Micro-Models

Micro-models

Micro models are a subset of machine learning (ML) models that are created to be compact and light in order to facilitate deployment and maintenance. They are frequently employed in situations when the model must be deployed in a distributed fashion over a large number of devices or run on devices with limited resources, such as mobile phones or internet of things (IoT) devices.

F

The open source active learning toolkit for computer vision

Check out on GitHub

What are the benefits of using micro-models in machine learning?

Numerous machine learning (ML) applications, such as object detection, language translation, and recommendation systems, can benefit from the use of micro models. They are often trained using the same methods as bigger ML models, but are intended to be smaller and more effective, with a focus on reducing their size and processing needs.

There are several approaches that can be used to create micro models, including model compression and distillation, and using lightweight model architectures or quantization techniques. Model compression involves reducing the size of an existing model by removing redundant or unnecessary parameters or layers, while distillation involves training a smaller model to mimic the behavior of a larger model. Lightweight model architectures, such as mobile nets or tiny YOLO, are designed specifically to be small and efficient, while quantization techniques involve representing model parameters using fewer bits, which can reduce the model's size and computational requirements.

Overall, micro models are an important tool in machine learning and are frequently employed in situations when the model needs to be deployed on distributed or resource-constrained devices. They may contribute to ML's increased effectiveness and scalability.