The Computer Vision Annotation Tool (CVAT) is a platform for annotating visual data. The annotated data further can be “fed” to the deep learning models, to teach them to identify different objects in images and videos.

CVAT was developed to meet the demand for a fast and precise way to label visual data. Accurate labels are essential because they help deep learning models understand and interpret what they “see” correctly. The importance of precise labeling will be discussed further.

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CVAT comes in two versions: CVAT Cloud, which you can use online, and a Self-Hosted option, which you can install on your computer. Being open-source, CVAT is free to use, and everyone is welcome to suggest improvements or add new functionalities. 

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Originally developed by Intel, CVAT has evolved significantly thanks to numerous updates informed by feedback from its global community of users and developers. Now, CVAT.ai  operates independently as a company and as a platform behind it, offering enhanced features, and better user experience. Trusted by teams at any scale, for data of any scale.

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Through this community-driven development, CVAT continues to lead in the field of computer vision and image recognition, providing a vital tool for different industries, organizations, and personal projects.

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Glossary

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Before we dive deeper, let’s take a look into some of the terms that we use in this article.

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• Visual Data Annotation (or Data Labeling): The process of adding descriptive labels or annotations to visual data, such as images or videos.
• Deep Learning (ML) Models: Algorithms or computational systems that use data to learn and improve their performance on a task without being explicitly programmed. In this context, ML models are trained using annotated visual data to recognize and interpret images or videos.
• Labels: Descriptive tags assigned to visual data to indicate the presence of specific objects, attributes, or patterns within the data. 
• Labels  Accuracy: the correctness and reliability of the labels assigned to visual data. 
• Model Training: The process of feeding annotated data into ML algorithms to enable them to learn and improve their performance over time. During training, ML models analyze labeled examples to recognize patterns and correlations, ultimately refining their ability to make accurate predictions or classifications.

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Why Labeling Data Matters in Deep Learning

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Visual data annotation (or data labeling) is an important part of the process of teaching deep learning models how to make sense of images and videos. It's like telling the model, "This is a cat," or "That's a stop sign," so it can learn to recognize these things on its own.

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Having good, clear labels on the data is key. If the labels are wrong or messy, the ML model gets confused and makes mistakes. But when the data is labeled correctly, the model can learn accurately and work better, whether it's helping cars drive by themselves or helping doctors spot diseases in X-rays.

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Good labels also mean the model can learn faster and doesn't need as much data to get smart. This is a big help because collecting and checking all that data takes a lot of time and effort.

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So, putting the right labels on data is one of the first steps in making smart ML models that can help us in all kinds of ways.

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What are Labels

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Labels are essential for effective annotation, serving as tags or identifiers that highlight and categorize specific parts of an image or video. As we’ve mentioned before, these descriptors are crucial, as they provide the context necessary for models to interpret what they are "seeing."

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For instance, if we aim for the model to differentiate between males and females in a crowd, we start by selecting an image featuring a crowd and uploading it to CVAT. 

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Next, we add the labels "male" and "female" and proceed to annotate the image. After completing the annotation, we export and import the data into an ML model. This model examines the image, interprets the labels, and thereby understands the contents of the picture. 

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Subsequently, it learns to recognize similar objects in unannotated images and videos.

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CVAT Labeling Tools

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CVAT allows you to use different tools to label objects in images and videos: bounding boxes, shapes, and lines, etc. Each helps with different jobs.

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For example, if you're trying to keep track of cars in a video, drawing boxes around them works great. This helps the model figure out where each car is and follow it as it moves.

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But if you're looking at something complicated, like a person doing yoga and you want to understand their pose, you'll need shapes that can match their outline exactly. That's where drawing polygons comes in handy. It lets you get the outline just right, even if they're in a tricky pose.

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Sometimes, you might just need to draw a line or mark a spot. If you're looking at roads on a map, drawing lines is perfect for showing where the roads go. 

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Or, if you're studying how people move, you can just mark points on their elbows or knees to see how they bend and twist - this is called “annotation with skeletons”.

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These different tools make it super easy for models to learn from pictures and videos. By showing the computer exactly what to look at, it gets better at understanding what it sees, whether it's finding cars, understanding yoga poses, or mapping out roads.

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What is Annotation?

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Annotation is the process of adding labels to the objects on images or videos with the help of the Annotation tools. 

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Here's what the annotation with bounding boxes looks like in CVAT:

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Getting Started with CVAT

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We encourage you to try CVAT Cloud right now to understand what we are talking about better.  The basic setup involves:

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1) Creating an account, which is a quick process. 

2) Adding a task with images or videos.

3) Starting your first annotation project, using different tools.

4) The interface is designed to be user-friendly, with a clear layout that makes it easy to navigate through the various tools and options. 

5) Then export the data to see what you’ve got.

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Have any questions? Let us know through email or contacts from the community and support section. 

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Community and Support

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• GitHub issues for feature requests or bug reports. If it's a bug, please add the steps to reproduce it.
• Discord is the place to also ask questions or discuss any other stuff related to CVAT.
• YouTube to see screencast and tutorials about the CVAT.
• LinkedIn for the company and work-related questions.
• Gitter to ask CVAT usage-related questions. Typically questions get answered fast by the core team or community. 
• #cvat tag on StackOverflow is one more way to ask questions and get our support.
• contact@cvat.ai to reach out to us if you need commercial support.

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Happy annotating!

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