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	-	-