In today’s fast-paced world, efficient and accurate object detection plays a crucial role in various domains, including autonomous driving, surveillance systems, and smart cities. To address this need, I have developed a prototype for custom object detection that leverages the cutting-edge YOLOv8m model, optimized with Intel Distribution for Python, Intel…

Machine learning (ML) is a powerful tool that enables computers to learn from data and make predictions or decisions without being explicitly programmed. However, training an ML model is a complex process that requires careful monitoring and tracking of various parameters to ensure that the model is working as expected…

Deep learning has made tremendous progress in various fields such as image recognition, natural language processing, and machine translation. Many of the techniques used in modern deep learning were inspired by biological systems, such as the structure of the brain and the way it processes information. However, it is also…

Object detection is a crucial task in the field of computer vision, as it involves identifying and locating objects in an image or video. There are various object detection models that have been developed over the years, each with its own unique features and capabilities. I’ll use my experience of…

As machine learning models become more prevalent in a variety of industries, the need to serve these models in production environments has become increasingly important. Serving a machine learning model means making it available for prediction or inference, either to serve real-time predictions to users or to batch prediction for…

Techniques to tackle Class Imbalance, Multi-Task, and Dataset Size — Amongst the many problems faced during training algorithms on medical datasets, these three are most common: Class Imbalance challenge Multi-Task challenge Dataset Size challenge For each of these problems, I will share a few techniques to tackle them. So let’s start with them one by one! Class Imbalance challenge In the real world…