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
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evaluate by opencv3.4 Python API:
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evaluate by opencv3.4 C++ API:
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]