Benchmarking RefineDet[1] and other SSD (Single shot Detection) Network based on chainer
Including
- DSSD[2]
- SSD with residual prediction module[2]
- ESSD[3]
- RefineDet[1]
Original DSSD is based on ResNet 101. Since memory limitation, only tried on VGG.
| Model name | Base model | Input image size | mAP | mAP(paper) |
|---|---|---|---|---|
| SSD | VGG16 | 300x300 | 77.5 | 77.5 |
| SSD Plus(Use Residual Prediction module) | VGG16 | 300x300 | 78.0 | NA |
| ESSD | VGG16 | 300x300 | 78.8 | 79.4 |
| RefineDet | VGG16 | 320x320 | Now evaluating | 80.0 |
*: I set batchsize to 22 because of memory limitation. The original paper used 32.
*: Some training condition is different from paper.
*: ESSD original paper did 3 stages training (Only SSD, Only extensional module and whole network), but I did whole training only.
*: I may mistook unintensionally.
- Chainercv and its dependencies
git clone https://github.com/fukatani/RefineDet_chainer
cd refinedet
python train.py --model refinedet320 --batchsize 22
Many implementation is referenced chainercv. Thanks!