# 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
[Follow Tutorial: Installation of Yolov8](https://ykkim.gitbook.io/dlip/deep-learning-for-perception/dp-tutorial/tutorial-yolo-in-pytorch/tutorial-yolov8-in-pytorch)
## 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. 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.
3. 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
1. Under the folder `/datasets/` create the following python file ( `visualizeLabel.py`) to view images and labels. Download [code here](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Pytorch/visualizeLabel.py)
```python
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.
1. Under the directory `datasets/` 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
```python
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")
```
1. 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](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. 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](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Pytorch/mask-teens.jpg) and copy the file under the folder of `yolov5/data/images`
Run the CLI
```python
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`
## NEXT
Test trained YOLO with webcam