YOLOv1

YOLOv1

This is the implementation of "YOLOv1" for Object Detection.
Original paper: J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 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'

./YOLOv1 \
    --train true \
    --augmentation true \
    --epochs 300 \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 20 \
    --size 448 \
    --batch_size 16 \
    --prob_thresh 0.03 \
    --lr_init 1e-4 \
    --lr_base 1e-3 \
    --lr_decay1 1e-4 \
    --lr_decay2 1e-5 \
    --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'

./YOLOv1 \
    --test true \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 20 \
    --size 448 \
    --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'

./YOLOv1 \
    --detect true \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 20 \
    --size 448 \
    --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'

./YOLOv1 \
    --demo true \
    --cam_num 0 \
    --dataset ${DATA} \
    --class_list "list/${DATA}.txt" \
    --class_num 20 \
    --size 448 \
    --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 darknet, yolo_v1_pytorch, and pytorch_yolov1.