Mobile Detection Benchmark

This repo is used to test the speed of the mobile terminal models

Benchmark Result

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{1^(ms)}	Latency^{2^(ms)}	Config
YOLOv3-Tiny	416	16.6	33.1	8.86	5.62	25.42	-	model link
YOLOv4-Tiny	416	21.7	40.2	6.06	6.96	23.69	-	model link
PP-YOLO-Tiny	320	20.6	-	1.08	0.58	6.75	-	model link
PP-YOLO-Tiny	416	22.7	-	1.08	1.02	10.48	-	model link
Nanodet-M	320	20.6	-	0.95	0.72	8.71	-	model link
Nanodet-M	416	23.5	-	0.95	1.2	13.35	-	model link
Nanodet-M 1.5x	416	26.8	-	2.08	2.42	15.83	-	model link
YOLOX-Nano	416	25.8	-	0.91	1.08	19.23	-	model link
YOLOX-Tiny	416	32.8	-	5.06	6.45	32.77	-	model link
YOLOv5n	640	28.4	46.0	1.9	4.5	40.35	-	model link
YOLOv5s	640	37.2	56.0	7.2	16.5	78.05	-	model link
PicoDet-S	320	27.1	41.4	0.99	0.73	8.13	6.65	model link
PicoDet-S	416	30.6	45.5	0.99	1.24	12.37	9.82	model link
PicoDet-M	320	30.9	45.7	2.15	1.48	11.27	9.61	model link
PicoDet-M	416	34.3	49.8	2.15	2.50	17.39	15.88	model link
PicoDet-L	320	32.6	47.9	3.24	2.18	15.26	13.42	model link
PicoDet-L	416	35.9	51.7	3.24	3.69	23.36	21.85	model link
PicoDet-L	640	40.3	57.1	3.24	8.74	54.11	50.55	model link
PicoDet-Shufflenetv2 1x	416	30.0	44.6	1.17	1.53	15.06	10.63	model link
PicoDet-MobileNetv3-large 1x	416	35.6	52.0	3.55	2.80	20.71	17.88	model link
PicoDet-LCNet 1.5x	416	36.3	52.2	3.10	3.85	21.29	20.8	model link

Table Notes:

Latency: All our models test on Qualcomm Snapdragon 865(4\*A77+4\*A55) with 4 threads by arm8 and with FP16. In the above table, test latency on 1NCNN and 2Paddle-Lite.
All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Support Library

TODO

TNN, MNN speed supplement, welcome to contribute!

Reference

Mobile Detection Benchmark

This repo is used to test the speed of the mobile terminal models

Benchmark Result

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{1^(ms)}	Latency^{2^(ms)}	Config
YOLOv3-Tiny	416	16.6	33.1	8.86	5.62	25.42	-	model link
YOLOv4-Tiny	416	21.7	40.2	6.06	6.96	23.69	-	model link
PP-YOLO-Tiny	320	20.6	-	1.08	0.58	6.75	-	model link
PP-YOLO-Tiny	416	22.7	-	1.08	1.02	10.48	-	model link
Nanodet-M	320	20.6	-	0.95	0.72	8.71	-	model link
Nanodet-M	416	23.5	-	0.95	1.2	13.35	-	model link
Nanodet-M 1.5x	416	26.8	-	2.08	2.42	15.83	-	model link
YOLOX-Nano	416	25.8	-	0.91	1.08	19.23	-	model link
YOLOX-Tiny	416	32.8	-	5.06	6.45	32.77	-	model link
YOLOv5n	640	28.4	46.0	1.9	4.5	40.35	-	model link
YOLOv5s	640	37.2	56.0	7.2	16.5	78.05	-	model link
PicoDet-S	320	27.1	41.4	0.99	0.73	8.13	6.65	model link
PicoDet-S	416	30.6	45.5	0.99	1.24	12.37	9.82	model link
PicoDet-M	320	30.9	45.7	2.15	1.48	11.27	9.61	model link
PicoDet-M	416	34.3	49.8	2.15	2.50	17.39	15.88	model link
PicoDet-L	320	32.6	47.9	3.24	2.18	15.26	13.42	model link
PicoDet-L	416	35.9	51.7	3.24	3.69	23.36	21.85	model link
PicoDet-L	640	40.3	57.1	3.24	8.74	54.11	50.55	model link
PicoDet-Shufflenetv2 1x	416	30.0	44.6	1.17	1.53	15.06	10.63	model link
PicoDet-MobileNetv3-large 1x	416	35.6	52.0	3.55	2.80	20.71	17.88	model link
PicoDet-LCNet 1.5x	416	36.3	52.2	3.10	3.85	21.29	20.8	model link

Table Notes:

Latency: All our models test on Qualcomm Snapdragon 865(4\*A77+4\*A55) with 4 threads by arm8 and with FP16. In the above table, test latency on 1NCNN and 2Paddle-Lite.
All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Support Library

TODO

TNN, MNN speed supplement, welcome to contribute!

Reference

Mobile Detection Benchmark

This repo is used to test the speed of the mobile terminal models

Benchmark Result

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{1^(ms)}	Latency^{2^(ms)}	Config
YOLOv3-Tiny	416	16.6	33.1	8.86	5.62	25.42	-	model link
YOLOv4-Tiny	416	21.7	40.2	6.06	6.96	23.69	-	model link
PP-YOLO-Tiny	320	20.6	-	1.08	0.58	6.75	-	model link
PP-YOLO-Tiny	416	22.7	-	1.08	1.02	10.48	-	model link
Nanodet-M	320	20.6	-	0.95	0.72	8.71	-	model link
Nanodet-M	416	23.5	-	0.95	1.2	13.35	-	model link
Nanodet-M 1.5x	416	26.8	-	2.08	2.42	15.83	-	model link
YOLOX-Nano	416	25.8	-	0.91	1.08	19.23	-	model link
YOLOX-Tiny	416	32.8	-	5.06	6.45	32.77	-	model link
YOLOv5n	640	28.4	46.0	1.9	4.5	40.35	-	model link
YOLOv5s	640	37.2	56.0	7.2	16.5	78.05	-	model link
PicoDet-S	320	27.1	41.4	0.99	0.73	8.13	6.65	model link
PicoDet-S	416	30.6	45.5	0.99	1.24	12.37	9.82	model link
PicoDet-M	320	30.9	45.7	2.15	1.48	11.27	9.61	model link
PicoDet-M	416	34.3	49.8	2.15	2.50	17.39	15.88	model link
PicoDet-L	320	32.6	47.9	3.24	2.18	15.26	13.42	model link
PicoDet-L	416	35.9	51.7	3.24	3.69	23.36	21.85	model link
PicoDet-L	640	40.3	57.1	3.24	8.74	54.11	50.55	model link
PicoDet-Shufflenetv2 1x	416	30.0	44.6	1.17	1.53	15.06	10.63	model link
PicoDet-MobileNetv3-large 1x	416	35.6	52.0	3.55	2.80	20.71	17.88	model link
PicoDet-LCNet 1.5x	416	36.3	52.2	3.10	3.85	21.29	20.8	model link

Table Notes:

Latency: All our models test on Qualcomm Snapdragon 865(4\*A77+4\*A55) with 4 threads by arm8 and with FP16. In the above table, test latency on 1NCNN and 2Paddle-Lite.
All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Support Library

TODO

TNN, MNN speed supplement, welcome to contribute!

Reference

Mobile Detection Benchmark

This repo is used to test the speed of the mobile terminal models

Benchmark Result

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{1^(ms)}	Latency^{2^(ms)}	Config
YOLOv3-Tiny	416	16.6	33.1	8.86	5.62	25.42	-	model link
YOLOv4-Tiny	416	21.7	40.2	6.06	6.96	23.69	-	model link
PP-YOLO-Tiny	320	20.6	-	1.08	0.58	6.75	-	model link
PP-YOLO-Tiny	416	22.7	-	1.08	1.02	10.48	-	model link
Nanodet-M	320	20.6	-	0.95	0.72	8.71	-	model link
Nanodet-M	416	23.5	-	0.95	1.2	13.35	-	model link
Nanodet-M 1.5x	416	26.8	-	2.08	2.42	15.83	-	model link
YOLOX-Nano	416	25.8	-	0.91	1.08	19.23	-	model link
YOLOX-Tiny	416	32.8	-	5.06	6.45	32.77	-	model link
YOLOv5n	640	28.4	46.0	1.9	4.5	40.35	-	model link
YOLOv5s	640	37.2	56.0	7.2	16.5	78.05	-	model link
PicoDet-S	320	27.1	41.4	0.99	0.73	8.13	6.65	model link
PicoDet-S	416	30.6	45.5	0.99	1.24	12.37	9.82	model link
PicoDet-M	320	30.9	45.7	2.15	1.48	11.27	9.61	model link
PicoDet-M	416	34.3	49.8	2.15	2.50	17.39	15.88	model link
PicoDet-L	320	32.6	47.9	3.24	2.18	15.26	13.42	model link
PicoDet-L	416	35.9	51.7	3.24	3.69	23.36	21.85	model link
PicoDet-L	640	40.3	57.1	3.24	8.74	54.11	50.55	model link
PicoDet-Shufflenetv2 1x	416	30.0	44.6	1.17	1.53	15.06	10.63	model link
PicoDet-MobileNetv3-large 1x	416	35.6	52.0	3.55	2.80	20.71	17.88	model link
PicoDet-LCNet 1.5x	416	36.3	52.2	3.10	3.85	21.29	20.8	model link

Table Notes:

Latency: All our models test on Qualcomm Snapdragon 865(4\*A77+4\*A55) with 4 threads by arm8 and with FP16. In the above table, test latency on 1NCNN and 2Paddle-Lite.
All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Support Library

TODO

TNN, MNN speed supplement, welcome to contribute!

Reference

Mobile Detection Benchmark

This repo is used to test the speed of the mobile terminal models

Benchmark Result

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{1^(ms)}	Latency^{2^(ms)}	Config
YOLOv3-Tiny	416	16.6	33.1	8.86	5.62	25.42	-	model link
YOLOv4-Tiny	416	21.7	40.2	6.06	6.96	23.69	-	model link
PP-YOLO-Tiny	320	20.6	-	1.08	0.58	6.75	-	model link
PP-YOLO-Tiny	416	22.7	-	1.08	1.02	10.48	-	model link
Nanodet-M	320	20.6	-	0.95	0.72	8.71	-	model link
Nanodet-M	416	23.5	-	0.95	1.2	13.35	-	model link
Nanodet-M 1.5x	416	26.8	-	2.08	2.42	15.83	-	model link
YOLOX-Nano	416	25.8	-	0.91	1.08	19.23	-	model link
YOLOX-Tiny	416	32.8	-	5.06	6.45	32.77	-	model link
YOLOv5n	640	28.4	46.0	1.9	4.5	40.35	-	model link
YOLOv5s	640	37.2	56.0	7.2	16.5	78.05	-	model link
PicoDet-S	320	27.1	41.4	0.99	0.73	8.13	6.65	model link
PicoDet-S	416	30.6	45.5	0.99	1.24	12.37	9.82	model link
PicoDet-M	320	30.9	45.7	2.15	1.48	11.27	9.61	model link
PicoDet-M	416	34.3	49.8	2.15	2.50	17.39	15.88	model link
PicoDet-L	320	32.6	47.9	3.24	2.18	15.26	13.42	model link
PicoDet-L	416	35.9	51.7	3.24	3.69	23.36	21.85	model link
PicoDet-L	640	40.3	57.1	3.24	8.74	54.11	50.55	model link
PicoDet-Shufflenetv2 1x	416	30.0	44.6	1.17	1.53	15.06	10.63	model link
PicoDet-MobileNetv3-large 1x	416	35.6	52.0	3.55	2.80	20.71	17.88	model link
PicoDet-LCNet 1.5x	416	36.3	52.2	3.10	3.85	21.29	20.8	model link

Table Notes:

Latency: All our models test on Qualcomm Snapdragon 865(4\*A77+4\*A55) with 4 threads by arm8 and with FP16. In the above table, test latency on 1NCNN and 2Paddle-Lite.
All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Support Library

TODO

TNN, MNN speed supplement, welcome to contribute!

Reference

Mobile Detection Benchmark

This repo is used to test the speed of the mobile terminal models

Benchmark Result

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{1^(ms)}	Latency^{2^(ms)}	Config
YOLOv3-Tiny	416	16.6	33.1	8.86	5.62	25.42	-	model link
YOLOv4-Tiny	416	21.7	40.2	6.06	6.96	23.69	-	model link
PP-YOLO-Tiny	320	20.6	-	1.08	0.58	6.75	-	model link
PP-YOLO-Tiny	416	22.7	-	1.08	1.02	10.48	-	model link
Nanodet-M	320	20.6	-	0.95	0.72	8.71	-	model link
Nanodet-M	416	23.5	-	0.95	1.2	13.35	-	model link
Nanodet-M 1.5x	416	26.8	-	2.08	2.42	15.83	-	model link
YOLOX-Nano	416	25.8	-	0.91	1.08	19.23	-	model link
YOLOX-Tiny	416	32.8	-	5.06	6.45	32.77	-	model link
YOLOv5n	640	28.4	46.0	1.9	4.5	40.35	-	model link
YOLOv5s	640	37.2	56.0	7.2	16.5	78.05	-	model link
PicoDet-S	320	27.1	41.4	0.99	0.73	8.13	6.65	model link
PicoDet-S	416	30.6	45.5	0.99	1.24	12.37	9.82	model link
PicoDet-M	320	30.9	45.7	2.15	1.48	11.27	9.61	model link
PicoDet-M	416	34.3	49.8	2.15	2.50	17.39	15.88	model link
PicoDet-L	320	32.6	47.9	3.24	2.18	15.26	13.42	model link
PicoDet-L	416	35.9	51.7	3.24	3.69	23.36	21.85	model link
PicoDet-L	640	40.3	57.1	3.24	8.74	54.11	50.55	model link
PicoDet-Shufflenetv2 1x	416	30.0	44.6	1.17	1.53	15.06	10.63	model link
PicoDet-MobileNetv3-large 1x	416	35.6	52.0	3.55	2.80	20.71	17.88	model link
PicoDet-LCNet 1.5x	416	36.3	52.2	3.10	3.85	21.29	20.8	model link

Table Notes:

Latency: All our models test on Qualcomm Snapdragon 865(4\*A77+4\*A55) with 4 threads by arm8 and with FP16. In the above table, test latency on 1NCNN and 2Paddle-Lite.
All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Support Library

TODO

TNN, MNN speed supplement, welcome to contribute!

Reference

Mobile Detection Benchmark

This repo is used to test the speed of the mobile terminal models

Benchmark Result

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{1^(ms)}	Latency^{2^(ms)}	Config
YOLOv3-Tiny	416	16.6	33.1	8.86	5.62	25.42	-	model link
YOLOv4-Tiny	416	21.7	40.2	6.06	6.96	23.69	-	model link
PP-YOLO-Tiny	320	20.6	-	1.08	0.58	6.75	-	model link
PP-YOLO-Tiny	416	22.7	-	1.08	1.02	10.48	-	model link
Nanodet-M	320	20.6	-	0.95	0.72	8.71	-	model link
Nanodet-M	416	23.5	-	0.95	1.2	13.35	-	model link
Nanodet-M 1.5x	416	26.8	-	2.08	2.42	15.83	-	model link
YOLOX-Nano	416	25.8	-	0.91	1.08	19.23	-	model link
YOLOX-Tiny	416	32.8	-	5.06	6.45	32.77	-	model link
YOLOv5n	640	28.4	46.0	1.9	4.5	40.35	-	model link
YOLOv5s	640	37.2	56.0	7.2	16.5	78.05	-	model link
PicoDet-S	320	27.1	41.4	0.99	0.73	8.13	6.65	model link
PicoDet-S	416	30.6	45.5	0.99	1.24	12.37	9.82	model link
PicoDet-M	320	30.9	45.7	2.15	1.48	11.27	9.61	model link
PicoDet-M	416	34.3	49.8	2.15	2.50	17.39	15.88	model link
PicoDet-L	320	32.6	47.9	3.24	2.18	15.26	13.42	model link
PicoDet-L	416	35.9	51.7	3.24	3.69	23.36	21.85	model link
PicoDet-L	640	40.3	57.1	3.24	8.74	54.11	50.55	model link
PicoDet-Shufflenetv2 1x	416	30.0	44.6	1.17	1.53	15.06	10.63	model link
PicoDet-MobileNetv3-large 1x	416	35.6	52.0	3.55	2.80	20.71	17.88	model link
PicoDet-LCNet 1.5x	416	36.3	52.2	3.10	3.85	21.29	20.8	model link

Table Notes:

Latency: All our models test on Qualcomm Snapdragon 865(4\*A77+4\*A55) with 4 threads by arm8 and with FP16. In the above table, test latency on 1NCNN and 2Paddle-Lite.
All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Support Library

TODO

TNN, MNN speed supplement, welcome to contribute!

Reference

Mobile Detection Benchmark

This repo is used to test the speed of the mobile terminal models

Benchmark Result

Model	Input size	mAP^val 0.5:0.95	mAP^val 0.5	Params ^(M)	FLOPS ^(G)	Latency^{1^(ms)}	Latency^{2^(ms)}	Config
YOLOv3-Tiny	416	16.6	33.1	8.86	5.62	25.42	-	model link
YOLOv4-Tiny	416	21.7	40.2	6.06	6.96	23.69	-	model link
PP-YOLO-Tiny	320	20.6	-	1.08	0.58	6.75	-	model link
PP-YOLO-Tiny	416	22.7	-	1.08	1.02	10.48	-	model link
Nanodet-M	320	20.6	-	0.95	0.72	8.71	-	model link
Nanodet-M	416	23.5	-	0.95	1.2	13.35	-	model link
Nanodet-M 1.5x	416	26.8	-	2.08	2.42	15.83	-	model link
YOLOX-Nano	416	25.8	-	0.91	1.08	19.23	-	model link
YOLOX-Tiny	416	32.8	-	5.06	6.45	32.77	-	model link
YOLOv5n	640	28.4	46.0	1.9	4.5	40.35	-	model link
YOLOv5s	640	37.2	56.0	7.2	16.5	78.05	-	model link
PicoDet-S	320	27.1	41.4	0.99	0.73	8.13	6.65	model link
PicoDet-S	416	30.6	45.5	0.99	1.24	12.37	9.82	model link
PicoDet-M	320	30.9	45.7	2.15	1.48	11.27	9.61	model link
PicoDet-M	416	34.3	49.8	2.15	2.50	17.39	15.88	model link
PicoDet-L	320	32.6	47.9	3.24	2.18	15.26	13.42	model link
PicoDet-L	416	35.9	51.7	3.24	3.69	23.36	21.85	model link
PicoDet-L	640	40.3	57.1	3.24	8.74	54.11	50.55	model link
PicoDet-Shufflenetv2 1x	416	30.0	44.6	1.17	1.53	15.06	10.63	model link
PicoDet-MobileNetv3-large 1x	416	35.6	52.0	3.55	2.80	20.71	17.88	model link
PicoDet-LCNet 1.5x	416	36.3	52.2	3.10	3.85	21.29	20.8	model link

Table Notes:

Latency: All our models test on Qualcomm Snapdragon 865(4\*A77+4\*A55) with 4 threads by arm8 and with FP16. In the above table, test latency on 1NCNN and 2Paddle-Lite.
All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Name		Name	Last commit message	Last commit date
app		app
gradle/wrapper		gradle/wrapper
ncnn		ncnn
paddlelite		paddlelite
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
README.md		README.md
build.gradle		build.gradle
gradle.properties		gradle.properties
gradlew		gradlew
gradlew.bat		gradlew.bat
local.properties		local.properties
screenshot.jpg		screenshot.jpg
settings.gradle		settings.gradle

Name		Name	Last commit message	Last commit date
app		app
gradle/wrapper		gradle/wrapper
ncnn		ncnn
paddlelite		paddlelite
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
README.md		README.md
build.gradle		build.gradle
gradle.properties		gradle.properties
gradlew		gradlew
gradlew.bat		gradlew.bat
local.properties		local.properties
screenshot.jpg		screenshot.jpg
settings.gradle		settings.gradle

Name		Name	Last commit message	Last commit date
app		app
gradle/wrapper		gradle/wrapper
ncnn		ncnn
paddlelite		paddlelite
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
README.md		README.md
build.gradle		build.gradle
gradle.properties		gradle.properties
gradlew		gradlew
gradlew.bat		gradlew.bat
local.properties		local.properties
screenshot.jpg		screenshot.jpg
settings.gradle		settings.gradle

Name		Name	Last commit message	Last commit date
app		app
gradle/wrapper		gradle/wrapper
ncnn		ncnn
paddlelite		paddlelite
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
README.md		README.md
build.gradle		build.gradle
gradle.properties		gradle.properties
gradlew		gradlew
gradlew.bat		gradlew.bat
local.properties		local.properties
screenshot.jpg		screenshot.jpg
settings.gradle		settings.gradle

Name		Name	Last commit message	Last commit date
app		app
gradle/wrapper		gradle/wrapper
ncnn		ncnn
paddlelite		paddlelite
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
README.md		README.md
build.gradle		build.gradle
gradle.properties		gradle.properties
gradlew		gradlew
gradlew.bat		gradlew.bat
local.properties		local.properties
screenshot.jpg		screenshot.jpg
settings.gradle		settings.gradle

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Mobile Detection Benchmark

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TNN, MNN speed supplement, welcome to contribute!

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Mobile Detection Benchmark

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Mobile Detection Benchmark

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Mobile Detection Benchmark

Benchmark Result

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