Getting Started

In this project, we are going to have a simulation about face recognition. The whole structure is divided into 4 parts:

• The general process of working with Yolo version 8
• Working online
• Working offline and preparing the dataset from scratch
• Results

General process

Installation

YOLOv8 released a package named “ultralytics”, that you can install with the mentioned command below.

pip install ultralytics

Preparation

By cloning the data, we can have the overall structure, we can prepare this item with the following command: (of course, we will tell another method for preparation in the last part)

git clone https://github.com/SMSajadi99/Custom-Data-YOLOv8-Face-Detection.git

Start Training

You can start training YOLOv8 on custom data by using mentioned command below in the terminal/(command prompt).

yolo task=detect mode=train model=yolov8n.pt data=custom.yaml epochs=3 imgsz=640

• task = detect (It can be segment or classify)

• mode = train (It can be predict or val)

• model = yolov8n.pt (It can yolov8s/yolov8l/yolov8x)

• epochs = 3 (It can be any number)

• imgsz = 640 (It can be 320, 416, etc, but make sure it needs to be a multiple of 32)

Hint: In the project that I implemented, I implemented it with yolov8s weight. I doubt you can change this weight according to the value of your GPU. Wait for training to complete, and then do inference with newly created weights. Custom-trained weights will be saved in the folder path mentioned below. runs/train/exp/weights/best.pt

Start Test

Once your model is trained, you can use it to make predictions on new data. Use the mentioned command below for detection with custom weights.

yolo task=detect mode=predict model="runs/train/exp/weights/best.pt" source="test.png"

or

yolo task=detect mode=predict model="runs/train/exp/weights/best.pt" source="test.mp4"

Working online

To work online, open this code and execute it based on the first part.

Working offline

To prepare the data, you must download the data from the following site and place it in a folder like the following structure:

└── Dataset_Orginal
    ├── wider_face_split.zip
    ├── WIDER_test.zip
    ├── WIDER_train.zip
    └── WIDER_val.zip

Now unzip them and with this code that you have at your disposal, you can have the structure to start training. Run the following code:

python Prapertion.py

Folder structure:

After running the following code, the folder structure should be as follows: (It is clear that 3 folders train, valid and test are important.)

.
└── Dataset_Orginal
    ├── test
    │   ├── images
    │   └── labels
    ├── train
    │   ├── images
    │   └── labels
    ├── valid
    │   ├── images
    │   └── labels
    |
    .
    .
    .
    |

Now create a folder called ‍‍yolov8 and make the previous folders in the following format:

├── images
│   ├── test
│   ├── train
│   └── valid
├── labels
│   ├── train
│   └── valid

In the yolov8 folder, create a file named custom.yaml and set the following values in it: (Make sure to set the path according to your folder)

path:  /<PATH-TO>/yolov8/
train: images/train
test: images/test
val: images/valid

#Classes
names:
 0: face

Now all the items are ready and you can train and test it based on the General process section.

Hint: In the ckpts folder, I put two sample yolov8 weights based on yolov8s.pth and 25 trained epochs numbers that you can use as an evaluation.

Result

General_public_preview-1.mp4