zychen2016 / Stronger-yolo-pytorch

Pytorch implementation of Stronger-Yolo with channel-pruning.

Strongeryolo-pytorch

Introduction

This project is inspired by Stronger-Yolo. I reimplemented with Pytorch and continue improving yolov3 with latest papers.
This project will also try out some model-compression approaches(e.g. channel-pruning).
See reimplementation results in MODELZOO.

Environment

python3.6, pytorch1.2(1.0+ should be ok), ubuntu14/16/18 tested.

Quick Start

See Usage.md for details.

Improvement with latest papers(Using StrongerV3 as baseline)

model	mAP50	mAP75	configs
baseline(with GIOU)	79.6	43.4	strongerv3.yaml
+ kl loss&&varvote	78.9	49.2	strongerv3_kl.yaml

Note:
1.Set EVAL.varvote=True to enable varvote in KL-loss. According to the paper, kl-loss(and varvote) can strongly boost the performance of mAP75(or higher), but decrease mAP50 slightly.

Performance on VOC2007 Test(mAP) after pruning

Model	MAP	Flops(G)	Params(M)
strongerv3	79.6	4.33	6.775
strongerv3-sparsed	77.4	4.33	6.775
strongerv3-Pruned(30% pruned)	77.1	3.14	3.36

Note:
1.All experiments are trained for 60 epochs.
2.All experiments tested with threshold 0.1 in 512 resolution.

Supported backbone

• MobileV2
• DarkNet
  ...

Reference

Stronger-Yolo
focal-loss
kl-loss