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Fssd.pytorch

A Pytorch0.4 re-implementation of Fssd detection network, with pretrained weights on VOC0712 and mAP=79.74.

Network train data test data mAP FPS Download Link
FSSD VOC 07+12 VOC 07 test 79.74 90 Baidu/Google

Official caffe implementation is here, and pytorch0.3 re-implementation is here.

For details, please read the paper: FSSD:Feature Fusion Single Shot Multibox Detector

Our re-implementation is slightly better than paper (79.74 vs 78.8). Inference time is tested on TITAN X Pasal and cudnn V5. More high performance models will be available soon.

About more details

  • The implementation code abandons the bn layer after feature fusion.
  • Because the limitation of amount of gpu, batch_size is set to 32. If use 64 or more, I believe it will produce better performance.
  • If you want to re-produce more accurate model, I believe that adding BN to the body of FSSD and applying Mixup will be very effective.
  • The code is mainly based on rfbNet. If you are interested in this project, please email me(yhao.chen0617@gmail.com)

Update

I add a prune file for FSSD, which bases on Network Slimming (ICCV 2017), for details, please refer to the paper: Network Slimming.

State mAP FPS GPU Model Size Download Link
Original 79.74 90 TITAN X Pascal 136M Baidu/Google
First Prune (50%) 79.64 150 TITAN X Pascal 52M Bd(p6fe)/Gg
Second Prune

Steps for pruning the model:

  • Training the FSSD with BN model, meanwhile applying L1 constraints to the parameters of BN
  • Applying Network Slimming to the model trained in the previous step (Prune threshold is defaulted to 50%)
  • Re-train the pruned model, approximate 200 epochs, drop 0.1% in accuracy.
  • If still prune the model, I believe it can produce the result of mAP>79%, FPS~200. Of course, for the pruned model, BN can be merged to the convolution layer to be further accelerate.

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Pytorch re-implementation of Fssd

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