YOLOv5 is a popular YOLO successor developed by the Ultralytics team. Thanks to its clean codebase and variety of pre-trained checkpoints, it's widely used to tackle many use cases, ranging from car detection in autonomous driving to defect detection in industrial applications.
In this tutorial, we will guide you through the steps to train your own YOLOv5 object detection algorithm on your own data. As an example, we will develop a car detector for a parking lot analytics application. Before you start, make sure you have a trainYOLO account. If not, you can create a free account here.
We start by creating a new project, which will hold our images and trained models, by clicking on the green ‘plus’ icon. We will set the project visibility to Public, the name to parking lot car detection, keep the annotation type at Bounding Boxes, and set a single category car. If you have multiple categories, you can add more.
To upload the images to our project, we go to the data tab and click on the green ‘plus’ icon. We select the images we’d like to upload and click upload. In this example, we will upload 100 images of a parking lot at different times and with different occupancy rates.
Once we have uploaded the images, we start labeling. By clicking on an image, you enter the labeling editor. By selecting the bounding box tool in the left menu, you can draw a box around each object, in our case each car. Note that if you have multiple object categories, you need to set the right category using the drop-down list on top. Once finished, you can use the arrow keys on top to go to the next image or select a specific one in the carousel at the bottom.
Once we have labeled around 50 images, we start training the first YOLOv5 model. This way, we can accelerate the labeling of the other images using model-assisted labeling.
Once you have labeled enough images, you can start training your YOLOv5 model. While you can train both locally or using cloud providers like AWS or GCP, we will use our preconfigured google Colab notebooks. Therefore, we go to the model's tab and choose the YOLOv5 notebook by clicking on the green ‘plus’ icon.
This will open our preconfigured notebook for YOLOv5 object detection. We fill in our API key (which you’ll get by clicking on your avatar next to your username) and our project name. We leave the other parameters to their default values and start the training session by clicking on “Runtime” => “Run all”. This will download your dataset, start training a model and upload the trained model to our platform.
After training, you can see the first version added to the model’s tab.
We can now use our first trained model as a labeling assistant, using so-called model-assisted labeling. Each time you add a new model (version) to your project, this latest version will be used as the labeling assistant. Instead of labeling an image from scratch, you now only need to verify or correct the model predictions. This dramatically speeds up the labeling process.
To use the labeling assistant, click on the magic paintbrush icon in the left menu. This will load the model predictions. Next, adapt or remove unwanted bounding boxes and label the ones that are not labeled.
After labeling 50 more images using the model-assisted labeling approach, we trained another version, this time on 100 images. As can be seen below, the mAP@50 increased from 0.854 to 0.986, which is already quite good.
Once you have labeled more images, you can train more versions and change the hyperparameters like image size, model type, etc. to optimize your model. Have fun training models!
