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CMakeLists.txt
CMakeLists.txt
 
 
README.md
README.md
 
 

README.md

YOLOv5

This is the implementation of "YOLOv5" for Object Detection.
Reference: link

Usage

1. Build

Please build the source file according to the procedure.

$ mkdir build
$ cd build
$ cmake ..
$ make -j4
$ cd ..

2. Dataset Setting

Recommendation

  • The PASCAL Visual Object Classes Challenge 2012 (VOC2012)
    This is a set of images that has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image.
    Link: official

Setting

Please create a link for the dataset.
The following hierarchical relationships are recommended.

datasets
|--Dataset1
|    |--trainI
|    |    |--image1.png
|    |    |--image2.bmp
|    |    |--image3.jpg
|    |
|    |--trainO
|    |    |--label1.txt
|    |    |--label2.txt
|    |    |--label3.txt
|    |
|    |--validI
|    |--validO
|    |--testI
|    |--testO
|    |
|    |--detect
|         |--image4.png
|         |--image5.bmp
|         |--image6.jpg
|
|--Dataset2
|--Dataset3
  • Class List

Please set the text file for class names.

$ vi list/VOC2012.txt

In case of "VOC2012", please set as follows.

aeroplane
bicycle
bird
boat
bottle
bus
car
cat
chair
cow
diningtable
dog
horse
motorbike
person
pottedplant
sheep
sofa
train
tvmonitor
  • Input Image

You should substitute the path of training input data for "<training_input_path>", test input data for "<test_input_path>", detection input data for "<detect_path>", respectively.
The following is an example for "VOC2012".

$ cd datasets
$ mkdir VOC2012
$ cd VOC2012
$ ln -s <training_input_path> ./trainI
$ ln -s <test_input_path> ./testI
$ ln -s <detect_path> ./detect
$ cd ../..
  • Output Label

You should get the id (class number), x-coordinate center, y-coordinate center, width, and height from the class and coordinate data of bounding boxes in the XML file and normalize them.
Please follow the steps below to convert XML file to text file.
Here, you should substitute the path of training XML data for "<training_xml_path>", test XML data for "<test_xml_path>", respectively.
The following is an example for "VOC2012".

$ ln -s <training_xml_path> ./datasets/VOC2012/trainX
$ ln -s <test_xml_path> ./datasets/VOC2012/testX

Please create a text file for training data.

$ vi ../../scripts/xml2txt.sh

You should substitute the path of training XML data for "--input_dir", training text data for "--output_dir", class name list for "--class_list", respectively.

#!/bin/bash

SCRIPT_DIR=$(cd $(dirname $0); pwd)

python3 ${SCRIPT_DIR}/xml2txt.py \
    --input_dir "datasets/VOC2012/trainX" \
    --output_dir "datasets/VOC2012/trainO" \
    --class_list "list/VOC2012.txt"

The data will be converted by the following procedure.

$ sh ../../scripts/xml2txt.sh

Please create a text file for test data.

$ vi ../../scripts/xml2txt.sh

You should substitute the path of test XML data for "--input_dir", test text data for "--output_dir", class name list for "--class_list", respectively.

#!/bin/bash

SCRIPT_DIR=$(cd $(dirname $0); pwd)

python3 ${SCRIPT_DIR}/xml2txt.py \
    --input_dir "datasets/VOC2012/testX" \
    --output_dir "datasets/VOC2012/testO" \
    --class_list "list/VOC2012.txt"

The data will be converted by the following procedure.

$ sh ../../scripts/xml2txt.sh

3. Training

Setting

Please set the shell for executable file.

$ vi scripts/train.sh

The following is an example of the training phase.
If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.

#!/bin/bash

DATA='VOC2012'

./YOLOv5 \
    --train true \
    --augmentation true \
    --model "yolov5s" \
    --epochs 300 \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 20 \
    --size 640 \
    --batch_size 16 \
    --prob_thresh 0.03 \
    --gpu_id 0 \
    --nc 3

Run

Please execute the following to start the program.

$ sh scripts/train.sh

4. Test

Setting

Please set the shell for executable file.

$ vi scripts/test.sh

The following is an example of the test phase.
If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.

#!/bin/bash

DATA='VOC2012'

./YOLOv5 \
    --test true \
    --model "yolov5s" \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 20 \
    --size 640 \
    --prob_thresh 0.03 \
    --gpu_id 0 \
    --nc 3

Run

Please execute the following to start the program.

$ sh scripts/test.sh

5. Detection

Setting

Please set the shell for executable file.

$ vi scripts/detect.sh

The following is an example of the detection phase.
If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.

#!/bin/bash

DATA='VOC2012'

./YOLOv5 \
    --detect true \
    --model "yolov5s" \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 20 \
    --size 640 \
    --prob_thresh 0.03 \
    --gpu_id 0 \
    --nc 3

Run

Please execute the following to start the program.

$ sh scripts/detect.sh

6. Demo

Setting

Please set the shell for executable file.

$ vi scripts/demo.sh

The following is an example of the demo phase.
If you want to view specific examples of command line arguments, please view "src/main.cpp" or add "--help" to the argument.

#!/bin/bash

DATA='VOC2012'

./YOLOv5 \
    --demo true \
    --cam_num 0 \
    --model "yolov5s" \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 20 \
    --size 640 \
    --prob_thresh 0.03 \
    --gpu_id 0 \
    --nc 3

Run

Please execute the following to start the program.

$ sh scripts/demo.sh

Acknowledgments

This code is inspired by yolov5.

Training Strategy

Data Augmentation

Transformation of 10 components:

  • Flipping : --flip_rate 0.5
  • Scaling (i.e., Resize) : --scale_rate 0.5
  • Blurring (i.e., Applying an averaging filter) : --blur_rate 0.5
  • Change Brightness (i.e., Value in HSV) : --brightness_rate 0.5
  • Change Hue : --hue_rate 0.5
  • Change Saturation : --saturation_rate 0.5
  • Shifting : --shift_rate 0.5
  • Cropping : --crop_rate 0.5
  • Mosaic : --mosaic_rate 0.5
  • Mixup : --mixup_rate 0.5

Please write the occurrence probability in each argument rate.
Here, 1.0 means that it always occurs.

If the distributions of training and test samples are quite similar, data augmentation has undesirable effect.
In the case, it is recommended to add --augmentation false to arguments, where the default value is true.

Anchor

Anchors are useful for stable detection.
Please refer to cfg/anchor.txt to change the config.

10.0 13.0     # (scale1.anchor1) Prior-width Prior-height
16.0 30.0     # (scale1.anchor2) Prior-width Prior-height
33.0 23.0     # (scale1.anchor3) Prior-width Prior-height
30.0 61.0     # (scale2.anchor1) Prior-width Prior-height
62.0 45.0     # (scale2.anchor2) Prior-width Prior-height
59.0 119.0    # (scale2.anchor3) Prior-width Prior-height
116.0 90.0    # (scale3.anchor1) Prior-width Prior-height
156.0 198.0   # (scale3.anchor2) Prior-width Prior-height
373.0 326.0   # (scale3.anchor3) Prior-width Prior-height

Please write the prior-size, where each pixel size is 1.0 on image.
If you want to change types of anchor, please write types on --na to arguments.

Detection Strategy

Detection performance is determined by the prediction result and two threshold.

Two threshold:

  • Simultaneous probability with confidence and class score : --prob_thresh 0.1
  • IoU between bounding boxes in Non-Maximum Suppression : --nms_thresh 0.5

If you allow over-detection, please decrease prob_thresh and increase nms_thresh. (e.g., --prob_thresh 0.05, --nms_thresh 0.75)