YoloV8-Training a model with Ultralytics Hub

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After uploading the roboflow Tennis Ball dataset from my previous post to an Ultralytics Hub dataset. I then used my Ultralytics Pro plan to train a proof of concept(PoC) YoloV8 model.

Creating a new Ultralytics project
Selecting training type the dataset to upload
Checking the Tennis Ball dataset upload
Confirming the number of classes and splits of the training dataset
Selecting the output model architecture (YoloV8s).
Configuring the number of epochs and payment method
Preparing the cloud instance(s) for training
The midpoint of the training process
The training process completed with some basic model metrics.
The resources used and model accuracy metrics.
Model training metrics.
Testing the trained model inference results with my test image.
Exporting the trained YoloV8 model in ONNX format.
The duration and cost of training the model.
Testing the YoloV8 model with the dem-compunet.Image console application
Marked-up image generated by the dem-compunet.Image console application.

In this post I have not covered YoloV8 model selection and tuning of the training configuration to optimise the “performance” of the model. I used the default settings and then ran the model training overnight which cost USD6.77

This post is not about how create a “good” model it is the approach I took to create a “proof of concept” model for a demonstration.

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