zhongqianli / iris_detector

Real-time iris detector. Only need 8ms on Intel i5 CPU!

iris_detector

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%.

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:

• evaluate by opencv3.4 C++ API:

GTX1080Ti GPU

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%.

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:

evaluate by opencv3.4 C++ API:

GTX1080Ti GPU

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.

Questions

Please contact [email protected]

iris_detector's Issues

下载出错

尝试下载了很多次，一直下到一半就下载出错，楼主能看看什么原因吗 非常感谢

Backing work and dataset

Hey,

You mentioned you used your dataset with 12800+3200 images. Can you share the link for the dataset?

Also, is there a research work on which this paper is based or you are planning to publish one?

模型迁移训练

您好， 我在使用您的预训练模型训练opencv人脸的resnet10-ssd模型出现了层的不匹配的问题，请问您是不是发布的这个预训练模型已经将bn层合并了
Cannot copy param 0 weights from layer 'data_bn'; shape mismatch. Source param shape is 1 (1); target param shape is 3 (3). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer.

replace/remove normalize layer in resnet10 model

Hi,
Is there any way I can replace/remove normalize layer in resnet10 model?