Strongeryolo-pytorch

Introduction

Pytorch implementation of Stronger-Yolo with channel-pruning.

Environment

python3.6, pytorch1.2(1.0+ should be ok)

Quick Start

1 . run the following command to start training, see yacs for more instructions.

python main.py --config-file configs/voc.yaml  EXPER.experiment_name voc_512 devices 0,

2 . run the following command to test

python main.py --config-file configs/voc.yaml EXPER.resume best  do_test True EXPER.experiment_name voc_512 devices 0,1,

Model Pruning

1 . training with sparse regularization

python main.py --config-file configs/voc.yaml  EXPER.experiment_name voc_512_sparse Prune.sparse True Prune.sr 0.01  

2 . Pruning and Finetune, check MobileV2 Pruning for a simplified example.

python main_prune.py --config-file configs/voc_prune.yaml  EXPER.experiment_name voc_512_sparse Prune.sparse True Prune.pruneratio 0.3   

Transfer back to Tensorflow and make it portable.

Check MNN-yolov3.

Performance on VOC2007 Test(mAP)

Model	MAP	Flops(G)	Params(M)
Yolov3	0.765	4.33	6.775
Yolov3-sparsed	0.750	4.33	6.775
Yolov3-Pruned(35% pruned)	0.746	3.00	2.815

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

Reference

Stronger-Yolo