Fine-tuning in machine learning

Nowadays, the application of machine learning in various fields is becoming more and more common. One popular technique is fine-tuning, which makes it possible to use off-the-shelf models and adapt them to specific tasks. In this article, we will look at the application of fine-tuning in a vision application for live counting. We will also tell the story of Scanway programmer Milena Michalska, who shares her experience in this field.

What is fine-tuning?

Fine-tuning in the context of machine learning involves modifying (tuning) an already existing, trained model to adapt it to a new task or data set. Instead of training a model from scratch, an off-the-shelf model that has general knowledge of a certain task is used. The model is then tuned to better handle a more specific task using new data.

Application of fine-tuning in vision application for livestock counting

An example of a fine-tuning application is a vision application for counting livestock. In this case, we can use an off-the-shelf model trained on general image data, such as ImageNet, to detect and classify objects in images. However, for the specific task of counting livestock, we need to adapt this model to new data that comes directly from the application.

To do this, we perform fine-tuning on the finished model, using a collection of images from a vision application for live counting. The fine-tuning process involves freezing some layers in the model to preserve the general knowledge, and then fine-tuning the remaining layers on the new data. This allows the model to adapt to the specific characteristics of the objects present in the application, such as different animal varieties or lighting conditions.

Meeting with Scanway programmer Milena Michalska

To better understand the practical application of fine-tuning in a vision application for counting livestock, we spoke with Scanway programmer Milena Michalska. Milena has extensive experience in machine learning and using off-the-shelf models to create effective vision applications.

In a short YouTube video, Milena shares her insights on fine-tuning and demonstrates how she uses the technique in a live counting application. She talks about the process of adjusting the finished model, the required training data and optimizing the parameters for best results. Her knowledge and experience are invaluable for anyone who wants to get started with fine-tuning in machine learning.

Fine-tuning in machine learning makes it possible to use off-the-shelf models to adapt them to specific tasks or data sets. In the case of a machine vision application for livestock counting, fine-tuning allows existing models to be used, but adapted to the specific characteristics and conditions of the application.

We hope this article has given you a better understanding of fine-tuning in the context of machine learning and its application to vision applications. If you have any questions or would like to learn more, feel free to ask!