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RefineDet_chainer

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.

Benchmark result

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.

Requirement

Usage

git clone https://github.com/fukatani/RefineDet_chainer
cd refinedet
python train.py --model refinedet320 --batchsize 22

Other

Many implementation is referenced chainercv. Thanks!

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RefineDet Network based on chainer

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