Mobile Detection Benchmark

Overview

Mobile Detection Benchmark

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

Benchmark Result

Model Input size mAPval
0.5:0.95
mAPval
0.5
Params
(M)
FLOPS
(G)
Latency1
(ms)
Latency2
(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 1 NCNN and 2 Paddle-Lite.
  • All model are trained on COCO train2017 dataset and evaluated on COCO val2017.

Support Library

TODO

TNN, MNN speed supplement, welcome to contribute!

You might also like...
ncnn is a high-performance neural network inference framework optimized for the mobile platform
ncnn is a high-performance neural network inference framework optimized for the mobile platform

ncnn ncnn is a high-performance neural network inference computing framework optimized for mobile platforms. ncnn is deeply considerate about deployme

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices.

Real time eye tracking for embedded and mobile devices.
Real time eye tracking for embedded and mobile devices.

drishti Real time eye tracking for embedded and mobile devices in C++11. NEWS (2018/08/10) Native iOS, Android, and "desktop" variants of the real-tim

PocketSphinx is a lightweight speech recognition engine, specifically tuned for handheld and mobile devices, though it works equally well on the desktop

PocketSphinx 5prealpha This is PocketSphinx, one of Carnegie Mellon University's open source large vocabulary, speaker-independent continuous speech r

Training and fine-tuning YOLOv4 Tiny on custom object detection dataset for Taiwanese traffic
Training and fine-tuning YOLOv4 Tiny on custom object detection dataset for Taiwanese traffic

Object Detection on Taiwanese Traffic using YOLOv4 Tiny Exploration of YOLOv4 Tiny on custom Taiwanese traffic dataset Trained and tested AlexeyAB's D

YOLOV4 tiny + lane detection on Android with 8 FPS!
YOLOV4 tiny + lane detection on Android with 8 FPS!

YOLOV4 Tiny + Ultra fast lane detection on Android with 8 FPS! Tested with HONOR 20PRO Kirin 980

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Build Type Linux MacOS Windows Build Status OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facia

A coverage-guided and memory-detection enabled fuzzer for windows applications.
A coverage-guided and memory-detection enabled fuzzer for windows applications.

WDFuzzer Manual 中文手册见 README_CN.md WDFuzzer:winafl + drmemory WDFuzzer is an A coverage-guided and memory detection abled fuzzer for for windows softw

Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)
Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)

GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Mo

Owner
Jewel
free occupation employee
Jewel
Benchmark framework of 3D integrated CIM accelerators for popular DNN inference, support both monolithic and heterogeneous 3D integration

3D+NeuroSim V1.0 The DNN+NeuroSim framework was developed by Prof. Shimeng Yu's group (Georgia Institute of Technology). The model is made publicly av

NeuroSim 12 Nov 11, 2022
Benchmark framework of compute-in-memory based accelerators for deep neural network (inference engine focused)

DNN+NeuroSim V1.3 The DNN+NeuroSim framework was developed by Prof. Shimeng Yu's group (Georgia Institute of Technology). The model is made publicly a

NeuroSim 32 Nov 24, 2022
DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

DAMOV is a benchmark suite and a methodical framework targeting the study of data movement bottlenecks in modern applications. It is intended to study new architectures, such as near-data processing. Described by Oliveira et al.

SAFARI Research Group at ETH Zurich and Carnegie Mellon University 30 Nov 16, 2022
Open-L2O - A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms

Open-L2O This repository establishes the first comprehensive benchmark efforts of existing learning to optimize (L2O) approaches on a number of proble

VITA 153 Nov 29, 2022
Conan recipe for Google Benchmark library

DEPRECATED Please note that as Google Benchmark now has an official Conan support this repository should be considered deprecated. Please download the

Mateusz Pusz 8 Jan 17, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Nov 30, 2022
VNOpenAI 28 Nov 30, 2022
A lightweight 2D Pose model can be deployed on Linux/Window/Android, supports CPU/GPU inference acceleration, and can be detected in real time on ordinary mobile phones.

A lightweight 2D Pose model can be deployed on Linux/Window/Android, supports CPU/GPU inference acceleration, and can be detected in real time on ordinary mobile phones.

JinquanPan 56 Nov 28, 2022
pose demo on android mobile based on PaddleDetection

pose_demo_android pose demo on android mobile based on PaddleDetection 本工程Android部分基于Paddle-Lite-Demo修改。 算法模型基于PaddleDetection的PP-TinyPose. 如欲获取更多详情,请

null 50 Dec 1, 2022