Author: Zen and the Art of Computer Programming
1 Introduction
With the rapid iteration of Internet products, user growth, fierce competition, falling software and hardware costs and many other characteristics, as well as the rapid development of AI technology, computer vision (CV) technology has become an important research hotspot in the field of artificial intelligence. In this field, model training is a very time-consuming and labor-intensive process, which requires a lot of time, energy and resources. However, how to improve the accuracy of the model, reduce the error rate and increase the running speed is the key to AI model optimization. In a production environment, model optimization work is a difficult and complex task, so how to find effective ways to improve the performance of the model is very important.
From a business perspective, traditional product optimization and upgrades often aim to improve the overall usability of the application and improve user satisfaction, such as increasing click-through rates, optimizing user interfaces, and increasing retention rates. For CV products, since the algorithm model needs to efficiently process massive data, and model training takes a long time and is expensive, the goal of optimizing the model is often not to improve the overall business indicators, but to select the most suitable model based on business characteristics. For example, for tasks such as image classification or object detection, images in certain special scenes will lead to a decrease in the accuracy of the model; and for target tracking tasks, abnormalities in the video sequence may make the model's prediction effect poor. Therefore, according to different task types and business requirements at different stages, different optimization strategies need to be designed to ensure that the performance of the model in each link is optimized. At the same time, due to the continuous updating and iteration of the model, it is necessary to pay attention to the latest progress of the model in the process of model optimization and upgrading, constantly track the latest technology of the model, and keep abreast of the cutting-edge technology.
2. Basic Concepts and Terminology
2.1 Model
The models in CV products can be divided into three categories:
Basic model: The basic model refers to the model developed by natural language processing technologies such as machine learning and deep learning, such as decision tree, neural network, support vector machine, etc. These models are generally only used in specific domains