YOLOv3-ModelCompression-MultidatasetTraining

This project mainly include three parts.

1.Provides training methods for multiple mainstream object detection datasets(coco2017, coco2014, BDD100k, Visdrone, Hand)

2.Provides a mainstream model compression algorithm including pruning, quantization, and knowledge distillation.

3.Provides multiple backbone for yolov3 including Darknet-YOLOv3，Tiny-YOLOv3，Mobilenetv3-YOLOv3

Source using Pytorch implementation to ultralytics/yolov3 for yolov3 source code. Pruning method based on BN layer by coldlarry/YOLOv3-complete-pruning, thanks to both of you.

If you can't download weights file and datasets from BaiDu, please send e-mail(spurslipu@pku.edu.cn) to me, I will rely as soon as I can.

Update

January 4, 2020. Provides download links and training methods to the Visdrone dataset.

January 19, 2020. Dior, Bdd100k and Visdrone training will be provided, as well as the converted weights file.

March 1, 2020. Provides Mobilenetv3 backbone.

April 7, 2020. Implement two models based on Mobilenetv3: Yolov3-Mobilenet, and Yolov3tin-Mobilene-small, provide pre-training weights, extend the normal pruning methods to the two Mobilenet-based models.

April 27, 2020. Update mobilenetv3 pre-training weights, add a layer pruning method, methods from the tanluren/yolov3-channel-and-layer-pruning/yolov3, Thanks for sharing.

May 22, 2020. Updated some new optimizations from ultralytics/yolov3, update cfg file and weights of YOLOv4.

May 22, 2020. The 8-bit quantization method was updated and some bugs were fixed.

July 12, 2020. The problem of mAP returning to 0 after pruning in yolov3-mobilenet was fixed. See issue#41 for more details.

September 30, 2020. The BN_Fold training method was updated to reduce the precision loss caused by BN fusion, and the POW (2) quantization method targeted at FPGA was updated. See the quantization section for details.

Requirements

Our project based on ultralytics/yolov3, see ultralytics/yolov3 for details. Here is a brief explanation:

numpy
torch >= 1.1.0
opencv-python
tqdm

Current support

Function
Multi-Backbone training	√
Multi-Datasets	√
Pruning	√
Quantization	√
Knowledge Distillation	√

Training

python3 train.py --data ... --cfg ... For training model command, the -pt command is required when using coco pre-training model.

python3 test.py --data ... --cfg ... For testing model command

python3 detect.py --data ... --cfg ... --source ... For detecting model command, the default address of source is data/samples, the output result is saved in the /output, and the detection resource can be pictures and videos.

Multi-Datasets

This project provides preprocessed datasets for the YOLOv3, configuration files (.cfg), dataset index files (.data), dataset category files (.names), and anchor box sizes (including 9 boxes for YOLOv3 and 6 boxes for tiny- YOLOv3) that are reclustered using the K-means algorithm.

mAP

Dataset	YOLOv3-640	YOLOv4-640	YOLOv3-mobilenet-640
Dior	0.749
bdd100k	0.543
visdrone	0.311	0.383	0.348

Datasets, download and unzip to /data.

COCO2017

Extract code：hjln
COCO2014

Extract code：rhqx
COCO weights

Extract code：k8ms

Training command

python3 train.py --data data/coco2017.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3.cfg --img-size ... --epochs ...

Dior

Extract code：vnuq
Dior weights

Extract code：l8wz

Training command

python3 train.py --data data/dior.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3-onDIOR.cfg --img-size ... --epochs ...

bdd100k

Extract code：8duw
bdd100k weights

Extract code：xeqo

Training command

python3 train.py --data data/bdd100k.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3-bdd100k.cfg --img-size ... --epochs ...

visdrone

Extract code：dy4c
YOLOv3-visdrone weights

Extract code：87lf
YOLOv4-visdrone weights

Extract code：xblu
YOLOv3-mobilenet-visdrone weights

Extract code：fb6y

Training command

python train.py --data data/visdrone.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3-visdrone.cfg  --img-size ... --epochs ...

oxfordhand

Extract code：3du4

Training command

python train.py --data data/oxfordhand.data --batch-size ... --weights weights/yolov3-608.weights -pt --cfg cfg/yolov3/yolov3-hand.cfg  --img-size ... --epochs ...

1.Dior

The DIRO dataset is one of the largest, most diverse, and publicly available object detection datasets in the Earth observation community. Among them, the number of instances of ships and vehicles is high, which achieves a good balance between small instances and large ones. The images were collected from Google Earth.

Introduction

Test results

2.bdd100k

Bdd100 is a large, diverse data set of driving videos containing 100,000 videos. Each video was about 40 seconds long, and the researchers marked bounding boxes for all 100,000 key frames of objects that often appeared on the road. The data set covers different weather conditions, including sunny, cloudy and rainy days, and different times of day and night.

Website

Download

Paper

Test results

3.Visdrone

The VisDrone2019 dataset was collected by AISKYEYE team at the Machine Learning and Data Mining Laboratory at Tianjin University, China. Benchmark data set contains 288 video clips, and consists of 261908 frames and 10209 frames a static image, by all sorts of installed on the unmanned aerial vehicle (uav) camera capture, covers a wide range of aspects, including location (thousands of kilometers apart from China in 14 different cities), environment (city and country), object (pedestrians, vehicles, bicycles, etc.) and density (sparse and crowded scenario). This data set was collected using a variety of uav platforms (i.e., uAvs with different models) in a variety of situations and under various weather and light conditions. These frames are manually marked with more than 2.6 million border frames, which are often targets of interest, such as pedestrians, cars, bicycles and tricycles. Some important attributes are also provided, including scene visibility, object categories, and occlusion, to improve data utilization.

Website

Test results of YOLOv3

Test results of YOLOv4

Multi-Backbone

Based on mobilenetv3, two network structures are designed.

Structure	backbone	Postprocessing	Parameters	GFLOPS	mAP0.5	mAP0.5:0.95	speed(inference/NMS/total)	FPS
YOLOv3	38.74M	20.39M	59.13M	117.3	0.580	0.340	12.3/1.7/14.0 ms	71.4fps
YOLOv3tiny	6.00M	2.45M	8.45M	9.9	0.347	0.168	3.5/1.8/5.3 ms	188.7fps
YOLOv3-mobilenetv3	2.84M	20.25M	23.09M	32.2	0.547	0.346	7.9/1.8/9.7 ms	103.1fps
YOLOv3tiny-mobilenetv3-small	0.92M	2.00M	2.92M	2.9	0.379	0.214	5.2/1.9/7.1 ms	140.8fps
YOLOv4	-	-	61.35M	107.1	0.650	0.438	13.5/1.8/15.3 ms	65.4fps
YOLOv4-tiny	-	-	5.78M	12.3	0.435	0.225	4.1/1.7/5.8 ms	172.4fps

YOLOv3,YOLOv3tiny and YOLOv4 were trained and tested on coco2014, and Yolov3-Mobilenetv3 and YOLOv3tiny Mobilenetv3-Small were trained and tested on coco2017.
The inference speed test on GTX2080ti*4, and image size is 608.
The training set should match the testing set, because mismatch will cause the mistakes of mAP. Read issue for detial.

Train command

1.YOLOv3

python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov3-608.weights --cfg cfg/yolov3/yolov3.cfg --img_size ...

Weights Download

COCO pretraining weights

Extract code：k8ms

2.YOLOv3tiny

python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov3tiny.weights --cfg cfg/yolov3tiny/yolov3-tiny.cfg --img_size ...

COCO pretraining weights

Extract code：udfe

3.YOLOv3tiny-mobilenet-small

python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov3tiny-mobilenet-small.weights --cfg cfg/yolov3tiny-mobilenet-small/yolov3tiny-mobilenet-small-coco.cfg --img_size ...

COCO pretraining weights

Extract code：pxz4

4.YOLOv3-mobilenet

python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov3-mobilenet.weights --cfg cfg/yolov3-mobilenet/yolov3-mobilenet-coco.cfg --img_size ...

COCO pretraining weights

Extract code：3vm8

5.YOLOv4

python3 train.py --data data/... --batch-size ... -pt --weights weights/yolov4.weights --cfg cfg/yolov4/yolov4.cfg --img_size ...

COCO pretraining weights

Extract code：njdg

Model Compression

1. Pruning

Features

method	advantage	disadvantage
Normal pruning	Not prune for shortcut layer. It has a considerable and stable compression rate but requires no fine tuning.	The compression rate is limited.
Shortcut pruning	Very high compression rate.	Fine-tuning is necessary.
Silmming	Shortcut fusion method is used to improve the precision of shear planting.	Best way for shortcut pruning
Regular pruning	Designed for hardware deployment, the number of filters after pruning is a multiple of 2, no fine-tuning, support tiny-yolov3 and Mobilenet.	Part of the compression ratio is sacrificed for regularization.
layer pruning	ResBlock is used as the basic unit for purning, which is conducive to hardware deployment.	It can only cut backbone.
layer-channel pruning	First, use channel pruning and then use layer pruning, and pruning rate was very high.	Accuracy may be affected.

Step

1.Training

python3 train.py --data ... -pt --batch-size ... --weights ... --cfg ...

2.Sparse training

--sSpecifies the sparsity factor，--pruneSpecify the sparsity type.

--prune 0 is the sparsity of normal pruning and regular pruning.

--prune 1 is the sparsity of shortcut pruning.

--prune 2 is the sparsity of layer pruning.

command：

python3 train.py --data ... -pt --batch-size 32  --weights ... --cfg ... --s 0.001 --prune 0

3.Pruning

normal pruning

python3 normal_prune.py --cfg ... --data ... --weights ... --percent ...

regular pruning

python3 regular_prune.py --cfg ... --data ... --weights ... --percent ...

shortcut pruning

python3 shortcut_prune.py --cfg ... --data ... --weights ... --percent ...

silmming

python3 slim_prune.py --cfg ... --data ... --weights ... --percent ...

layer pruning

python3 layer_prune.py --cfg ... --data ... --weights ... --shortcut ...

layer-channel pruning

python3 layer_channel_prune.py --cfg ... --data ... --weights ... --shortcut ... --percent ...

It is important to note that the cfg and weights variables in OPT need to be pointed to the cfg and weights files generated by step 2.

In addition, you can get more compression by increasing the percent value in the code. (If the sparsity is not enough and the percent value is too high, the program will report an error.)

Pruning experiment

1.normal pruning oxfordhand，img_size = 608，test on GTX2080Ti*4

model	parameter before pruning	mAP before pruning	inference time before pruning	percent	parameter after pruning	mAP after pruning	inference time after pruning
yolov3(without fine tuning)	58.67M	0.806	0.1139s	0.8	10.32M	0.802	0.0844s
yolov3-mobilenet(fine tuning)	22.75M	0.812	0.0345s	0.97	2.72M	0.795	0.0211s
yolov3tiny(fine tuning)	8.27M	0.708	0.0144s	0.5	1.13M	0.641	0.0116s

2.regular pruning oxfordhand，img_size = 608，test ong GTX2080Ti*4

model	parameter before pruning	mAP before pruning	inference time before pruning	percent	parameter after pruning	mAP after pruning	inference time after pruning
yolov3(without fine tuning)	58.67M	0.806	0.1139s	0.8	12.15M	0.805	0.0874s
yolov3-mobilenet(fine tuning)	22.75M	0.812	0.0345s	0.97	2.75M	0.803	0.0208s
yolov3tiny(fine tuning)	8.27M	0.708	0.0144s	0.5	1.82M	0.703	0.0122s

3.shortcut pruning oxfordhand，img_size = 608，test ong GTX2080Ti*4

model	parameter before pruning	mAP before pruning	inference time before pruning	percent	parameter after pruning	mAP after pruning	inference time after pruning
yolov3	58.67M	0.806		0.8	6.35M	0.816
yolov4	60.94M	0.896		0.6	13.97M	0.855

2.quantization

--quantized 2 Dorefa quantization method

python train.py --data ... --batch-size ... --weights ... --cfg ... --img-size ... --epochs ... --quantized 2

--quantized 1 Google quantization method

python train.py --data ... --batch-size ... --weights ... --cfg ... --img-size ... --epochs ... --quantized 1

--FPGA Pow(2) quantization for FPGA.

experiment

oxfordhand, yolov3, 640image-size

method	mAP
Baseline	0.847
Google8bit	0.851
Google8bit + BN Flod	0.851
Google8bit + BN Flod + FPGA	0.852
Google4bit + BN Flod + FPGA	0.842

3.Knowledge Distillation

Knowledge Distillation

The distillation method is based on the basic distillation method proposed by Hinton in 2015, and has been partially improved in combination with the detection network.

Distilling the Knowledge in a Neural Network paper

command : --t_cfg --t_weights --KDstr

--t_cfg cfg file of teacher model

--t_weights weights file of teacher model

--KDstr KD strategy

`--KDstr 1` KLloss can be obtained directly from the output of teacher network and the output of student network and added to the overall loss.
`--KDstr 2` To distinguish between box loss and class loss, the student does not learn directly from the teacher. L2 distance is calculated respectively for student, teacher and GT. When student is greater than teacher, an additional loss is added for student and GT.
`--KDstr 3` To distinguish between Boxloss and ClassLoss, the student learns directly from the teacher.
`--KDstr 4` KDloss is divided into three categories, box loss, class loss and feature loss.
`--KDstr 5` On the basis of KDstr 4, the fine-grain-mask is added into the feature

example:

python train.py --data ... --batch-size ... --weights ... --cfg ... --img-size ... --epochs ... --t_cfg ... --t_weights ...

Usually, the pre-compression model is used as the teacher model, and the post-compression model is used as the student model for distillation training to improve the mAP of student network.

experiment

oxfordhand，yolov3tiny as teacher model，normal pruning yolov3tiny as student model

teacher model	mAP of teacher model	student model	directly fine tuning	KDstr 1	KDstr 2	KDstr 3	KDstr 4(L1)	KDstr 5(L1)
yolov3tiny608	0.708	normal pruning yolov3tiny608	0.658	0.666	0.661	0.672	0.673	0.674

SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone

YOLOv3-ModelCompression-MultidatasetTraining

Update

Requirements

Current support

Training

Multi-Datasets

1.Dior

Test results

2.bdd100k

Test results

3.Visdrone

Test results of YOLOv3

Test results of YOLOv4

Multi-Backbone

Train command

Model Compression

1. Pruning

Features

Step

Pruning experiment

2.quantization

experiment

3.Knowledge Distillation

Knowledge Distillation

experiment