# 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](/dlip/deep-learning-for-perception/dp-tutorial/tutorial-yolo-in-pytorch/tutorial-yolov8-in-pytorch.md)
## Step 1. Create Project Folder
1. We will create the working space directory as
`\DLIP\YOLOv8\`
2. 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**](https://www.kaggle.com/techzizou/labeled-mask-dataset-yolo-darknet) 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:
```
0 0.8024193548387096 0.5887096774193549 0.1596774193548387 0.2557603686635945
```
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]`.
1. Download the dataset : [**Labeled Mask YOLO**](https://www.kaggle.com/techzizou/labeled-mask-dataset-yolo-darknet).
2. 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
1. Under the working space ( `YOLOv8/ )` , create the following python file ( `visualizeLabel.py`) to view images and labels.
2. Download [code here](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Pytorch/visualizeLabel.py)
```python
## Visualize B.Box and Label on Train Dataset
import cv2
image_path = 'datasets/dataset_mask/archive/obj/2-with-mask'
image = cv2.imread(image_path + '.jpg')
class_list = ['using mask', 'without mask']
colors = [(0, 255, 0), (0, 255, 255)]
height, width, _ = image.shape
T=[]
with open(image_path + '.txt', "r") as file1:
for line in file1.readlines():
split = line.split(" ")
# getting the class id
class_id = int(split[0])
color = colors[class_id]
clazz = class_list[class_id]
# getting the xywh bounding box coordinates
x, y, w, h = float(split[1]), float(split[2]), float(split[3]), float(split[4])
# re-scaling xywh to the image size
box = [int((x - 0.5*w)* width), int((y - 0.5*h) * height), int(w*width), int(h*height)]
cv2.rectangle(image, box, color, 2)
cv2.rectangle(image, (box[0], box[1] - 20), (box[0] + box[2], box[1]), color, -1)
cv2.putText(image, class_list[class_id], (box[0], box[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0))
cv2.imshow("output", image)
cv2.waitKey()
```
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](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Pytorch/split_data.py)
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.
```python
# Split Dataset as Train and Test
import os, shutil, random
# preparing the folder structure
full_data_path = 'datasets/dataset_mask/archive/obj/'
extension_allowed = '.jpg'
split_percentage = 90
images_path = 'datasets/dataset_mask/images/'
if os.path.exists(images_path):
shutil.rmtree(images_path)
os.mkdir(images_path)
labels_path = 'datasets/dataset_mask/labels/'
if os.path.exists(labels_path):
shutil.rmtree(labels_path)
os.mkdir(labels_path)
training_images_path = images_path + 'training/'
validation_images_path = images_path + 'validation/'
training_labels_path = labels_path + 'training/'
validation_labels_path = labels_path +'validation/'
os.mkdir(training_images_path)
os.mkdir(validation_images_path)
os.mkdir(training_labels_path)
os.mkdir(validation_labels_path)
files = []
ext_len = len(extension_allowed)
for r, d, f in os.walk(full_data_path):
for file in f:
if file.endswith(extension_allowed):
strip = file[0:len(file) - ext_len]
files.append(strip)
random.shuffle(files)
size = len(files)
split = int(split_percentage * size / 100)
print("copying training data")
for i in range(split):
strip = files[i]
image_file = strip + extension_allowed
src_image = full_data_path + image_file
shutil.copy(src_image, training_images_path)
annotation_file = strip + '.txt'
src_label = full_data_path + annotation_file
shutil.copy(src_label, training_labels_path)
print("copying validation data")
for i in range(split, size):
strip = files[i]
image_file = strip + extension_allowed
src_image = full_data_path + image_file
shutil.copy(src_image, validation_images_path)
annotation_file = strip + '.txt'
src_label = full_data_path + annotation_file
shutil.copy(src_label, validation_labels_path)
print("finished")
```
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](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Pytorch/maskdataset.yaml)
```python
train: ../datasets/dataset_mask/images/training/
val: ../datasets/dataset_mask/images/validation/
# number of classes
nc: 2
# class names
names: ['with mask', 'without mask']
```
## 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](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Pytorch/Yolov8_train.py)
```python
from ultralytics import YOLO
def train():
# Load a pretrained YOLO model
model = YOLO('yolov8n.pt')
# Train the model using the 'maskdataset.yaml' dataset for 3 epochs
results = model.train(data='maskdataset.yaml', epochs=3)
if __name__ == '__main__':
train()
```
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 train
>
> For 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](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Pytorch/mask-teens.jpg) 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](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Pytorch/Yolov8_test.py)
```python
from ultralytics import YOLO
import cv2
def test():
# Load a pretrained YOLO model(Change model directory)
model = YOLO('runs/detect/train4/weights/best.pt')
# Inference Source - a single source(Change directory)
src = cv2.imread("datasets/dataset_mask/images/testing/mask-teens.jpg")
# Perform object detection on an image using the model
result = model.predict(source=src, save=True, save_txt=True) # save predictions as labels
# View result
for r in result:
# print the Boxes object containing the detection bounding boxes
print(r.boxes)
# Plot results image
print("result.plot()")
dst = r.plot() # return BGR-order numpy array
cv2.imshow("result plot", dst)
# Plot the original image (NParray)
print("result.orig_img")
cv2.imshow("result orig", r.orig_img)
# Save results to disk
r.save(filename='result.jpg')
cv2.waitKey(0)
if __name__ == '__main__':
test()
```
Your result image will be saved under `runs/detect/predict#/`
## NEXT
Test trained YOLO with webcam
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