Post by simranratry20244 on Feb 12, 2024 4:23:55 GMT -5
The need to have a set of data to be able to address any problem with the use of machine learning techniques is common knowledge . This data provides the basis for algorithms to learn and make predictions and is necessary to address all types of tasks or problems; whether tabular data, data in text format or images. In this post, we talk about image classification problems, which fall within the field of computer vision . In general terms, the algorithms that work best and the most widespread in image tasks are those based on convolutional neural networks (CNN) . CNNs are especially effective due to their ability to learn and detect specific patterns and features in images . These neural networks can be trained with a set of labeled images and then used to classify new images based on what they have learned.
The main problem with this type of Colombia Telemarketing Data algorithms is that they require a generally very high number of images for training, especially when the complexity of the problem to be solved is high. So, having a large and correctly labeled data source is essential, but what happens if we don't have that many images? How to generate new data? It is common that in our work as data scientists we encounter situations in which theory and reality do not correspond. Although theory provides us with a solid and informed framework for working with data, in practice there may be certain limitations that make our work difficult.
One of the biggest challenges we face is data scarcity . Sometimes there simply isn't enough data available to train models effectively. This may be due to a variety of factors, such as the nature of the problem or the inability to collect data. In other cases, the lack of data is even more extreme, reaching its total non-existence.
Despite these challenges, as data scientists, we must find creative ways to address these limitations and obtain the data necessary to perform our tasks. In the case of image classification, there are different techniques to generate new data. Techniques to generate new images To address the scarcity of data in imaging problems, various techniques have traditionally been used such as: Data augmentation techniques. It consists of applying filters or modifications to images to create others similar to the originals but different enough so that the model does not recognize that they are the same image. Some of these techniques can be rotating or transposing images, enlarging certain areas (zoom), changing colors through contrast or luminosity filters, among others.