Data annotation is at the core of Computer Vision. It helps machines trained with machine learning algorithms see and understand. This article will begin with what data annotation is and go into detail about its two important components – object detection and classification. It will also discuss the annotation methods and give you a checklist on what to look for in a good annotation software. Having read this piece you will see how important data labeling is for machine learning.
What is Data Annotation?
Having access to machine learning training data is critical for improving AI accuracy. In machine learning, data annotation is the process of detecting raw data i.e. images, videos, text files, etc. and tagging them. Tags i.e. labels are identifiers that give meaning and context to the data. That’s what helps the machine learning model learn from it. In other words, data labeling is the process of creating training data for a visual perception model based on AI and machine learning principles.
Machine learning models, based on how they operate, fall into three categories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement learning
Most common ML models employ supervised learning and for supervised learning to work we need a labeled dataset to help the model learn based on it and make right decisions. In other words, annotated images are used to train the ML algorithm for the latter to learn and make accurate predictions.
Object Recognition and Classification
When it comes to object recognition and classification, this is where humans i.e. annotators come in, take the unlabeled, raw data and give them meaningful context i.e. labels. This is to help machines identify objects in their natural surroundings. Machines are like kids. The way you keep telling a kid what this or that object is by pinpointing those objects to them, the same way you outline the objects on an image using a data labeling software for the ML model to learn and increase the accuracy of its predictions.
These labeling tools are equipped with features designed for the annotators to outline objects on an image and classify them. You’ll notice, object recognition is not the only goal of image annotation; once the objects are outlined on an image, you need to classify them. A number of objects can be captured in an image, making it difficult for the machine to distinguish them. That’s why the objects outlined on an image should also be tagged. It’s not enough to tell a machine that there are two objects in an image, you need to also tell what each object each is. Especially if the two objects in the image have the same dimension, both object detection and classification are crucial. Take an image, for instance, of a car parked on a street next to a tree. Unless a labeler tags the objects in that picture, “car,” “tree,” “street,” the model later won’t have any information to extract from the image and hence be able to learn from it. In other words, it can’t undergo training and make predictions. That’s how the machine running on that ML algorithm learns to see and understand. Until trained with this specific process, self-driving vehicles, robots, and autonomous flying machines would be unable to identify those objects and literally bump into them.
Data Annotation Methods
There are a number of methods to structure and label data. You can have an in-house team of annotators working on your project or hire a third-party labeling service. It all depends on the size of your project, the dataset you need to have annotated, and your financial resources. If you are outsourcing your annotation services, make sure to hire a team that has knowledge of your industry. Plus, you need an annotation software.
There are a number of labeling tools out there. Some of them just provide a labeling platform for you to annotate your data on. Some provide annotation services meaning they take your data, annotate it themselves and hand to you the labeled dataset to train your model. There are also a couple that offer both the platform and the services. Which is the best tool, however, depends on your use case. However, the criteria to select an annotation tool that fits your CV project is simple. All the tools get the job done. You need the one that does it fast without compromising the annotation quality.
On most annotation platforms you can create your training data, automate annotations for predefined classes, and review existing annotations. On few other platforms you can also train, iterate, enhance, and deploy CV models. When it comes to the selection process, you need to choose that one platform that has a user-friendly interface. It’s not enough to have a rich functionality vise software unless everything it offers is visually accessible. Book a demo with a few software providers and see which one fits the criteria. This is what you need to look for in any platform along with the pretty face:
- Automation features: Does the software have integrated automation features for you to cut the annotation time. If so, what are those features? Do they allow automation both for the labeling process and quality assurance?
- Rich toolset: Check out the editor and what features it includes. Most editors include a Bounding Box and Polygon meant for outlining the objects on an image. For instance, check if they have a Rotating Box in case your data includes images requiring the feature.
- Pre-annotation: Ask if the software has a pre-annotation feature in case you need to import annotations you have done on a different tool previously.
Annotation Service Providers
In terms of annotation service marketplace, you can search and test out a few annotation teams, choose the one that fits your needs in terms of price, skills and quality. If you are running a large project, you can manage the team you are working with and track their progress, speed as well as annotation quality. This is not a common feature though. Some data annotation platforms just provide the tool. So you do need to go out there and search the teams, unless you are working on your project yourself. There are, however, annotation service providers who have both the tool and the teams to take over any project.
With this piece we covered what data annotation was, how it was used in machine learning, what methods are there and what to look for in any annotation software. In a nutshell, the labeled dataset to train your machine learning model is your ground truth. So, the accuracy of your trained model will depend on how well your dataset is labeled. The data must be labeled around features that help the ML model organize the data into patterns to have the desired outcome. The labels used to identify those features must be informative and descriptive in order to produce a quality algorithm. This is all you need to know about data labeling and what it has to do with machine learning.