See how_to_train_iris_detector_with_caffe_ssd.md
Using 4 residual modules as base network, then add SSD's extra layers.
Visualization of network structure (tools from ethereon) https://ethereon.github.io/netscope/#/gist/bc73857987941a56bc45bf4c4ae870b0
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) https://ethereon.github.io/netscope/#/gist/cf4dccec1f9a6c8f3f125000cd7b97f9
See https://github.com/chuanqi305/MobileNet-SSD
Visualization of network structure (tools from ethereon) https://ethereon.github.io/netscope/#/gist/e1e8c3c3a450f0502ef8ff6547d5dedb
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 |
When set confidence threshold to 0.5 and set IoU threshold to 0.5, the accuracy is 100%.
On Intel i5 CPU, the average inference time is 20ms. On GTX1080Ti GPU, the average inference time is 13ms.
Intel i5 CPU
GTX1080Ti GPU
When set confidence threshold to 0.5 and set IoU threshold to 0.5, the accuracy is 100%.
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
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.
Please contact [email protected]