Coder Social home page Coder Social logo

iris_detector's Introduction

iris_detector

Alt text

How to train iris detector

See how_to_train_iris_detector_with_caffe_ssd.md

Network design

ResNet10-SSD

Using 4 residual modules as base network, then add SSD's extra layers.

Visualization of network structure (tools from ethereon) http://ethereon.github.io/netscope/#/gist/bc73857987941a56bc45bf4c4ae870b0

ResNet10-SSD with half filter number

The structure is same as ResNet10-SSD except its filter number. When filter number is larger than 32, reduce it by half.

Visualization of network structure (tools from ethereon) http://ethereon.github.io/netscope/#/gist/cf4dccec1f9a6c8f3f125000cd7b97f9

MobileNet-SSD

See https://github.com/chuanqi305/MobileNet-SSD

Visualization of network structure (tools from ethereon) http://ethereon.github.io/netscope/#/gist/e1e8c3c3a450f0502ef8ff6547d5dedb

Experiment

Our iris dataset has 12800 training samples and 3200 test samples. Training on GTX1080Ti. Evaluate on Intel i5 CPU and GTX1080Ti GPU.

Speed test ResNet10+SSD(half) is faster than others.

Network [email protected] Speed on Intel i5 CPU(ms) Speed on GTX1080Ti(ms) Input resolution
ResNet10+SSD 1.0 20 13 640x480
ResNet10+SSD(half) 1.0 10 7 640x480
MobileNet+SSD - 27 18 640x480

ResNet10-SSD

Training

When set confidence threshold to 0.5 and set IoU threshold to 0.5, the accuracy is 100%.

Alt text

Alt text

Evaluation

On Intel i5 CPU, the average inference time is 20ms. On GTX1080Ti GPU, the average inference time is 13ms.

Intel i5 CPU

  • evaluate by opencv3.4 Python API: Alt text

  • evaluate by opencv3.4 C++ API: Alt text

GTX1080Ti GPU

Alt text

ResNet10-SSD with half filter number

Training

When set confidence threshold to 0.5 and set IoU threshold to 0.5, the accuracy is 100%.

Alt text

Alt text

Evaluation

On Intel i5 CPU, the average inference time is 10ms. On GTX1080Ti GPU, the average inference time is 7ms.

Intel i5 CPU

evaluate by opencv3.4 Python API: Alt text

evaluate by opencv3.4 C++ API: Alt text

GTX1080Ti GPU

Alt text

MobileNet-SSD

Evaluation

We just evaluate mobilenet_300x300_ssd_iter_3000.caffemodel. On Intel i5 CPU, the average inference time is 27ms. On GTX1080Ti GPU, the average inference time is 18ms. The speed is slower than ResNet10-SSD which has high accurracy on iris dataset, so we stop training.

Alt text

Questions

Please contact [email protected]

iris_detector's People

Contributors

zhongqianli avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.