Data labeling is very important as it is the base for any machine learning project. These labeled data act as data set which are fed in algorithms to train various machine learning models.
Machine learning models usually needs lots of data labeling for each projects which is called training data and these labeled data should be of very high and very precise quality for a Machine Learning models to work accurately in real world scenario.
These Labeled data help AI recognize various objects, shapes and patterns. for example how tree looks like, how a mango looks like.
Hard part in data labeling is the quality of data required. You need very good experience in various annotation tools to deliver the pixel perfect labeling.
And second part is the volume. Most of the data labeling requires quick data labeling in large scale or large volume.
and third difficult part is that most of the AI and ML companies want there data labeling done in intervals or you may say some time they have big volumes of data and some time they have nothing.
Machine learning is the way Computer and Software are trained using data which makes Computer Vision model smarter and intelligent.
Machines are much faster at processing and storing knowledge compared to humans. But how can one leverage their speed to create intelligent machines? The answer to this question – make them feed on relevant data. This is also referred to as Training data.
Machine learning models are not too different from a human child. When a child observes a new object, say for example a dog and receives constant feedback from its environment, the child is able to learn this new piece of knowledge.
Machine learning technology centered on deep learning has attracted attention. Machine Learning companies have inculcated deep learning processes that requires the algorithm to identify and learn from the images fed as raw data.
Everything depends on the kind of use case you have. When you’re building your own labeled training data sets in large scale, it’s helpful to familiarize yourself with the right image annotation tool and its usage.
As it sounds like, labeler is asked to draw a box over the objects of interest based on the requirements of the data scientist. Object classification and localization models can be trained using bounding boxes.
Polygonal Segmentation
The Polygonal segmentation masks are mainly used to annotate objects with irregular shapes. Unlike boxes, which can capture a lot of unnecessary objects around the target, leading to confuse training your computer vision models, polygons are more precise when it comes to localization.
Line Annotation
The Line Annotation(a.k.a Lane Annotation), as it sounds like its used to draw lanes to train vehicle perception models for lane detection. Unlike bounding box, it avoids a lot of white space and additional noises.
Landmark Annotation
The Dot annotation (a.k.a Landmark annotation) is used to detect shape variations and count minute objects.
3D Cuboids
The 3D cuboids are used to calculate the depth/distance of the vehicle and furnitures.
Semantic Segmentation
The Semantic Segmentation(or) Pixel-level labeling is used to label each and every pixel in the image. Unlike polygonal segmentation devised specifically to detect a defined object(s) of interest, full semantic segmentation provides a complete understanding of every pixel of the scene in the image.
Machine learning works by Building ‘smart algorithms’ and present the computer with ‘enough’ real-world examples of the environment (training data), so that when the computer sees ‘similar data’, it knows what to do.
In order to stay at the top, machine learning models need to be trained on representative datasets that include all the needed all possible circumstances and possibilities
Some examples:
Traffic cameras that automatically detect lane violations.
Fitness applications that automatically log your calorie count from pictures of the food you eat. You don’t have to input the amount and type of food anymore.
Security cameras that annotate the root cause of motion sensor triggers (e.g. whether it was an animal, human, falling leaves, a car driving by, etc.) and react accordingly. It also helps decrease the frequency of false alarms.
For these Computer Vision models to work in real world with best accuracy, curated (labeled) data sets are used by ML experts to train algorithms by adjusting parameters, in order to make accurate predictions for incoming data.
Basically when you are implementing a Computer Vision, some basic steps are very necessary.
1. You need to collect lot of data
2. Label these data
3. Train the Model using Algorithm and repeat the above steps till you get the desired results.
For your Model to be accurate, Active Learning is required - In Active Learning, the data is taken, trained, tuned, tested and more data is fed back into the algorithm to make it smarter, more confident, and more accurate. This approach–especially feeding data back into a classifier is called active learning.
1 Data collection - For this you can either use free datasets or paid datasets which are available online.
2 Labeling- Once you have data with you, it can be outsourced to a good data labeling company.
You can use Services of PBS data labeling services fordata labeling.
ML and AI need humans to tag the data. It can be very difficult to find people to tag large datasets yourselves, not to mention the tooling and management necessary for it to be done efficiently. The overhead can be enormous for even small datasets.
You can annotate text, using tool like bounding box and creating the box around the area of importance. You can also highlight or color the text using various tools depending on your requirement.
Text annotations helps machines to recognize the crucial words in sentence making it more meaningful.
PBS data labeling services is been providing various text annotation services to various clients. We have worked on various text annotation for Invoice, Books, license, passport, product name and details etc.
Text Annotation is the practice and the result of adding a note or gloss to a text, which may include highlights or underlining, comments, footnotes, tags, and links.
Text annotations helps machines to recognize the crucial words in sentence making it more meaningful.
Large Annotation data are Extensively used to train autonomous driving perception models for pedestrians, traffic signs, lane obstacles, etc. For ex: Bounding boxes can be used to annotate various fashion accessories and this is used to train visual search machine learning models.
Annotation is a tedious and time-consuming work, it needs highly experienced & professional work-space to create large volumes of annotated data like pictures or images that can be used to train machines and make them functional for AI-based models.
Collecting labelled data is the key to develop good ML solutions.
In simple words Labeled data is a group of samples for example images, that have been tagged with one or more labels and the process to tag these data is called Data Labeling. Data labeling service lets machines learn what humans see, hear, or think.
Labeled or human labeled data or ground truth dataset is designed for to train specific ML models with an end application in perspective.
Labeled data is the data you need to train your models. You might just need to collect more of it to sharpen your model accuracy. As you build a great model you need great training data at scale.
How to get data labeled
We at PBS data labeling services could be a good partner in your journey just after all we annotated millions of images a day for some of the world’s most innovative companies. Whether it’s bounding boxes, dots, semantic segmentation or any sorts of shape, we can help you collect high-quality training data with high precision and recall value.
