/YOLOV3

yolov3 by pytorch

Primary LanguagePythonMIT LicenseMIT

YOLOV3

Introduction

This is my own YOLOV3 written in pytorch, and is also the first time i have reproduced a object detection model.The dataset used is PASCAL VOC. The eval tool is the voc2010. Now the mAP gains the goal score.

Subsequently, i will continue to update the code to make it more concise , and add the new and efficient tricks.

Note : Now this repository supports the model compression in the new branch model_compression

Results

name Train Dataset Val Dataset mAP(others) mAP(mine) notes
YOLOV3-448-544 2007trainval + 2012trainval 2007test 0.769 0.768 | - baseline(augument + step lr)
YOLOV3-*-544 2007trainval + 2012trainval 2007test 0.793 0.803 | - +multi-scale training
YOLOV3-*-544 2007trainval + 2012trainval 2007test 0.806 0.811 | - +focal loss(note the conf_loss in the start is lower)
YOLOV3-*-544 2007trainval + 2012trainval 2007test 0.808 0.813 | - +giou loss
YOLOV3-*-544 2007trainval + 2012trainval 2007test 0.812 0.821 | - +label smooth
YOLOV3-*-544 2007trainval + 2012trainval 2007test 0.822 0.826 | - +mixup
YOLOV3-*-544 2007trainval + 2012trainval 2007test 0.833 0.832 | 0.840 +cosine lr
YOLOV3-*-* 2007trainval + 2012trainval 2007test 0.858 0.858 | 0.860 +multi-scale test and flip, nms threshold is 0.45

Note :

  • YOLOV3-448-544 means train image size is 448 and test image size is 544. "*" means the multi-scale.
  • mAP(mine)'s format is (use_difficult mAP | no_difficult mAP).
  • In the test, the nms threshold is 0.5(except the last one) and the conf_score is 0.01.others nms threshold is 0.45(0.45 will increase the mAP)
  • Now only support the single gpu to train and test.

Environment

  • Nvida GeForce RTX 2080 Ti
  • CUDA10.0
  • CUDNN7.0
  • ubuntu 16.04
  • python 3.5
# install packages
pip3 install -r requirements.txt --user

Brief

  • Data Augment (RandomHorizontalFlip, RandomCrop, RandomAffine, Resize)
  • Step lr Schedule
  • Multi-scale Training (320 to 640)
  • focal loss
  • GIOU
  • Label smooth
  • Mixup
  • cosine lr
  • Multi-scale Test and Flip

Prepared work

1、Git clone YOLOV3 repository

git clone https://github.com/Peterisfar/YOLOV3.git

update the "PROJECT_PATH" in the params.py.

2、Download dataset

  • Download Pascal VOC dataset : VOC 2012_trainval 、VOC 2007_trainval、VOC2007_test. put them in the dir, and update the "DATA_PATH" in the params.py.
  • Convert data format : Convert the pascal voc *.xml format to custom format (Image_path0   xmin0,ymin0,xmax0,ymax0,class0   xmin1,ymin1...)
cd YOLOV3 && mkdir data
cd utils
python3 voc.py # get train_annotation.txt and test_annotation.txt in data/

3、Download weight file

Make dir weight/ in the YOLOV3 and put the weight file in.

Train

Run the following command to start training and see the details in the config/yolov3_config_voc.py

WEIGHT_PATH=weight/darknet53_448.weights

CUDA_VISIBLE_DEVICES=0 nohup python3 -u train.py --weight_path $WEIGHT_PATH --gpu_id 0 > nohup.log 2>&1 &

Notes:

  • Training steps could run the "cat nohup.log" to print the log.
  • It supports to resume training adding --resume, it will load last.pt automaticly.

Test

You should define your weight file path WEIGHT_FILE and test data's path DATA_TEST

WEIGHT_PATH=weight/best.pt
DATA_TEST=./data/test # your own images

CUDA_VISIBLE_DEVICES=0 python3 test.py --weight_path $WEIGHT_PATH --gpu_id 0 --visiual $DATA_TEST --eval

The images can be seen in the data/

TODO

  • Mish
  • OctConv
  • Custom data

Reference