For a computer to understand images, the training data needs to be labeled and presented in a language that the computer would eventually learn and implement by itself thus becoming artificially intelligent.The labeling methods used to generate usable training data are called Annotation techniques, or for Computer Vision, Image Annotation. The best image annotation service is provided by the Bridged Internet Inc. As reliable players for artificial intelligence and machine learning training data, they offer a range of image annotation services such as :
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.
