A to Z Data Annotation Guide

The purpose of data annotation  

Machine Learning  

AI includes machine learning, which enables the training of machines to carry out particular tasks. With data annotation, it can learn practically anything. Unsupervised learning, semi-supervised learning, supervised learning, and reinforcement learning is the four different categories of machine learning approaches. 

  • Supervised Learning: Using a set of labeled data, supervised learning makes discoveries. It is an algorithm that forecasts the outcome of fresh data using data that has already been labeled and is known. 
  • Unsupervised Learning: Training is done with unlabeled data in unsupervised machine learning. You are unaware of the outcome or label of the input data in this method.  
  • Semi-Supervised Learning: The AI will gain knowledge from a partially labeled dataset. This is a hybrid of the first two kinds. 
  • Reinforcement Learning: Reinforcement learning is the algorithm that helps a system determine its behavior to maximize its benefits. At the moment, it is mostly used in game theory, where algorithms must choose the next move to get the best score.   

The most common strategies are unsupervised and supervised learning, albeit there are four different varieties. According to Booz Allen Hamilton, the following picture demonstrates how supervised and unsupervised learning work: 

 

What is labeled data?  

Labeled data is a group of samples that have been tagged with one or more labels. A set of unlabeled data is typically supplemented with descriptive tags by labeling. With the help of labeled data, machine learning will “learn” the similar pattern in the input data and then predict another dataset. 

Ways to process data annotation  

Step 1: Data Collection    

The practice of obtaining and analyzing information from a wide variety of sources is known as data collection. The data we gather and keep must be organized in a way that makes sense for the particular business problem at hand in order to create viable artificial intelligence (AI) and machine learning solutions. 

There are several ways to find data. In classification algorithm cases, it is feasible to use class names to form keywords and crawl data on the internet to find photos. You can find photos, videos, and satellite images from social networking sites (COCO, ImageNet, and Google’s Open Images), free collected data from public cameras or cars (Waymo, Tesla), even you can buy data from outside sources (notice the accuracy of data).   

Some common data types are Image, Video, Text, Audio, and 3D sensor data.   

  • Image (photographs of people, objects, animals, etc.)   

The most popular type of data is probably the image. Since it deals with the most basic data type, it is crucial in a variety of applications, including robotic vision, facial recognition, and any application that has to interpret images.  

It is crucial that the raw datasets offered from various sources be annotated with metadata that includes identifiers, captions, or keywords. Data annotation can apply the key fields such as healthcare (blood cell annotation), and autonomous vehicles (traffic lights and sign annotation). AI applications can work without human intervention flawlessly with the effective and accurate annotation of images.  

The images must have metadata in the form of identifiers, captions, or keywords to train these solutions. Several use cases demand high volumes of annotated images, from self-driving cars and machines that pick and sort produce using computer vision systems, to healthcare applications that auto-identify medical conditions. Using image annotation to train these systems efficiently boosts precision and accuracy.    

  • Video (Recorded tape from CCTV or camera, usually divided into scenes)   

Video is a more sophisticated form of data than photos and requires efforts to properly annotate. Simply explained, a video is made up of several frames like images. As an illustration, a one-minute video may contain thousands of frames, it takes a lot of time.  

The fact that video annotation in artificial intelligence and machine learning models provides great insight into how an object moves and its direction. A video can also show whether or not an object is partially obscured, whereas image annotation is only capable of showing this.   

  • Text: Different types of documents, including words, and numbers in multiple languages.  

Large volumes of annotated data are used by algorithms as part of a larger data labeling workflow to train AI models. A metadata tag is used to markup features of a dataset during the annotation process. Text annotated data comprises tags that highlight criteria like words, phrases, or sentences. To train the computer to understand the meaning or emotion behind words, text annotation in some applications can also include tagging various sentiments in text, such as “angry” or “sarcastic”.  

The machine processes the annotated data, known as training data. The aim? Help the machine understand the natural language of humans (NLP). That is combined with data pre-processing and annotation.   

  • Audio: They are sound records from people having dissimilar demographics.  

According to market trends in voice AI data annotation, LTS GDS provides excellent service for annotating voice data. Annotators who are multilingual are available. 

Any audio file that has been recorded as sound can be annotated with additional keynotes and the necessary information. The Cogito annotation team is capable of looking into the audio features and offering the corpus with smart audio annotations. Our sound annotation service’s annotators attentively listen to each word in the audio in order to correctly identify the speech. 

 The listeners of an audio recording are the intended audience for various words and sentences used in the speech. It is possible to make these phrases in the audio files understandable to machines by using a special data labeling technique while annotating the audio. In NLP or NLU, machine algorithms for speech recognition need audio linguistic annotation to recognize such audio.   

  • 3D sensor data: 3D models generated by sensor devices.  

Money is a concern in any situation. The cost of 3D capable sensors can range from hundreds to thousands of dollars depending on the complex level. Selecting them over the standard camera setup is not cheap, especially since you would typically require multiple units to ensure a sufficient field of view.  

Low-resolution data  

The data collected by 3D sensors are nowhere as dense or high-resolution as the one from conventional cameras. In the case of LiDARs, a standard sensor discretizes the vertical space in lines (the number of lines varies), each having a couple of hundred detection points. This produces approximately 1000 times fewer data points than what is contained in a standard HD picture. Furthermore, the further away the object is located, the fewer samples land on it, due to the conical shape of the laser beams’ spread. As a result, it becomes exponentially harder to detect objects the farther they are from the sensor.    

Step 2: Identify the problem    

Knowing what problem helps you to decide the techniques you should use with the input data. There are some tasks in computer vision, such as:  

  • Image classification: Collect and classify the input data by tagging each image with a class label.  
  • Object detection & localization: Identify and localize the presence of objects in a picture, and indicate their location with a bounding box, point, line, or polyline.  
  • Object instance/semantic segmentation: In semantic segmentation, each pixel must be assigned to one of several classes of objects (e.g., Car, Person, Dog, etc.) and non-objects (Water, Sky, Road, etc.). Object semantic segmentation can be used with polygon and masking tools.  

Step 3: Data Annotation   

After identifying the issues, you can handle the data labeling appropriately. The labels for the categorization task are the search and data-crawling keywords. For a segmentation task, each pixel of the image should have a label. After getting the label, you need to use tools to perform image annotation (i.e. to set labels and metadata for images). The well-known tools are LabelMe, Annotorious, and Comma Coloring.   

However, this way requires human labor and takes time. Using algorithms like Polygon-RNN ++ or Deep Extreme Cut is a quicker option. To make it easier to label, Polygon-RNN ++ takes the object in the image as the input and gives the output as polygon points surrounding the object to create segments. Deep Extreme Cut operates on a similar basis to Polygon-RNN ++, however, it supports up to 4 polygons.  

Using pre-trained models on sizable datasets like ImageNet and Open Images, the “Transfer Learning” method can also be used to label data. The pre-trained models’ accuracy is fairly high because they learned many features from millions of different images. You can identify and label each object in the image using these models. It should be noted that these pre-trained models must be similar to the collected dataset to perform feature extraction or fine-turning.   

Types of data annotation  

Data Annotation is the process of labeling the training data sets, which can be images, videos, or audio. Machine Learning (ML) algorithms depend on (quality) annotated data to function, thus, AI annotation is of utmost relevance to ML.  

In our AI training projects, we use different types of annotation. Choosing what type(s) to use mainly depends on what kind of data and annotation tools you are working on.   

  • Bounding Box: A rectangular box will enclose the intended object. The data is labeled using terminology from a range of businesses, including as the automotive, security, and e-commerce sectors.  
  • Polygon: If you want a more precise result for irregular objects like human bodies, logos, or street signs, you should use polygons. The boundaries that are drawn around the objects can provide a precise picture of their shape and size, enabling the machine to anticipate outcomes more accurately. 
  • Polyline: Polylines are frequently used to reduce bounding boxes’ vulnerability because they typically have more room. It is usually used to indicate lanes on road photos.  
  • 3D Cuboids: 3D Cuboids are used to calculate the volume of various objects, including furniture, buildings, and cars.  
  • Segmentation: Segmentation is more difficult than polygons but similar to them. While polygons select few features of interest, segmentation marks the levels of related objects until every pixel in the image is marked, improving recognition results. 
  • Landmark: Landmark annotation is convenient for face and emotion recognition, human pose estimation, and body recognition. Applications using data labeled with markers can display the density of the target object in a particular scene. 

Popular tools for data annotation  

Data processing and analysis are crucial to machine learning; there are several tools for annotating data to make the process easier.  

PixelAnnotationTool – Data Annotation Tools   

This tool is useful for diagnosing difficulties with segmentation, such as detecting automobiles, roads, and cells in the medical field.   

The OpenCV watershed marking algorithm is used by this tool. Anyone can access the binary URL to download and use the tool.  

The color can be modified in the source code’s configuration file, and then let the number of colors correspond to the regions you want to segment differently. Then, all you need to use the mouse “dot” the color and press the “enter” key according to your desired color area.  

Data Generator Tool  

Text Recognition Data Generator is a tool used to generate text.  

With this tool, you can generate different fonts and colors for your text detection problem. You only need to save the cn.txt file in dicts and the font also saved in the cn directory always and run the code according to the following code:  

python run.py -l cn -c 1000 -w 1 -t 6 -k 3 -rk -b 3 -bl 1 -rbl  

To generate data according to the requirements of the problem, you should study carefully in the documentation  

Tool LabelImg  

LabelImg is also a tool to annotate data but other than Pixeltool in that LabelImg is used to take out the 4 surrounding corners. To install the tool, you can clone github or use pip.  

  • pip3 install pyqt5 lxml # Install qt and lxml by pip  
  • make qt5py3  
  • python3 labelImg.py  
  • python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]  

Who can annotate data?  

The data annotators are the ones in charge of labeling the data. There are some ways to allocate them:  

In-house  

Your team’s data scientists and AI researchers are in charge of data labeling. The benefits of this approach include ease of management and excellent accuracy. Data scientists will have to put a lot of time and effort into a manual, repetitive activity, which is such a waste of human resources.   

Outsource  

You can locate a third party – a business that offers services for data annotation. Although your team will spend less time and effort on this option, you must make sure that the business is committed to providing accurate and transparent data.  

Online tools for the workforce  

As an alternative, you may use websites like Amazon Mechanical Turk or Crowdflower that provide online labor. To perform data annotation, these systems hire online workers from all around the world. However, the factors you need to take into account while buying this service are the dataset’s organization and accuracy.    

 The guide includes essential information about data annotation. To train AI, you actually need to find experienced annotators. LTS GDS specializes in providing professional data annotation services. We promise to offer a high-quality and secure service for customers. Contact us if you want to get more information!  

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