Tutorial: Train Yolo v8 with custom dataset
This tutorial is about learning how to train YOLO v8 with a custom dataset of Mask-Dataset.
This Tutorial also works for YOLOv5
Step 0. Install YOLOv8 in local drive
Follow Tutorial: Installation of Yolov8
Step 1. Create Project Folder
We will create the working space directory as
\DLIP\YOLOv8\
Then, create the sub-folder
/datasets
in the same parent of/yolov8
folder
Step 2. Prepare Custom Dataset
Download Dataset and Label
We will use the Labeled Mask YOLO to detect people wearing mask.
This annotation file has 4 lines being each one referring to one specific face in the image. Let’s check the first line:
The first integer number (0) is the object class id. For this dataset, the class id 0 refers to the class “using mask” and the class id 1 refers to the “without mask” class. The following float numbers are the xywh
bounding box coordinates. As one can see, these coordinates are normalized to [0, 1]
.
Download the dataset : Labeled Mask YOLO.
Under the directory
/datasets
, create a new folder for the MASK dataset. Then, copy the downloaded dataset under this folder. Example:/datasets/dataset_mask/archive/obj/
The dataset is indeed a bunch of images and respective annotation files:
Visualize Train Dataset image with Boundary Box and Label
Under the working space (
YOLOv8/ )
, create the following python file (visualizeLabel.py
) to view images and labels.Download code here
You will see this result
Step 3. Split Dataset
The YOLO training process will use the training subset to actually learn how to detect objects. The validation dataset is used to check the model performance during the training.
We need to split this data into two groups for training model: training and validation.
About 90% of the images will be copied to the folder
/training/
.The remaining images (10% of the full data) will be saved in the folder
/validation/
.
For the inference dataset, you can use any images with people wearing mask.
Under the working directory create the following python file split_data.py
.
Download code here
This code will save image files under the folder /images/
folder and label data under the folder /labels/
Under each folders,
/training
and/validation
datasets will be splitted.
Run the following script and check your folders
Step 4. Training configuration file
The next step is creating a text file called maskdataset.yaml
inside the yolov8
directory with the following content.
Download code here
Step 5. Train Model
change batch number and epochs number for better training
Create the following python file ( Yolov8_train.py
) to train model.
Download code here
Finally, in the end, we have the following output:
Now, confirm that you have a yolov8/runs/detect/train/weights/best.pt
file:
Depending on the number of runs, it can be under
/train#/weights/best.pt
, where #:number of trainFor my PC, it was train3
Also, check the output of runs/detect/train#/results.png
which demonstrates the model performance indicators during the training:
Step 6. Test the model (Inference)
Now we have our model trained with the Labeled Mask dataset, it is time to get some predictions. This can be easily done using an out-of-the-box YOLOv8 script specially designed for this:
Download a test image here and copy the file under the folder of yolov8/datasets/dataset_mask/images/testing
Create the following python file ( Yolov8_test.py
) to test model.
Download code here
Your result image will be saved under runs/detect/predict#/
NEXT
Test trained YOLO with webcam
Last updated