Before customizing your first machine learning model with MediaPipe Model Maker, it is important to keep some key takeaways in mind. Gathering the necessary data and annotations for proper training can be a challenge, but using existing datasets from Kaggle and annotation software can be helpful. To simplify your first custom model, focus on a single problem, use your own data, and pay attention to details such as licensing and threshold values. To be certain of your results, test the output through MediaPipe Studio first. And don’t forget that the process of training a machine learning model is iterative!
MediaPipe is a platform from Google AI that allows developers to create custom tasks using their MediaPipe Model Maker. This platform makes it possible to customize models for image classification, gesture recognition, text classification and object detection. MediaPipe also provides tools to quickly make changes in the training process and get desired results.
In addition to this, MediaPipe Studio is an important tool in the process of customizing ML models with MediaPipe Model Maker. This can help to verify that the model is correctly trained by applying different score thresholds and testing them on various images. This ensures that the model is working the way it should and that there is no system related bad performance.
Overall, when customizing a machine learning model with MediaPipe Model Maker, learning how to properly source data, annotate data, and simplify the use-case can be essential in achieving desired results. Testing the model through MediaPipe Studio and making small changes at a time will also ensure better performance.
