Introduction

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

2020.6.1
(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.
2020.6.24
(1) Add pruning according to NetworkSlimming.
(2) Add distillation for higher mAP after pruning.
(3) Add Imagenet pretraining model for GhostNet.
2020.9.26
(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

Environment

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

Datasets

• Oxfordhand datasets (1 class, including human's hand)
  https://pan.baidu.com/s/1ZYKXMEvNef41MdG1NgWYiQ (extract code: 00rw)
• Visdrone Remote datasets (10 classes, including pedestrian, car, bus, etc)
  https://pan.baidu.com/s/1JzJ6APRym8K64taZgcDZfQ (extract code: xyil)
• Bdd100K datasets (10 classes, including motor, train, traffic light, etc)
  https://pan.baidu.com/s/1dBrKEdy92Mxqg-JiyrVjkQ (extract code: lm4p)
• Dior datasets (20 classes, including airplane, airport, bridge, etc)
  https://pan.baidu.com/s/1Fc-zJtHy-6iIewvsKWPDnA (extract code: k2js)

Usage

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

3. 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

4. Results:
   
   
   

Pruning and Quantization

Pruning

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

Quantization

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

TODO

• ShuffleNetV2 backbone
• HuaWei GhostNet backbone
• ImageNet pretraining
• COCO datasets training
• Other detection strategies
• Other pruning strategies