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DetectX

this android demo is aiming to apply poplular inference frameworks for different ML tasks with CameraX APIs.

inference framework

  • Opencv::DNN (4.2.0)
  • NCNN
  • Mace
  • SNPE (Qualcomm chips only)
  • pytorch(C++ version)

ML Models

  • MobileSSD
  • NanoDet-Plus-m_416

working table

Model Names NCNN OpenCV
NanoDet-Plus_m416 Y N
MobileSSD N Y

Profiling

NanoDet-Plus_m416

it takes around 0.2s in pure CPU mode with ncnn on SnapDragon-888.

references

benchmark from nanoDet

Model Resolution mAPval
0.5:0.95		 CPU Latency
(i7-8700)		 ARM Latency
(4xA76)		 FLOPS Params Model Size
NanoDet-m 320*320 20.6 4.98ms 10.23ms 0.72G 0.95M 1.8MB(FP16) | 980KB(INT8)
NanoDet-Plus-m 320*320 27.0 5.25ms 11.97ms 0.9G 1.17M 2.3MB(FP16) | 1.2MB(INT8)
NanoDet-Plus-m 416*416 30.4 8.32ms 19.77ms 1.52G 1.17M 2.3MB(FP16) | 1.2MB(INT8)
NanoDet-Plus-m-1.5x 320*320 29.9 7.21ms 15.90ms 1.75G 2.44M 4.7MB(FP16) | 2.3MB(INT8)
NanoDet-Plus-m-1.5x 416*416 34.1 11.50ms 25.49ms 2.97G 2.44M 4.7MB(FP16) | 2.3MB(INT8)
YOLOv3-Tiny 416*416 16.6 - 37.6ms 5.62G 8.86M 33.7MB
YOLOv4-Tiny 416*416 21.7 - 32.81ms 6.96G 6.06M 23.0MB
YOLOX-Nano 416*416 25.8 - 23.08ms 1.08G 0.91M 1.8MB(FP16)
YOLOv5-n 640*640 28.4 - 44.39ms 4.5G 1.9M 3.8MB(FP16)
FBNetV5 320*640 30.4 - - 1.8G - -
MobileDet 320*320 25.6 - - 0.9G - -