LTS QA

Parallel to the fast-paced development of the Artificial Intelligence and Machine Learning market, the field of data annotation is moving forward with the most accelerating trends, both in terms of tools and workflow.

From AI-Powered Virtual Assistant to Autonomous Cars, data annotation has played an important role.

Some might think that data annotation is a boring, timid and time-consuming process, while others might deem it the crucial element of artificial intelligence’s success. 

In fact, data annotation, or AI data processing, was once the most-unwanted process of implementing AI in real life. However, with the ever-growing expansion AI in multiple fields of our daily lives, the needs for rich, versatile and high-quality datasets are higher than ever. 

In order for a machine to run, in this case, is the AI system, we have to pour training data in so that the “machine” could learn to adapt to whichever is coming at it.

With these trends in the data annotation and AI data processing market, it not only sets a new outlook for the whole market, it also proves the urgent needs for well-annotated datasets.

Predictive Annotation Tools – Auto Labeling Tool

It is pretty obvious that the more fields we can apply Artificial Intelligence and Machine Learning in, the more we need AI data processing. 

By saying AI data process, we also mean that we need both the data collection and data annotation.

The rapidly expanding needs of the AI and machine learning market have set a new goal for another focus of the data annotation process. As it is with the Testing market, the demands for auto labeling, or we can call it predictive annotation tools are coming to a peak.

Auto Data Labeling

Basically, the predictive annotation tools (auto labeling tools) are the tools that can automatically detect and label items with the foundation of the similar existing manual annotations.

With the implementation of the aforementioned tools, after some manually annotated data, the toolkit can subsequently annotate the similar datasets.
Throughout this process, the human intervention is limited to the minimum amount, hence saving a lot of time and effort to do such repetitive and boring tasks.

With just some scratches on the surface, auto labeling, or predictive annotation tools may be the pivotal change that will boost up the speed of the annotation process by 80%. But to put one auto labeling tool on the market, it takes years of developing sophisticated features, not to mention a large number of data types need to be put in the data annotation system of that tool. That is why you often see one tool for only one data type.

While the advantages of an auto labeling tool are undeniable, the cost for one commercial tool like that can be enormous.

Emphasis on Quality Control

It is sure that Quality Control plays a huge role in every process. However, the current situation only shows that QC is only circumstantial. 

In the future, data engagements at scale will be the main focus, requiring a higher emphasis on quality control.

With more data labeling solutions going into production, and later into the training model of AI systems, more edge cases will be considered.

Emphasis on Quality Control

Under this circumstance, it is a must that you build your own teams of QC to exclusively handle the quality of the annotated datasets. They will not work the way the old QC staff did. On the contrary, these specialized experts can function without detailed guidelines and focus on spotting and fixing issues with large datasets.

What about security? With the security, the QC team should follow a stringent process of maintaining security of the annotation process. This should be ensured throughout the whole project.

Involvement of metadata in data annotation process

From autonomous vehicles to medical imaging, in order for the AI system to run smoothly without glitches, a staggering amount of data is required for annotation.

Metadata is the data clarifying your data. With the same old annotations as the code snippets you put in at the Java class or method level that further define data about the given code without changing any of the actual coded logic, metadata is for data management.

Metadata

All in all, metadata is created and collected for the better utility of that data.

If we can make good use of the metadata, any human errors including misplacing things, management malfunctions, etc. will be tackled. With metadata in hand, we will be able to find, use, preserve and reuse data in a more systematic manner.

• In finding data, metadata speeds up the process of finding the relevant information. Take a dataset in the form of audio for example. Without metadata and the management from it, it would be impossible to us to find the location of the data. This also applies to data types such as images and videos.
• In using the data, metadata gives us a better understanding of how the data is structured, definitions of the terms, its origin (where it was collected, etc.)
• In re-using data, metadata helps annotators navigate the data. In order to reuse data, annotators are to have careful reservation and documentation of the metadata.

The key to making all of this happen is data annotation. Adding metadata to datasets helps detect patterns and annotation helps models recognize objects.

With all the benefits of metadata in how we can manage and use the datasets, many firms now have grown interested in developing metadata for better management.

Workforce of SMEs

The rapidly growing number of the industries embracing AI, a subject-specific data annotation team is of urgent needs. 

For every domain such as healthcare, finance, automotive, etc. a team trained with custom curricular will be deployed on projects, hence expert annotators built over time. With this being done, more value and high-quality to the annotation process will be focused with a deeper approach, and this strategy will start with the validation of guidelines to time of data delivery.

Do you want to deploy these data annotation trends? Come and contact LQA for further details:

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

Data is the foundation of all the AI projects and there are different ways to prepare datasets, including collecting through the internet or consulting an agency. So, what is the best way to get raw data for the AI Data Training process?

One suggested way to collect the train and test data is to visit various open labeled resources like Google’s Open Images and mldata.org or many other websites providing datasets for training in ML projects. These platforms supply you with an endless multitude of data (mostly in the form of images) to start your training process. 

Depending on what kind of datasets you’re looking for, you can divide it into these categories of:

• Open Dataset Aggregators
• Public government Datasets for machine learning
• Machine Learning Datasets for finance & economics
• Image datasets for computer vision

For a high-quality machine learning / artificial intelligence project, datasets for training is the top priority that defines the outcome of the project. For the qualified and suitable datasets, you can consider the following filters to find the most suitable ones.

Open Dataset Aggregators

The most common thing that you might be looking for when working on machine learning / artificial intelligence is a source of free datasets. Open dataset finders that you can use to browse through a wide variety of niche-specific datasets for your data science projects. You can find it in:

• 1. Kaggle: A data science community with tools and resources which include externally contributed machine learning datasets of all kinds. From health, through sports, food, travel, education, and more, Kaggle is one of the best places to look for quality training data.
• 2. Google Dataset Search: A search engine from Google that helps researchers locate freely available online data. It works similarly to Google Scholar, and it contains over 25 million datasets. You can find here economic and financial data, as well as datasets uploaded by organizations like WHO, Statista, or Harvard.
• 3. OpenML: An online machine learning platform for sharing and organizing data with more than 21.000 datasets. It’s regularly updated and it automatically versions and analyses each dataset and annotates it with rich meta-data to streamline analysis.

Public government Datasets

For machine learning projects concerning social matters, public government datasets are very important. You can find useful datasets in these following sources:

• 4. EU Open Data Portal: The point of access to public data published by the EU institutions, agencies, and other entities. It contains data related to economics, agriculture, education, employment, climate, finance, science, etc.
• 5. World Bank: The open data from the World Bank that you can access without registration. It contains data concerning population demographics, macroeconomic data, and key indicators for development. A great source of data to perform data analysis at a large scale.

Machine Learning Datasets for finance & economics

The use of machine learning / artificial intelligence for finance & economics has long been very promising with the vast implementation in algorithmic trading, stock market predictions, portfolio management, and fraud detection. The quantity for this is very big thanks to the datasets built over many years. You can find the easily accessible datasets for finance & economics here:

• 6. Global Financial Development (GFD): An extensive dataset of financial system characteristics for 214 economies around the world. It contains annual data which has been collected since 1960.
• 7. IMF Data: International Monetary Fund publishes data related to the IMF lending, exchange rates, and other economic and financial indicators.

Image datasets for computer vision

Medical imaging, automatic cars/self-driving cars are becoming more popular these days. With the high-quality datasets of training visual data, the application of these technologies will be better than ever. You can find the sources here:

• 8. Visual Genome: A large and detailed dataset and knowledge base with captioning of over 100.000 images.
• 9. Google’s Open Images: A collection of over 9 million varied images with rich annotations. It contains image-level label annotations, object bounding boxes, object segmentation, and visual relationships across 6000 categories. This large image database is a great source of data for any data science project.
• 10. Youtube-8M: A vast dataset of millions of YouTube video IDs with high-quality machine-generated annotations of more than 3,800 visual entities. This dataset comes with pre-computed audio-visual features from billions of frames and audio segments.

Finding the suitable datasets for machine learning / AI is never easy. Besides the 4 categories mentioned above, the datasets can be Natural Language Processing Datasets, Audio Speech and Music Datasets for Machine Learning Projects, Data Visualization Datasets. You can check out other free source of datasets for machine learning with V7’s 65+ Best Free Datasets for Machine Learning.

However, the downside is that those open sources are not credible enough, so if your team accidentally gathers wrong data, your ML project will be affected badly, which reduces the level of accuracy for end-users. Also, collecting the data from unknown sources will cost you a great deal of time as it requires a lot of physical and manual labor.

So, the optimal strategy to get high-quality data for the task of labelling is to outsource to a professional vendor who has profound experience and knowledge providing data collection service to AI-based projects.

For your information, Lotus Quality Assurance is an expert at both data collection and annotation services. The datasets that Lotus Quality Assurance collects, including but not limited to images from reliable sources on the Internet, videos and sound captured and recorded with specific scenes, are provided with best quality and accuracy. 

If you have any difficulties in data collecting or data annotation for your projects, feel free to reach out to us!

Lotus Quality Assurance (LQA)

Tel: (+84) 24-6660-7474
Email: [email protected]
Website: https://www.lotus-qa.com/

“We enjoy working with LQA because of the high quality of their work and their flexibility in accommodating any new task. In the past year, we had a variety of different projects, from simple bounding box annotations to complex pixel-wise segmentation, and every time the team was able to perform the task according to the specification and within the agreed time frame. We are very impressed with the amount of effort the team put into understanding of precise requirements and making sure there are no grey areas in the task before starting the work. The work processes seem very smooth and well organised, making the interactions easy and predictable. So far LQA have been one of our best experiences when working with external annotation teams.” – Daedalean

“Daedalean (www.daedalean.ai) was founded in 2016 with an aim to specify, build, test and certify a fully autonomous sensor and autopilot system that can reliably and completely replace the human pilot. Currently the company is working with EASA on an Innovation Partnership Contract to develop concepts of design assurance for neural networks.”

If you have any difficulties in data collecting or data annotation for your projects, feel free to reach out to us!

Lotus Quality Assurance (LQA)

Tel: (+84) 24-6660-7474
Email: [email protected]
Website: https://www.lotus-qa.com/

Data Annotation is the process of labelling the training data sets, which can be images, videos or audios. Needless to say, AI Annotation is of paramount importance to Machine Learning (ML), as ML algorithms need (quality) annotated data to process. 

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: As you can guess, the target object will be framed by a rectangular box. The data labelled by using bounding boxes are used in various industries, and most used in automotive vehicle, security and e-Commerce industries. 

Polygon: When it comes to irregular shapes like human bodies, logos or street signs, to have more precise outcome, Polygons should be your choice. The boundaries drawn around the objects can give an exact idea about the shape and size, which can help the machine make better predictions.

Polyline: Polylines usually serve as a solution to reduce the weakness of bounding boxes, which usually contain unnecessary space. It is mainly used to annotate lanes on road images.

3D Cuboids: The 3D Cuboids are utilized to measure the volume of objects which can be vehicles, buildings or furniture. 

Segmentation: Segmentation is similar to polygons but more complicated. While polygons just choose some objects of interest, with segmentation, layers of alike objects are labeled until every pixel of the picture is done, which leads to better results of detection.

Landmark: Landmark annotation comes in handy for facial and emotional recognition, human pose estimation and body detection. The applications using data labeled by landmark can indicate the density of the target object within a specific scene. 

If you have any difficulties in data collecting or data annotation for your projects, feel free to reach out to us!

On 19th November, LQA was honored to join “Kanagawa business seminar” – the significant networking event with the participation of Kanagawa government officials, Kanagawa and Vietnamese businesses.

Here, LQA CEO Xuan Phung had a conversation with Mr. Yuji Kuroiwa (黒岩 祐治), the Governor of Kanagawa Prefecture, to share with him about LQA’s plan to set up a Japanese subsidiary in Kanagawa. Kanagawa is an ideal place for the establishment, as it offers a range of advantages. In geographical terms, Kanagawa’s capital city – Yokohama, which lies on Tokyo Bay, in the Kantō region of the main island of Honshu, is a major commercial hub of the Greater Tokyo Area. The prefecture’s government also gives special preference to foreign businesses. 

Mr. Yuji Kuroiwa was delighted with this plan of LQA, and he believed that this would be a great step forward for the company. The government will be willing to help LQA so that the company can develop, achieve much success in Japan.