YOLO Multi Backbones Attention Save

Model Compression—YOLOv3 with multi lightweight backbones(ShuffleNetV2 HuaWei GhostNet), attention, prune and quantization

Project README


This Repository includes YOLOv3 with some lightweight backbones (ShuffleNetV2, GhostNet, VoVNet), some computer vision attention mechanism (SE Block, CBAM Block, ECA Block), pruning,quantization and distillation for GhostNet.

Important Update

(1) The best lightweight model——HuaWei GhostNet has been added as the YOLOv3 backbone! It is better than ShuffleNetV2. The result for visdrone dataset is as following.
(2) Add Dorefa quantization method for arbitrary bit quantization! The result for visdrone dataset is as following.
(3) And I delete the ShuffleNet and the attention mechanism.
(1) Add pruning according to NetworkSlimming.
(2) Add distillation for higher mAP after pruning.
(3) Add Imagenet pretraining model for GhostNet.
(1) Add VoVNet as the backbone. The result is excellent.

Model Params FPS mAP
GhostNet+YOLOv3 23.49M 62.5 35.1
Pruned Model+Distillation 5.81M 76.9 34.3
Pruned Model+INT8 5.81M 75.1 34
YOLOv5s 7.27M - 32.7
YOLOv5x 88.5M - 41.8
VoVNet 42.8M 28.9 42.7

Attention : Single GPU will be better
If you need previous attention model or have any question, you can add my WeChat: AutrefoisLethe


  • python 3.7
  • pytorch >= 1.1.0
  • opencv-python



  1. Download the datasets, place them in the data directory
  2. Train the models by using following command (change the model structure by changing the cfg file)
  python3 train.py --data data/visdrone.data --batch-size 16 --cfg cfg/ghost-yolov3-visdrone.cfg --img-size 640
  1. Detect objects using the trained model (place the pictures or videos in the samples directory)
  python3 detect.py --cfg cfg/ghostnet-yolov3-visdrone.cfg --weights weights/best.pt --data data/visdrone.data
  1. Results:

Pruning and Quantization


First of all, execute sparse training.

python3 train.py --data data/visdrone.data --batch-size 4 --cfg cfg/ghost-yolov3-visdrone.cfg --img-size 640 --epochs 300  --device 3 -sr --s 0.0001

Then change cfg and weights in normal_prune.py then use following command

python normal_prune.py

After obtaining pruned.cfg and corresponding weights file, you can fine-tune the pruned model via following command

python3 train.py --data data/visdrone.data --batch-size 4 --cfg pruned.cfg --img-size 640 --epochs 300  --device 3 --weights weights/xxx.weighs


If you want to quantize certain convolutional layer, you can just change the [convolutional] to [quan_convolutional] in cfg file. Then use following command

  python3 train.py --data data/visdrone.data --batch-size 16 --cfg cfg/ghostnet-yolov3-visdrone.cfg --img-size 640

Experiment Result for Changing YOLOv3 Backbone

ShuffleNetV2 + Two Scales Detection(YOLO Detector)

Using Oxfordhand datasets

Model Params Model Size mAP
ShuffleNetV2 1x 3.57M 13.89MB 51.2
ShuffleNetV2 1.5x 5.07M 19.55MB 56.4
YOLOv3-tiny 8.67M 33.1MB 60.3

Using Visdrone datasets(Incomplete training)

Model Params Model Size mAP
ShuffleNetV2 1x 3.59M 13.99MB 10.2
ShuffleNetV2 1.5x 5.09M 19.63MB 11
YOLOv3-tiny 8.69M 33.9MB 3.3

Experiment Result for Attention Mechanism

Based on YOLOv3-tiny

SE Block paper : https://arxiv.org/abs/1709.01507
CBAM Block paper : https://arxiv.org/abs/1807.06521
ECA Block paper : https://arxiv.org/abs/1910.03151

Model Params mAP
YOLOv3-tiny 8.67M 60.3
YOLOv3-tiny + SE 8.933M 62.3
YOLOv3-tiny + CBAM 8.81M 62.7
YOLOv3-tiny + ECA 8.67M 62.6


  • ShuffleNetV2 backbone
  • HuaWei GhostNet backbone
  • ImageNet pretraining
  • COCO datasets training
  • Other detection strategies
  • Other pruning strategies
Open Source Agenda is not affiliated with "YOLO Multi Backbones Attention" Project. README Source: HaloTrouvaille/YOLO-Multi-Backbones-Attention

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