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Data Labeling is the process of assigning meaningful tags or annotations to raw data, typically in the form of text, images, audio, or video. These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions.
If you are new to this domain and wanted to learn how to label data for machine learning problems, then you’ve landed on the right page. Here we shall discuss all the essentials around data labelling. If some terminologies in the blog around Machine Learning seems unfamiliar to you, don’t worry we have the Best Data Science courses to help you out.
In the world of Supervised Machine Learning, the models train using the samples of “labelled” datasets. A labelled dataset is one in which each sample contains features, and it is respective target. While learning, the model learns a functional mapping between the above features as an input and target column as the output. The more data we feed, the better the model gets. Data labelling is the process of marking raw, unlabelled data with an accurate label which can help the model to predict the desired outcome.
For example, let us imagine we want to train a simple classifier that can detect spam emails in real time. For this we would have to create a dataset that contains several emails and categorize them into their respective category of "spam” or “not-spam”. You can check out Machine Learning course fees as well build and deploy deep learning and data visualization models in a real-world project.
You can break down the data labeling process in the below logical order:
1. Defining the Labeling Task: The first step is to determine what specific information needs to be labeled. This could involve tasks like object detection, image classification, sentiment analysis, named entity recognition, or any other type of data annotation.
2. Labeling Process: The annotators review the unlabeled data and assign the appropriate labels based on the predefined guidelines. This process may involve manual tasks such as drawing bounding boxes around objects in images, marking sentiment in text, or assigning categorical labels.
3. Quality Control: Quality control measures are implemented to ensure the accuracy and consistency of the labeled data. This can include various techniques like double-checking by multiple annotators, regular feedback sessions, or statistical analysis to identify potential errors or discrepancies.
4. Continued Iteration and Improvement: As new challenges or requirements arise, the data labeling process may need to be iterated and improved to maintain or enhance the accuracy and relevance of the labeled dataset.
Raw data can come in different forms such as text, music, images, videos etc. Depending on the type, we can make use of a variety of data labelling software. These tools are either open source - making their usage free for everyone - or we need to pay a subscription fee to use their service. Some of the popular data labelling tools are mentioned below:
V7Labs is a powerful image and video annotation tool. Apart from manual data labelling, it has a host of additional features such as model version control, workflow management model-assisted labelling, model training and inference, annotator statistics etc. By making use of its workflow management tool, you can create a fully automated data labelling pipeline. Although the platform charges a subscription fee, it has an “Education Plan” which is free of cost.
Labelbox was launched in 2018 and is one of the most popular data labelling tools for machine learning tasks. It has support for text and image annotation. You get features like AI-assisted labelling, and Python SDK for extensibility. The pricing structure for this tool allows you label first 5000 images for free and later charges are applicable based on the plan.
LabelMe is an open-source, graphical image annotation tool. It’s written in Python and was developed as a research project at MIT Computer Science and AI Lab. Since this tool is free of cost, it can be used to build image databases for computer vision research.
Data annotation majorly fall in one these 4 buckets: Categorization, Segmentation, Sequencing and Mapping. Let’s discuss each bucket in detail with an example.
In Categorization, each sample in the dataset is assigned one or more category labels. It is the most used labeling type. Let us take an example of this labeling in machine learning. Say you want to build a pet classifier. The labeled dataset would contain images that have been assigned their respective pet category such as dog, cat, fish, etc.
In Segmentation each sample in the dataset is divided into multiple segments. For example, say you want to train a model to detect pedestrians in an image. For training this model, you would have to create a dataset containing images of people walking, then manually create the outline around each pedestrian so that the model could identify each one of them individually.
In Sequencing each sample in the dataset describes the progression of items with time. An example of this labelled data in machine learning can be found when creating a text generation model. For this model the dataset would contain raw text and labelling would contain which words are occurring in the vicinity of the current word.
In Mapping labels are created by mapping one piece of data to another. Take an example of language translation models. These models require labelled dataset of pairs of sentences – one from source language, another from the target language.
As mentioned previously, raw data can come in different formats such as images, videos and text. Depending on the type of data we have different techniques of labelling them efficiently. If you want to learn about the machine learning algorithms which help us to solve tasks involving these different formats, you can refer to KnowledgeHut Machine Learning course fees as well master supervised and unsupervised learning, regression and classifications.
Data labelling for computer vision tasks can be categorized into below categories:
Natural Language Processing simply means analysis of human language and speech. Annotation tasks for NLP can be categorized into below categories:
Audio Labelling is done using the following steps:
1. Spectogram Conversion: The first step to label audio data is to create a visual representation of the input. This visual representation is called Spectogram.
2. Creating Labels: Once the spectrogram is created, we then mark the regions containing the labels.
3. Exporting Labels: Once the entire sample has been labelled we export the file which contains the start and end time of each label along with its frequency.
Synthetic Data Labelling allows companies to create synthetic datasets using machine learning methods. Algorithms like Generative Adversarial Networks (GANs) can be used for this process. GAN is semi-supervised algorithm, it is comprised of two sub-models – a “Generator” and a “Discriminator”. The Generator creates synthetic data samples, and the “Discriminator” classifies them into real / fake category.
The use of this technique substantially decreases the cost of manpower but requires significant compute resources.
Automated Data Labelling uses the principle of “Active Learning” to label large datasets. Active learning is a great alternative to manual data labelling. Imagine you’ve built an object detection model which identifies objects across several categories. You want to improve this model as the time goes by. For doing this you can use the images that the model has already classified and decide to label those images, with their respective classes, if the confidence of the model in the prediction is above a certain threshold.
A lot of companies focus on creating cutting edge AI models by using in-house datasets. The datasets are created by with the help of either of dedicated labelling teams or with help of data scientists and data engineers.
A big advantage of these technique is that it allows the companies to set strict data labelling standards and create a consistent annotation process. The companies can make selection of the data labelling platform that best fits their requirements and keep a strong check on quality.
As it might be evident, this technique can only be used by companies which have enough manpower and resources to build datasets big enough to train a model from scratch. This serves as a big disadvantage.
Crowdsourcing, as the name suggests, involves making use of a crowdsourcing platform which makes it possible to assign a task to several data labellers at once. Data labelling platforms like Amazon MTurk provides companies with fulltime access to a worldwide workforce. The biggest drawback of this technique is that it can get very difficult to maintain a consistent annotation quality as we cannot be sure who is labelling our data.
In Outsourcing the company hires data service providers that have necessary resources and manpower to label large volumes of data. This technique comes at the cost of vendor payments, but the quality of the labelled data is better than Crowdsourcing. The technique can be used by companies that cannot afford in-house labelling and doesn’t prefer the option of Crowdsourcing.
The benefits of Data Labeling are as follows:
The challenges of Data Labeling are as follows:
Creating a labelled data is crucial for building state-of-the-art Machine Learning models. We can find a lot of use cases around us for creating a labelled dataset.
Nowadays, all smartphones come with a feature of facial unlock. We place the camera in front of our face and the phone captures an image and authenticates whether the image matches with the owner’s face. Although this might appear a simple task at first, but the there are hundreds of ways to cheat this system and gain wrongful access to someone’s device. To ensure no one can bypass the checks, a labelled dataset must be created that records all the necessary facial features of the owner. The performance of our model will depend on the precision of our labels.
Self-driving vehicles are at the pinnacle of AI’s ability to replicate human intelligence. Every fraction of the second the model must predict what’s in front of the car. It takes inputs from several sensors around the car to drive the vehicle safely. Such a complex task requires millions of labelled samples of images that clearly marks all the objects in it.
Let’s now discuss the best practices for creating a labelled dataset.
In this article we have covered almost everything necessary to get started with data labelling. It should be evident by now that without data labelling, we cannot expect models to deliver outstanding performance. While there are a lot of cost factors involved in the process, efficient use of the tools and manpower can make this process streamlined and really beneficial for the problems at hand. We hope you can use the learnings of this article to create outstanding models and experience the power of using labelled datasets.
The choice of who performs data labeling depends on factors such as the complexity of the task, the required expertise, the availability of resources, and the desired level of accuracy.
An organization can use various techniques like double-checking by multiple annotators, regular feedback sessions, or statistical analysis to identify potential errors or discrepancies and ensure quality of labelled data.
Data labeling challenges include defining clear criteria, ensuring consistency, maintaining quality, handling scale and complexity, balancing speed and accuracy, and addressing privacy concerns.
Yes! We can automate the data labeling process by using “Active Learning”. This technique utilizes AI algorithms to assign labels to datasets without human intervention.
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