Best Platform To Build Deep Learning Models For Computer Vision Tasks

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Crucial Computer Vision Applications:

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As the shine of computer vision begins to sparkle with each progression made in the fields of deep learning and computer vision, there are several modern computer vision projects that are in demand in the modern world. Projects such as the classification of cats and dogs, face recognition, emotion and gesture recognition, object detection, and segmentation tasks are highly renowned.

In this section of the article, we will understand and cover three major applications of computer vision that are considered to be the most significant ones, namely classification tasks, object detection, and instance segmentation. Without further ado, let us explore these basic concepts and understand an overview of the following.

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1. Classification:

Image classification is one of the most notable computer vision operations that is performed. A common classification image processing task could include something as simple as classifying between dogs or cats or classifying between numerous dog breeds. Image classification is the most basic task that we tend to perform on images or even videos. Most image classification tasks can be performed with convolutional neural networks, transfer learning models, and other similar projects.

2. Object Detection:

Object detection is one of the most vital tasks of computer vision. There have been several algorithms that have been continuously developed for decades to approach the following problem. However, it wasn’t until recently (almost a decade ago) that the task garnered more traction. The purpose of object detection in simple terms is to create bounding boxes around the desired object location.

3. Instance Segmentation:

The final significant computer vision operation that we will discuss in this section of the article is segmentation. Segmentation is used to deduce individual entities in an image. With the help of the segmentation techniques, you can segregate the essential elements accordingly. While semantic segmentation tries to identify the role of each pixel in a given particular image, instance segmentation tends to identify the labels of each class.

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