.0.0, v1.1.0 mainly has the following updates：

  1. Add some NN module.

    • genderage: gender and age

      • Det_10g + genderage

      • LightFace + genderage

  2. 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.

  3. 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.

  4. Standardize post-process code.

  5. 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.

  6. 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.

  7. 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.

  8. Improve the toString() function for each model's result, and support the detection result toString JSON format.

  9. 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
languageplain
# ------------------------------------------------------------------------
# define which PLUGIN to build
  NN_PLUGIN_ENABLE_YOLOV5S_V2                 := yes

  NN_PLUGIN_ENABLE_RTMDETS                    := no
  NN_PLUGIN_ENABLE_GEDERAGE                   := no
  NN_PLUGIN_ENABLE_YOLOV8S_DETECTION          := no
  NN_PLUGIN_ENABLE_YOLOV8S_OBB                := no
  NN_PLUGIN_ENABLE_YOLOV8S_POSE               := no
  NN_PLUGIN_ENABLE_YOLOV8S_SEGMENT            := no
  NN_PLUGIN_ENABLE_YOLOV10S_DETECTION         := no

  NN_PLUGIN_ENABLE_YOLOV8N_CCPD               := no
  NN_PLUGIN_ENABLE_YOLOV8N_OBB_OPTI           := no
  NN_PLUGIN_ENABLE_YOLOV8N_POSE_OPTI          := no
  NN_PLUGIN_ENABLE_YOLOV8N_SEGMENT_OPTI       := no
  NN_PLUGIN_ENABLE_YOLOV8N_DETECTION_OPTI     := no
  NN_PLUGIN_ENABLE_YOLOV8S_DETECTION_OPTI     := no

  NN_PLUGIN_ENABLE_BOTSORT_TRACK              := no

...

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.

...

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
                        VehicleFilter              W600kR50                   YoloV8sOdMap
                        Yolov5sDetection           Yolov5sV1                  Yolov8nClassify
                        Yolov8sClassify            stgcn                      Yolov5sV2
                        BotSortTrackStgcn          GenderAge                  Rtmdets
                        YoloV10sDetection          YoloV8nCcpdOpti            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,HumanFilter,HumanAttr
                        LightFace,Age
                        OcrDet,OcrRec
                        OcrDet,OcrCls,OcrRec
                        YoloV8nCcpdOpti,OcrRec
                        Det10g,W600kR50
                        YoloV8sDetection,VehicleFilter,VehicleAttr
                        YoloV8nDetectionOpti,BotSortTrack
                        YoloV8nPoseOpti,BotSortTrackStgcn
                        YoloV8nCcpdOpti,OcrRec
                        YoloV8nCcpdOpti,OcrCls,OcrRec
                example:./bin/snnf_nnsample -s Yolov5sDetection,HumanFilter,HumanAttr
                        ./bin/snnf_nnsample --sequential ocrDet,ocrCls,ocrRec
                        ./bin/snnf_nnsample -s YoloV8nCcpdOpti,OcrRec,imageWriter
                        ./bin/snnf_nnsample -s YoloV8sDetectionOpti,BotSortTrack,videoWriter -v resource/video/humanCountpk-12.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

        [-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

...