General Information

  • Free, open-source based library of pre-trained AI applications available on github.
  • ⭐Source Code: https://github.com/Ignitarium-Renesas/RZV2L_AiLibrary
  • This Library has API functions for leveraging AI applications that will run on Renesas RZ/V2L Board. Currently this library has following sample applications:
    • Human Head Counter
    • Line crossing object Counter
    • Elderly people fall detection (Work in progress)
    • Safety helmet and vest detection
    • Human age and gender detection (Work in progress)
    • Face recognition and spoof detection (Work in progress

Pre-Trained AI Article

Addition Notes

The Pre-trained models include pre-compiled applications as well as AI Models translated to run on the DRP-AI hardware. These files are located in the "exe" folder for each Pre-trained Application. These precompiled application are compiled for the Renesas RZV2L EVK using the Coral MIPI Camera. This folder can be simple copied to the board using SCP recursive command. NOTE : Some Pre-trained Applicatino

Support USB Camera

By default the Pretrained Applications are compiled to use the MIPI camera. The Pre-trained applications can be modified to use USB camera. This modification is only relevant to applications that support video.

  1. Open the application folder src folder. For example 01_Head_count/Head_count_cam/src
  2. Open the define.h header file.
  3. Find the following line. Comment out the macro that defines INPUT_CORAL.
/* Coral Camera support */
#define INPUT_CORAL

Sample Videos

NOTE: Memory Usage includes the Image Input, Inference Output, Inference Weights and Inference Parameters. Applications that use multiple AI Models are run sequentially


Head Count Application


 Model : YoloV3 
 Memory Usage: 235MB 
 Inference Input Shape : 416,416,3 
 Inference Time: 348ms



Head Count


Line Crossing Object Counting


 Model : TinyYoloV2 
 Memory Usage: 52MB 
 Inference Input Shape : 416,416,3 
 Inference Time:



Line Count


Fall Detection


 Model : Tiny Yolov2 
 Memory Usage: 52MB 
 Inference Input Shape : 256, 192, 3 
 Inference Time: 59ms

 Model : HRNET 
 Memory Usage: 129MB 
 Inference Input Shape : 256, 192, 3 
 Inference Time: 163ms

 Total Inference Time : 222ms



Fall Detection


Age and Gender Detection


 Model : Custom age 
 Memory Usage: 24MB 
 Inference Input Shape : 416,416,3 
 Inference Time: 10ms

 Model : Custom gender 
 Memory Usage: 87MB 
 Inference Input Shape : 224,224,3 
 Inference Time: 10ms

 Model : Tiny YoloV2 
 Memory Usage: 52MB 
 Inference Input Shape : 416,416,3 
 Inference Time: 59ms

 Total Inference Time: 79ms



Age and Gender Detection


Face Recognition, Spoofing, and Registration


 Model : Resnet50 
 Memory Usage: 88MB 
 Inference Input Shape : 224,224,3 
 Inference Time: 96ms



Face Recognition


Animal Detection


 Model : YoloV3 
 Memory Usage: 236MB 
 Inference Input Shape : 416,416,3 
 Inference Time: 360ms



Animal Detection


Hand Gesture Recognition


 Model : Custom Pose Detector for Hand 
 Memory Usage: 91MB 
 Inference Input Shape : 256,256,3 
 Inference Time: 256ms



Hand Gesture Recognition


Human Gaze Recognition


 Model : Resnet18 
 Memory Usage: 38MB 
 Inference Input Shape : 416,416,3 
 Inference Time: 33

 Model : Tiny YoloV2 
 Memory Usage: 52MB 
 Inference Input Shape : 416,416,3 
 Inference Time: 58

 Total Inference Time: 91ms



Gaze Recognition


  • No labels

Site Policy