Tutorial: Train Yolo v5 with custom dataset

This tutorial is about learning how to train YOLO v5 ~ v8 with a custom dataset of Mask-Dataset.

This Tutorial works for both YOLOv5 and YOLOv8

Step 1. Install and Configure YOLO in local drive

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:

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].

Download the dataset : Labeled Mask YOLO.
Create the folder /datasets in the same parent with the /yolov5 folder. You already have this folder if you have trained coco128 in previous tutorial.
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 label

Under the folder /datasets/ create the following python file ( visualizeLabel.py) to view images and labels. Download code here

import cv2

image_path = '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 YOLOv5 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 directory datasets/ 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

import os, shutil, random

# preparing the folder structure

full_data_path = 'dataset_mask/archive/obj/'
extension_allowed = '.jpg'
split_percentage = 90

images_path = 'dataset_mask/images/'
if os.path.exists(images_path):
    shutil.rmtree(images_path)
os.mkdir(images_path)
    
labels_path = '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 yolov5 directory with the following content. Download code here

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. Running the train

It is time to actually run the train:

python train.py --img 640 --batch 1 --epochs 2 --data maskdataset.yaml --weights yolov5s.pt

change bath number and epochs number for better training

Finally, in the end, we have the following output:

Now, confirm that you have a yolov5_ws/yolov5/runs/train/exp/weights/best.pt file:

Depending on the number of runs, it can be under /train/exp#/weights/best.pt, where #:number of exp
For my PC, it was exp3

Also, check the output of runs/train/exp/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 YOLOv5 script specially designed for this:

Download a test image here and copy the file under the folder of yolov5/data/images

Run the CLI

python detect.py --weights runs/train/exp/weights/best.pt --img 640 --conf 0.4 --source data/images/mask-teens.jpg

Your result image will be saved under runs/detect/exp

Test trained YOLO with webcam

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Last updated 1 month ago