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What is labeled data in machine learning?
In supervised learning, from autonomous vehicles, drones to AI-powered chatbots, all need labeled data. Data labeling, also known as data annotation or data classification, provides labels, tags, or classes to datasets for machine learning. Often, unlabeled data could be a collection of images, audio files, video clips, or even emails.
Crowdsourced data, data labeling services enable your machine learning models to be trained with custom labels by human labelers.
By working with human labelers, even a mix of machine and human labelers, these tags are very useful to ensure your machine learning models provide accurate results.
There are three types of learning or teaching machines in machine learning: supervised, unsupervised, and reinforced learning. Machine learning algorithms use both labeled and unlabeled data for training models.
So, which machine learning uses both labeled and unlabeled data for training?
Well, labeled data is for supervised machine learning models. Unlabeled data is for unsupervised learning models. But, reinforced learning (RL) explores autonomous possibilities, focusing on a long-term goal. It like artificial general intelligence (AGI), the ability of a machine to develop any task as humans can.
RL involves an agent learning in an unfamiliar environment based on a framework such as Markov Decision Processes (MDP), GAIuS software framework, OpenAI baselines, Keras-RL2. RL does not need a separate data collection step because training data comes from the agent’s direct interaction with the environment.
How to do data labeling in Machine Learning?
There are three main questions to consider – the who, how, and what to label images for deep learning or machine learning.
These are some of the actors who could do it:
- In-house Data Annotation Teams
- Crowdsourcing Platforms
- Outsourcing from Social Media and Job Postings
- Synthetic Labeling
How long does it take to complete data labeling for a machine learning task?
The time is directly proportional to who will label the data. Often, in-house data annotation teams take longer than crowdsourcing, which can provide faster results. Outsourcing annotators from social media job postings are easier to recruit because you can evaluate their experience and skill. Yet, outsourcing involves creating a process to qualify each annotator based on a set of necessary criteria.
Comparing quality vs. time vs. cost is vital to decide which way is more suitable for your machine learning or deep learning model. Another way could be data labeling with data programming or synthetic labeling, which requires higher computational power.
What are some of the most common data labeling tools or techniques for a machine learning task?
🍎Bounding Box: It is about rectangles and squares. Autonomous cars to drones need to recognize objects in sceneries. Image processing techniques such as bounding boxes enables to recognize objects in images. These 2D imaginary collision boxes are drawn by human labelers around the object’s x and y-axis coordinates. Thus, allowing your autonomous vehicles or drones to identify the object’s position and location.
📦3D Bounding Box: In computer vision for autonomous vehicles, 3D bounding boxes are drawn from 2D imagery expanding to detect the object’s location, position, dimensions, and direction.
😀Landmark Annotation: Landmark point annotation is an image annotation technique that uses key points. For example, for facial recognition, human labelers label faces with key points around the face dimensions and attributes to make them recognizable to computer vision models.
🚙Polygonal Annotation: Another image processing technique for data labeling is polygonal and polyline image annotation. Polyline uses precise lines and angles for illustrating the boundaries, like in highways and roads. Polygonal captures each vertex of objects’ exact edges through plot points for irregular shapes.
There are several ways to obtain labeled training data: crowdsourced, in-house data science teams for facial recognition, aerial imagery, or autonomous vehicles. The most effective way is to measure cost, time, and quality vs. your machine learning requirements. Custom image annotation can enable your machine learning models to train with custom labels based on your desired outcomes.
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