Thanks to AI and machine learning, computer vision technology is getting upgraded with improved version of visualizing making perception through machines reliable. Actually, this is completely related computer based visual processing of objects.

What is Computer Vision in AI and Machine Learning?

Computer vision is the simply the process of perceiving the images and videos available in the digital formats. In Machine Learning (ML) and AI – Computer vision is used to train the model recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use.

The main purpose of using the computer vision technology in ML and AI is to create the a model than can work itself without human intervention. The whole process involves methods of acquiring the data, processing, analyzing and understanding the digital images to utilize the same in the real-world scenario.

How Computer Vision System Work?

You can say computer vision is used for deep learning to analyze the different types of data sets through annotated images showing object of interest in an image. It can recognize the patterns to understand the visual data feeding thousands or millions of images that have been labeled for supervised machine learning algorithms training.

Also Read: How Much Training Data is Required for Machine Learning Algorithms?

This process depends subject to use of various software techniques and algorithms, that are allowing the computers to recognize the patterns in all the elements that relate to those labels and make the model predictions accurately in future. Computer vision can be only utilize only with image processing through machine learning.

What is Difference Between Computer Vision and Image Processing?

Both are part of AI technology used while processing the data and creating a model. The difference between computer vision and image processing is Computer vision helps to gain high-level understanding from images or videos.

For instance, object recognition, which is the process of identifying the type of objects in an image, is a computer vision problem. In computer vision, you receive an image as input and you can produce an image as output or some other type of information.

Whereas, image processing doesn’t need such high level of understanding of image. In fact it is the sub-field of signal processing but also applied to images. For an example, if you have a noisy or blurred images, then under image processing the deblurring or denoising is done to make the object in the image clearly visible to machines.

Image process task involves filtering, noise removal, edge detection, and color processing. In entire processing the you receive an image as input and produce another image as an output that can be used to train the machine through computer vision.

The main difference between computer vision and image processing are the goals (not the methods used). For example, if the goal is to enhance the image quality for later use, which is called image processing. If the goal is to visualize like humans, like object recognition, defect detection or automatic driving, then it is called computer vision.

Application and Role of Computer Vision in Artificial Intelligence

The applied science of computer vision is expanding into multiple fields. From AI development to machine learning, it is playing a significant role in helping the machines identify the different types of objects in their natural environment.

From simple home task to recognizing human faces, detecting the objects in autonomous vehicle, or combating with enemies in war, computer vision the only technology giving an edge to AI-enabled devices to work efficiently.

The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flaying like drones and manufacturing etc.

Actually, to create the computer vision-based model the labeled data is required for supervised machine learning. And image annotation is the data labeling technique used for creating such labeled images for computer vision.

Cogito is one the companies providing the data annotation service for computer vision providing the image annotation solution for AI and machine learning. Rendering the high-quality training data using the best tools and techniques allowing computer vision to help algorithms train the model perform accurately in real-life use.

AI in healthcare is now playing a life-sustaining role helping people to get the accurate treatment with timely diagnosis of various types of diseases. Similarly, machine learning in healthcare is becoming more imperative covering more types of disorders in the body helping people take precautions and well-timed treatments.

Machine Learning (ML) in dentistry for dental image analysis is playing an important role to find out the conditions of teeth helping doctors to recommend the right treatment. But there is more improvements required in this sub-field of healthcare sector.

Actually, machine learning algorithms is lying under the hood of high-quality medical training data sets, and with further advances in parallel computing and augmentation of training data sets ML will improve the dental image analysis. So, here we will discuss how dental imaging can be improved with machine learning.

Availability of High-quality Training Data Sets

The most important inputs to improve the accuracy of AI-based models prediction using dental images is – a high-quality training data sets, only that can help ML algorithm recognize the patterns and store in its virtual memory to use in the real-life.

As much as quantitative and qualitative data will be used to train the model, the accuracy will improve in machine learning for dental image analysis. And the healthcare training data for such needs is created with proper labeling of object of interest using the annotation technique to highlight the affected area.

In dentistry, the affected or damaged teeth conditions are outlined in the x-ray images by experienced radiologist, once such data sets get ready, it is used in ML. The ML algorithm learn from varied types of annotated dental x-rays, and learn from such source data, that is further used to detect when shown such x-rays.         

Using the Right Machine Learning Algorithm

The machine learning algorithms can only understand and learn the training data sets and its use. If you choose the inapplicable algorithm your AI project will be failed or will give the inaccurate results, which has no room in healthcare sector.

So, train your model with efficient dental image analysis algorithms as per the availability of types of data and model validation system. And to improve the machine learning algorithm performance, a huge amount of data is required.

Also Read: How Much Training Data is Required for Machine Learning Algorithms?

Implementing the CAD and CNN Systems

CAD or Computer-aided design systems are one the with potential AI application in healthcare, helps to analyze the medical images providing the radiologist another reliable opinion as a support to enhance the accuracy of analysis.

Though, commercially available dental CAD systems are available in the market and at the same time research on efficient dental image analysis algorithms continues. And to analysis the dental illness various techniques of image annotation is used.

Semantic segmentation is one of them used for detection, classification and segmentation of objects (teeth) in dentistry. But again choosing the right algorithm is again the important aspect to improve the dental imaging analysis.

Convolutional neural networks (CNN) is one of the most successful machine learning algorithms for object recognition in machine learning. It is a variation of a multilayer artificial neural network with Convolutional layer. This algorithm is particularity efficient in extracting features, regardless of their location in the image.

And this algorithm has shown great performance in image recognition and classification. They also have solved the thought-to-be-impossible problem of facial recognition. The amount of healthcare training data is the main concerning factor for CNN.

But with the increase in the number of increasing training data images up to millions, such algorithm starts recognizing the objects with unbelievable precision. Google image search algorithm is the best example of such “data-driven” training.

Yes, off-course improvements is architecture also improve performance and accelerate the training process, but training data again remains the key factor.

State-Of-The-Art Facilities and Practice Use

To improve the performance of  machine learning in dentistry the well-sourced architectures for dental image analysis systems is required at enterprise level. As the performance of AI model can vary in terms of sensitivity and specificity depending on the task, imaging modality and algorithms used to develop such models.

Hence, state-of-the-art facility is important to build the suitable model for automated medical imaging analysis machines in dental care and treatment. Similarly, the practice of using the training data into right algorithm is also crucial improving the overall performance and accuracy of dental image analysis algorithms.

And acquiring the right training data from the right source at the right price helps to build a fully-functional AI-enabled ML model for dental imaging analysis. Cogito is the company providing data annotation services with track record of supplying the high-quality training data sets for healthcare including dental image analysis.

For machine learning dentistry, Cogito has highly-skilled and experienced radiologists to annotate to dental x-rays manually using the best software for developing the huge amount of data sets as a dental image with best level of accuracy.

Training data is the key input to machine learning (ML), and having the right quality and quantity of  data sets is important to get the accurate results. The larger the training data available for ML algorithm, it will help model to perceive the diverse types of objects making easier to recognize  when used in real-life predictions. 

But the question here is, how will you decide how much of training is enough for your machine learning. As insufficient data will affect your model prediction accuracy while more than enough data can give best results but can you manage the big data or huge quantity of datasets and it also required deep learning or more complex way to fed such data into algorithms.

Actually, there are many factors decide how much training data is required for machine learning like your model complexity, machine learning algorithms and data training or validation process. And in some cases how much data is required to demonstrate that one model is better than another. All these factors considered while choosing the right amount of datasets let we discus more elaborately to find how much data is enough of ML.

Depends on Complexities of Problem and Learning Algorithms

One of the most important factor while selecting training data for machine learning is complexity of problem means the unknown underlying function that relates to your variable inputs to the output variable as per the ML model type.

Also Read : What are the various Types of Data Sets used in Machine Learning?

Similarly, complexity of machine learning model algorithm is another important factor considered while choosing the right quantity of data sets. Actually, the algorithm used to inductively learn the unknown underlying mapping function from specific examples to make the best use of training data and integrate the same into the machine learning model.

Using the Statistical Heuristic Rule

In statistical terms there are many components considered like factor of the number of classes, factor of number of  input features and factor of the number of model parameters. And there are statistical heuristic methods available that allow you to calculate a suitable sample size.

In factor of the number of classes, there must be X independent examples for each class, where x could be tens, hundreds or thousands depending up on your parameter range. While input features there must be X% more examples than there input features and in model parameters there must be independent examples for each parameter in the model.

Model Skill vs Data Size Evaluation

While choosing the training data set for machine learning you can design s study that can evaluate model skill required against the size of training dataset. To perform this study plot the result of your model prediction, as a line plot with training dataset size on the x-axis and model skill on the y-axis that will give you an idea how much the quantity of data affects the skill of the model while solving a specific problem with machine learning.

You can use a learning curve in which you will be able to project the amount of data required to develop a skillful model or perhaps how small data you actually needed before touching an inflection point of diminishing returns. So, you can perform the study with available data and single performing algorithms like random forest and suggest you to develop a robust models in the context of well-rounded understanding of the problems.

More Data Required for Nonlinear Algorithms

Nonlinear algorithms are usually known as one the most powerful machine learning algorithms. As they are capable to learn the complex nonlinear relationships between inputs and output features. If you are using the nonlinear algorithms you need adequate amount of data sets and need to hire machine learning engineer that can work with such applied mathematics.

Also Read : How to Create Training Data for Machine Learning?

Such algorithms are often more flexible and even nonparametric means they can find out itself how many parameters are required to model your problem in addition to the values of those parameters. The predictions with such models vary based on the particular data used to train them resulting lots of data required for such model training.

Don’t Wait for More Data, Get Started what you have

It is not necessary you will get sufficient amount of training data for your ML and waiting to acquire such data for long days is not a sensible decision. Don’t let the problem of the training set size stop you from getting started on your model prediction problem solving.

Get started with the data you can, use what you have, and check how effective models are on you problem. Acquire something then take action to understand better what you have with for further analysis and then increase the data you have with augmentation or collect more data from your domain to make your model training more accurate.

Conclusion

The quality and quantity of training data is one of the most important factor machine learning engineers or data scientist are taking into the serious consideration while developing a model. However, in coming years it would become more clear how much training data is sufficient for machine learning model development but it is clear now “the more the better”. Hence, if you can acquire as much data and utilize the information it would be better for you, but waiting for bid data acquisition for longer time can delay your projects. 

Also Read : How To Hire A Good Data Scientist: Five Easy Steps

Cogito is one the companies providing the high-quality training data sets for machine learning and AI. It is involved in data collection, classification and categorization with image annotation services to provide the well-supervised training data at affordable cost. It is providing the training data for all leading sectors including healthcare, automobile, agriculture and retail sector ready to adopt the machine learning or AI-based automated systems.

Machine learning models are built with the help of data sets used at various stage of development. Actually, there are different types of data sets used on machine learning of AI-based model development like training data, validation data and test data sets.

The questions is why data is split and what are these data types. The data is split into different types training, validation and test data, and here we will discuss what are these types of data and where or how they used in various stage of machine learning development.

Why Data is Split into various types?

Developing a machine learning model is the process that extrapolate to input samples that it has never noticed before. This kind of work needs exposing the ML model to certain number of data inputs to make the output accuracy at best level. And this data inputs are split into multiple steps and each model has to go through before it used in the real-life.

#1 Data Examination by ML Model
#2 Model Learning from Mistakes
#3 Output Quality and Accuracy Check

As, you can see each step is fairly different resulting each data is treated differently at different stage of model development. Hence, we need to decide here which data in the data set is playing an important role in which stage of ML development.

Training Data Sets

This is the first stage of datasets that comprises set of input examples that the model will be fit into or used to trained the model while adjusting the various parameters like weights, height and other factor in the context of neural networks. Simply, you can say training data sets are used to train the model with data used in real-life that gathered as machine learning training data.

Validation Data Sets

The second stage is evaluating the model predictions and learn from mistakes before validating the data sets. Through evolution process, estimating the mistakes or the losses the model yields on the validation set at any given point of time. It helps to know the machine learning engineers how accurate is the model output which is very much important. It helps to tune its parameters depending on the frequent evaluation results on the validation set.

Also Read: How to Measure Quality While Training the Machine Learning Models?

Testing Data Sets

This data sets type is you can say the final evaluation that a model need to go through after the training stage in model development. This step is critical to test the final testing of model that helps to generalizability and find out the working accuracy of the model. However, every AI or machine learning engineer need to be subjective by and unbiased by not exposing the such models to the test set until the training phase is fully completed. This kind of positive approach in ML model training development is considered as the final accuracy measure to be reliable.

The machine learning model training involves looking at training examples and learning from how much model is inaccurate by evaluating through the ML model validation data sets. However, the most important or valuable indicator on the accuracy of a model is a result of testing the model on the testing set when the model training is fully completed to make sure it can work with best accuracy without showing any inaccuracy.

A right prediction is the output that expected by every developer while training a model through machine learning. And to get such desirable outputs, set of raw data needs to be layered properly with wise human decisions to train such models that can work itself take similar decisions while working on new pieces of data.

Humans are playing a key role in training such models through various forms. Image annotation is one of them, an intelligible contribution that translate the human knowledge into such form so that these models can understand and the method in which predictions by models can in turn understood by the humans.

While working with machine learning models predictions are the key role player in the entire project. And there are mainly types of model predictions adopted in machine learning. In first use case, predictions are used to partially or semi-automate the labeling process. The advantages of using the semi-automating is the cost of human labor is comparatively low and it also reduces the training time taking process into the production line.

While on the other hand, another use case model predictions is keep monitoring the model assessments and at the same time improve the accuracy of organizing the machine learning systems. In this case, each model prediction is accompanied by confidence score where a low confidence scores go through a review process done by humans.

Here the decision of humans can override the model’s prediction and resulting data feedbacks into training that very same model in production. So, here we will discuss about what are the two cases of model predictions are used while building the machine learning based models.

Types of Use Cases of Model Predictions in Machine Learning

Organizing training data sets in right manner is one of the most complicated processes into machine learning that requires a strict and disciplined curating by experts. But predictions make this notion wrong by adding a machine into labor intensive development process.

As per the normal course of action, training data machine learning takes the same form as predictions an output of model can be used to make an initial annotation of raw data in real time. And this data can be used to feed through the training data development where it can be further improved with the help of labeling team.

After reviewing and labeled by the team, the improved annotations would be used back into a model as a data set to increase the accuracy of prediction. And this kind of strict feedback loop is known as semi-automatic labeling in machine learning world.

Also Read: How to Create Training Data for Machine Learning?

SEMI-AUTOMATIC LABELING – Predictions with Manually Pre-label Data

The best part of semi-automatic labeling is that predictions are used to pre-label data and as per the research and experiments, semi-automatic labeling can outperform manual labeling across bounding boxes and polygon shapes with better results.

Though, correct prediction is also much faster than manually labeling of data but when a prediction is wrong, it is often but not always faster to correct a label rather than label it from scratch. And the types of labeling configuration can affect the predictions.

Though, if you think because a label is not faster to fix does not means that it wouldn’t be faster overall to use predictions to pre-label data. But here question arise how many times model is correct and how to calculate the benefit of time saved during the labeling.

How efficiency of predictions evaluated?

Thus, the answer for the last line in previous Para is right here. To get the answer of this question we need to evaluate the efficiency of using predictions on you own project you first need to decide how often your model is correct. And the next step is measuring the changes in labeling speed for an accurate label and incorrect label.

For an example, if you are working on a model that is correct 80% of the time. Assure here each image takes around 6 seconds to get labeled. And if the model is correct you will save about 5 seconds per label while assuming that it take 1 second to accept the correct pre-label image. However, if model is not correct you will do it at faster speed but comparatively at slower rate than when the model is completely correct.

So, right here let’s imagine 1 second is lost in every label correction that leads to an average net positive gain of 3.8 seconds per label, resulting in a cost reduction of about 60%. And it matters when you achieve state-of-the-art performance for deep learning models that often requires millions of labeled images with best quality.

QA PRODUCTION MODELS – AI-enabled Fully Automated Predictions

The fully functional machine learning application is believed to be one that makes decisions independent without human interference. In this case the labeling process is entirely automated and all predictions would be decisions and this workflow chart would compress into a single step. However, such models operating in the real are not 100% accurate every time. While AI-based applications only reduce the need of human input but not eliminate completely.

However, humans play an important role in machine learning, even after the deployment of models. Actually, data collection, training, and deployment – the three basic pillars of machine learning are not isolated stages that happen in a sequential order.

All they operate at the same time and interactively to form a sophisticated and complex workflow. And like any other mission’s critical system once the model has been installed it still needs to be maintained and updated on regular basis. And this is another scenario where predictions can fit into a big picture of building best deep learning models.

Every prediction that a model makes in a real world application is accompanied by a confidence score. And models are great at showing us how confident they are in their prediction. Hence, you can set a confidence score benchmark to define how to treat a model’s prediction in production. A prediction with a confidence score above that benchmark will be considered as final decision without human intervention whereas a prediction below the confidence benchmark will go through a quality check assurance process.

Also Read: How to Measure Quality While Training the Machine Learning Models?

Predictions facilitate a best way of conducting the quality assurance at scale. And predictions, training data and quality assurance are all rendered visually in such cases. Since the subsequent quality assurance data is also like training data that can be used back into the model to improve accuracy level in batch or on real time basis.

For an instance, suppose an insurance company uses the model in production to assess the damages automatically. Now imagine a customer sent a photo of his damaged car to insurance company that is further submitted to the model which automatically predicts the exact location and severity of damage.

Here the predictions are considered with confidence score and if the confidence score is low the model’s prediction would not considered a conclusive claim. And such doubtful claims need to routed through human check who will conduct the physical examination of damage for actual assessment of production. And such manually data collection is then fed back into the model as training data and adding such new improved training data will continue to improve the accuracy of production models and also confidence in its predictions.

Summing-up

In the whole discussion we got to know how predictions can be used to increase the speed of labeling and assure the prediction accuracy in production line. As compare to traditional labeling process where the labeling was purely done manually by humans, the predictions introduce machine-driven automation into the training data loop. And, as apposed to models in production being a purely automated process assuring quality predictions with low confidence scores are always improve and update increasingly performant models. And Cogito is the providing such labeled training data sets use into machine learning for accurate predictions.

The quality of data in training the machine learning models is one the most important factor while developing such models. The quality means here is the accuracy and consistency of labeled data. While calculating the training data, benchmarks consensus, and review are the industry standards followed by annotators. We need to figure out here what combination of these quality assurance procedures is suitable for your project.

Here in this article you will learn about the definitions of quality, consistency and accuracy and why quality matters in training the machine learning models. Here you will also get to know about the industry standard methods to quantify quality and what are the most cutting-edge tools used to automate quality assurance processes in this field.

Consistency or Accuracy, which one is important

Quality here is directly related to consistency and accuracy, this is not just how correct a data or label is but also how frequently it is correct. So, here we will discuss about how industry standard methods used for measuring consistency and accuracy. But we proceed, check below what are the advantages of quality check in machine learning training.

Benefits of Quality Check in Training the Machine Learning:

• 1. Monitor the consistency and accuracy of training data.
• 2. Quickly troubleshoot quality related errors.
• 3. Improve labeler instructions, on-boarding, and training.
• 4. Better understanding of their project on what and how to label.

Actually, consistency is the parameter labeler annotators agree with one another and helps to ensure that labels are correct or incorrect in consistent manner. The level of consistency is measured through a right algorithm and without the automation of advance AI tools this process is manual, time taking and also unsecured.

While, accuracy means how close a label is to the “Ground Truth” which also known as subset of the training data is labeled by the experts or data scientists have knowledge to test annotator accuracy. The benchmark is determined to measure the accuracy and allows data scientist to monitor the quality of data to examine and troubleshoot the quality related issues by providing the insight into accuracy of the labelers work.

Review is another method to check the accuracy and this done by trusted experts after completion of labeling. The review process is performed by spot checking the labels while few projects review all labels. Review helps to use to identify accuracy level and inconsistencies in the labeling process, whereas benchmark is often used know the performance of labeler.

Benchmarks, is considered one of the economical way of assurance option, as it involves the latest amount of overlapping work. And it can only cover the subset of the training data. While on the other hand, Consensus and Review are more expensive, but cheaper than each other depending on the consensus settings and the level of review.

QUALITY WORKFLOWS

Idyllically, quality assurance is an automated process, that works continuously, during your training data development and improvement processes. Such data labeling consensus and benchmark features, you can automate consistency and accuracy tests with amazing results. As this test allows you to customize the section of your data to test and the number of labelers that will annotate the test data with right process.

Benchmarks

This process of data quality and to follow this process of testing the quality you have to create a new Benchmark by starring an existing label. Below, as a sample project for benchmarks workflow is mentioned in which rectangular bounding box technique is used in image annotation services, though polygon and point options for more fine-tuned shapes is also available here for more fined tuned annotations. And once you have started a project and labeled the ground truth data, using the benchmark star you can tag the labels.

In this method, labelers are randomly benchmarked and you can observe the project quality with the overall quality chart. And if troubleshoot drop in quality you can explore performance by labeler or benchmark. Systemic poor labeler performance is often indication of poor instructions while poor performance on certain pieces of data is often indicative of edge cases.

For an example, if you can compare the benchmark with accuracy by clicking on benchmark to see the crowded jellyfish image and compare the same. Though, they are very similar as the labels is not agreed on how much of the tentacle to be included in each bounding box with the options to edit, delete and re-queue the label.

CONSENSUS

Consensus actually measures the rate of agreement between multiple annotators that is usually humans or machines. To calculate the consensus score divide the sum of agreeing labels by the total number of labels per asset and get the accurate score results.

The workflow of Consensus enables consensus and customize the consensus parameters and also facilitates, as random labels are distributed across labelers at random intervals. This process also keeps track of overall consistency and investigates any dips in quality by looking into individual labeler and label consensus score.

While measuring the quality with Consensus process you can customize the percentage data and number of labelers to test to monitor the consistency with Consensus histogram. To compare the labels of a particular image you can breakdown the consensus score by asset. This is a two-way part of research and development enabling AI teams to be innovating in such projects to think about the problem from various perspectives also provide the opportunity to find out the better alternative solutions.

REVIEW

To ensure the quality, getting the control of data with tight feedback loop between machines and humans enables building a functional machine learning applications for various needs. Envisaging the data is vital and not only helps in controlling the quality check but also for developing a deep understanding of a solution for machine learning data related issues.

Review Workflow

Reviewing is a manual process and become part of a human in loop. And to perform this action you have to either choose which labels to review or modify or re-enqueue labels. And the best example of reviewing is filtering that helps annotators to prioritize which labels to review. In this process filters include labeler, consensus score, label contains, and more.

Reviewers are considered the best mode of quality measurement that labelers do with their internal knowledge and experience. Along with reviewing with thumbs up or downs icons, reviewer has options to modify or correct the labels on the spot. Moreover, it also allows reviewers to delete and re-enqueue the label or view the benchmark wherever applicable with the options to copy the link to send to other reviewers for better decision.

Summing-up

While building the machine learning applications, creating a training data is one the most costly components. And consistently monitoring training data quality improves the chance of having a well-performing model even develop for the first time. While getting labels right the first time, it is cheaper than the cost of identifying and redoing work to solve such problems.

However, using the right tools and techniques can ensure your labeling maintains the level of quality you need to develop the best model and get the expected results you are seeking. Cogito is the most suitable company providing the high-quality training data for machine learning and computer vision with image annotation and data labeling service for various industries like Healthcare, Ecommerce, Automobile, Agriculture and various other sectors working on AI-based models through machine learning.