24Nov

Semantic Segmentation in Facial Recognition

Facial recognition technology is becoming a feature in our everyday lives. More and more companies are using facial recognition technology to detect and identify faces for various use cases. These include monitoring a driver’s facial expression for safe driving and unlocking smartphones, just to name a few. 

Using specific image annotation techniques e.g. semantic segmentation and landmark annotation, logical computer vision models for facial recognition are probable. These unique data labels to aid in identifying the shape and variation of objects. 

Keypoint Annotation for Facial Features Detection

Also referred to as landmark annotation, keypoint annotation is suitable for building AI-based facial recognition applications. By making high-quality keypoint annotations across different classes for pinpoint detection of facial features/attributes. 

Landmark annotation involves labeling a facial image using key points placed at specific locations on the face. This aids the model to identify the facial expression or gesture to effectively train a logical AI bases facial recognition application. Landmarking aids in determining the authentic density of an object in specific areas. 

Semantic Segmentation for Facial Recognition

Semantic segmentation is employed to produce datasets crucial to building self-driving cars and ADAS semi-autonomous cars. Also known as image segmentation, its use cases are ever-increasing given the evolving AI technology. 

At Impact Outsourcing, we offer the best data annotation services at a fraction of the total cost. By trusting us, your datasets will be of the highest quality, perfect for training logical AI/ML models. Be it in healthcare, automotive, robotics, or agriculture, Impact Outsourcing has the solutions to build your world-class AI/ML application.

24Nov

Data Annotation and its Benefits Defined

Data annotation refers to tagging/labeling data from different formats e.g. text, video, images, etc. To build a practical AI/ML application, accurately labeled data is needed so that the application can learn and understand the patterns it’s designed for.

The value of having precisely annotated data to train a computer vision-based ML model cannot be underrated. Using a wide array of data annotation methods and tools, accurate data sets for practical computer vision training are created. By using tags or added metadata, data is made more informative to AI/ML models. 

Types of Data Annotation

Depending on an AI/ML model’s algorithm (which varies depending on the sector and use case) data annotation techniques employ a variety of tools, approaches, and data labeling expertise.

Most of the training data is mainly available in text, image, and video. These different data types are labeled using different annotation techniques. In this blog, we are going to cover the different types of annotation suitable for training AI/ML models.

Bounding Box Annotation

Sometimes referred to as 2D and 3D bounding boxes, it refers to drawing rectangular lines on an image thus making it visible to a Machine Learning model. This method is perfect for training models whose use cases are in retail, agriculture, and fashion, just to name a few.

Semantic Segmentation

Also referred to as image segmentation, it involves clustering areas of an image together as belonging to the same class. A form of pixel-level prediction since every pixel in an image is grouped differently depending on the category. Semantic segmentation is mainly employed in the automotive industry and agriculture.

Keypoint/Landmark Annotation

For landmark/keypoint annotation, one must label significant points at specific points. Keypoint annotation is mainly used for gesture and facial recognition. To build a logical image recognition AI model, accurately annotated points are crucial.

Polygonal Annotation

Polygonal annotation allows you to capture more lines and angles. Polygonal annotation is basically plotting/drawing more lines to capture more angles. This annotation technique is mainly used in drone and satellite imaging technology.

LIDAR Annotation

Lidar Annotation works by assigning anatomical or structural points of interest which leads to error-free data sets that ascertain the form of different-sized objects. This enables Artificial Intelligence and Machine Learning algorithms better recognize their surroundings when deployed.

There exists a wide range of practical use cases for data annotation for computer vision. Below is a mention of a few sectors where data annotation for computer vision is strongly put to use.

  1. Autonomous Automobiles
  2. Autonomous Flying
  3. Sports and Gaming
  4. Retail
  5. Agriculture 
  6. Livestock Management
  7. Forest Management
  8. Media
  9. Security and Surveillance
  10. Robotics

Why Impact Outsourcing?

Impact Outsourcing offers annotation services be it Lidar, Semantic Segmentation, Keypoint, etc. With our professionally managed workforce headed by experienced project managers, we are well-positioned to deliver quality datasets for your AI/ML project.

18Nov

The Importance of Image Annotation Services For Artificial Intelligence and Machine Learning

Recognizing, extracting, differentiating, and comprehending data from digital mediums like pictures or videos can be characterized as an image annotation. This is one of the main building blocks in developing computer vision for image recognition in AI/ML applications. 

The use cases for computer vision vary from autonomous vehicles and medical imaging to security. As a consequence, image annotation is ever critical in building logical AI/ML models across multiple fields and sectors. 

Use Cases of Image Annotation 

By utilizing photo and video data, AI/ML models can be taught to interpret and interact with the world just like a woman would. Data annotation for Artificial Intelligence and Machine Learning is ever more crucial at this development stage. An AI/ML model’s accuracy is dependent on the quality of annotations fed into the application. Poorly annotated data will almost guarantee a flawed application and vice versa. 

Below are some use cases where image annotation is employed; 

Autonomous Vehicles 

An effective AI/ML application should correctly identify road signs, traffic lights, pavements, bike lanes, etc. Below are some of the data annotation techniques employed when building autonomous vehicle applications; 

  1. LIDAR Sensing Technology
  2. Object and Dimension Detection 
  3. Navigation with Steering Response
  4. Advanced Driver Assistance Systems (ADAS) 

Agriculture 

Agriculture has not been left out in this great AI revolution. Players in this sector can use AI/ML technologies to aid crop monitoring, reducing human participation. Agricultural AI can aid the sector in the following ways; 

  1. Livestock management
  2. Crop and plant health monitoring
  3. Detection of pests and diseases

Security and Surveillance 

The ever-increasing number of security cameras has been a critical factor in the evolution of AI/ML as a security feature. Image annotation is a necessary component for building futuristic models for crowd detection, reading face IDs for theft detection, and pedestrian tracking to name a few. 

Conclusion 

Over the years, our team has bridged boundaries and crossed time zones operating from Kenya for clients across the globe. We strive to tackle our clients’ tasks as effectively as they’d do them. Contact us today and let us power your AI/ML application by providing carefully annotated high-quality datasets.

18Nov

Frequently Employed Text Annotation Techniques For Natural Language Processing

Artificial Intelligence and Machine Learning are now part of everyday life. The consequences of these new technologies have affected how we see and interact with the world. AI and ML applications have limitless potential to radically change and drive the global economy forward. These algorithms are opening new frontiers in medicine, the arts, and finance. Natural Language Processing is at the front of all these.

Recent breakthroughs in NLP mean that people with speech impairments can conveniently communicate using automatic voice recognition software. However, to realize this technology data must be carefully annotated to train the AI models. Otherwise, all the hype around AI and ML would be wishful thinking.

To adequately train an NLP model, massive amounts of annotated texts are necessary. Below is a breakdown of the different types of text annotation tools for computer vision NLP.

Entity Text Annotation

Crucial to training chatbots, entity annotation is the foundation block for training logical NLP solutions. Recognizing, fragmenting, and annotating values is referred to as text mining.  

Entity Linking

This refers to connecting similar entities to larger data repositories. This process is crucial to creating a logical NLP computer vision model.

Sentiment Annotation

Sarcasm is one of our natural reactions as humans. When giving reviews, we sometimes opt to be sarcastic whenever confronted with a bad experience at a spa or a hotel. A poorly trained software might understand sarcasm as genuine praise when it’s the complete opposite.

To avoid this, sentiment annotation/analysis is crucial. Judging from a person’s emotion or tone, people are able to label each sentiment as either positive, negative, or neutral. 

Linguistic Annotation

This is also referred to as corpus annotation. This refers to tagging language data in both text and audio recordings. Labelers are tasked with identifying and highlighting phonetic, grammatical, and semantic features in both audio and text. 

Intent Annotation

Intent annotation is mainly employed to decipher a user’s intention. Different users have different intentions when interacting with chatbots. Some users wish to learn about their overhead charges, others want statements, etc. This annotation technique uses different labels to categorize a user’s intent.

Conclusion

With this blog, we hope you have a better understanding of text annotation and how it is employed for computer vision. Text annotation can prove to be overwhelming when faced with ever-increasing data volumes. 

To cure this, it’s always wise to outsource this non-core yet important part of AI/ML model development. 

At Impact Outsourcing, we provide a professionally managed workforce backed by years of experience in data annotation. Contact us today and let us power your next NLP computer vision model.

19Oct

Outsourcing Image Annotation: A How-To Guide

The real-life application of facial recognition for security, autonomous vehicles, and even robot assistants is no longer restricted to the sci-fi movie realm. These life-altering technologies are already here and they are bound to shape our future in a major way. Computer vision AI applications are ever leading us in this direction. 

To actualize successful AI and ML applications, models rely on accurately labeled/tagged data. For instance, in order to build a computer vision application, massive loads of visual data must be annotated and fed into the model. This is what is referred to as image annotation. This human-powered task of labeling images can be tedious, overly expensive, and time-consuming.

Employing an in-house data annotation team can be a monotonous task that comes with its own set of challenges. As a consequence, we find that many businesses prefer to outsource some if not all of their data training needs. These include; image annotation, data collection, data validation, live project monitoring, etc.

Advantages of Outsourcing Data Annotation

Scalability

With a reliable image annotation outsourcing team, you rid yourself of the constraints that come with data volume upheavals. One can easily ask the outsourcing firm to scale up or down depending on your current needs.

Expertise

Data labeling companies come with a breadth of experience that places them in a unique position. They can better advise on the right talent, tools, and approach that fits your project. 

Saves Time

Data labeling and collection consume a huge amount of time and it takes even longer to train a team to do the job. By partnering with an experienced outsourcing company, the task of recruiting and training the team is passed on to them. This frees your time which can be better utilized in other aspects of running your company.

There are some very important points to go through before settling on a data annotation outsourcing partner. With the ever-increasing number of image annotation outsourcing companies, choosing the right fit can be a daunting task. 

Follow these steps to find your way through the murk.

Step 1: Realize your needs

For every computer vision application or model, there is a specific annotation technique to actualize it. You must first determine what your AI model use case is and the problems it intends to solve.

Below are some questions to ask yourself when selecting the right vendor.

  • What sort of data are you operating?
  • What sort of image annotation fits your project? (text annotation, image annotation, video annotation, etc)
  • What is your budget?
  • How do you determine project efficiency?

Being knowledgeable about your needs places you on a solid footing to effectively pass on your requirements to potential partners.

Step 2: Go for the right vendor

Selecting the right partner can make or break your AI/ML project. Below are some questions to help you select the right outsourcing partner. 

 

  • Industry Knowledge and Experience – Given the different types of annotation (image, video, text, etc), annotating data can vary depending on the type of annotation needed. Let’s say your AI model requires video annotation, be careful to select an outsourcing company with relative experience on the same before committing.

 

  • What platforms/tools do they employ – There are many annotation tools and platforms out there in the market. It is important to interrogate every potential outsourcing partner’s knowledge on this as they can advise on the best tool that meets your needs. 

 

  • Are they committed to ethical AI and Social Impact – Since you are basically offshoring your work, you want to ensure that you are making the most positive impact on the people that handle your project. Enquire on how annotators are remunerated and their overall benefits. From experience, most outsourcing companies are happy to share this information with a potential partner. 

 

Step 3: Monitor and Manage Expectations

To ensure the success of outsourcing data annotation, proper quality assurance is paramount. The outsourcing company must have layers upon layers of quality checks to guarantee high-quality datasets.

Measure the vendor’s ability to produce high-quality datasets by posing questions like:

 

  • Project Trial – Most outsourcing companies offer a free trial for clients to measure their quality and overall professionalism. Before committing to anything long-term, first, send the potential vendor a sample of the expected work and judge their output. If the quality satisfies your needs, then you can proceed to partner.

 

  • The number of Annotators/Capacity – This is important to ask for when you want to scale your team. You don’t want to commit to a vendor who can only commit a small number of annotators. Equally important, always go for the vendor who can easily scale down the team when the circumstance calls for it.

 

  • Pricing – It’s important to find out the most suitable pricing model for a successful partnership. This can be on a per-hour basis or per task/image. Depending on which one suits you best, always make it clear to the potential vendor.

Impact Outsourcing prides itself on providing humans in the loop, crucial for actualizing Artificial Intelligence and Machine Learning. We seek to create long-term meaningful employment for thousands of marginalized youth and women through data annotation jobs. With our years of experience in data collection, data curation, data labeling, and live project monitoring, we have birthed a quality-first attitude to project management. Try us today and we’d be happy to be your number-one outsourcing partner.

15Jul

Data Annotation In Agriculture: Using Artificial Intelligence and Machine Learning On Agricultural Data

In this new and modern digital age, artificial intelligence is being incorporated into vital human activities creating more efficiency and enhanced productivity. Not to be left behind, the agricultural sector has made great strides through AI-powered computer vision models in crop monitoring and production.

The role of AI robots, drones, and other automated machines is becoming ever more vital in planting, health monitoring, harvesting, and enhancing crop productivity. But have you ever sat back and asked yourself how these AI-powered machines aid in meticulous agriculture and farming? 

Surprisingly, AI-powered computer models are made possible through computer vision technology. To achieve this, the AI models are thoroughly trained using annotated/labeled images fed to them using the correct machine learning algorithms.

Image Labeling for Machine Learning in Agriculture

Image labeling services in the field of agriculture aids in performing different actions e.g. identifying crops, fruits, vegetables, or weeds. Once enough annotated data is uploaded onto the deep learning algorithm, an AI-powered computer model becomes intelligent enough to predict and perform human functions like sorting fruits and monitoring crop health.

Through the data labeling process, image and data annotation is playing an ever-increasing role in applying artificial intelligence and machine learning to agricultural data. Below are some of the ways in which image annotation is applied to machine learning for use in agriculture. 

Precise Agriculture using Robots

Robots are steadily emerging as the number one preference for farmers on farm fields. In agriculture, AI-powered robots are performing different actions with aid from machine vision algorithms. These robots can perform actions such as; plowing, planting, weed detection, and monitoring crop productivity and overall health. They also aid in picking fruits and vegetables, sorting, and packaging them accordingly. In addition to this, robots use computer vision cameras to the group and classify different farm produce faster and with improved accuracy.

With the aid of deep learning algorithms, it is easy for an AI-powered robot to identify faults from multiple angles using color and geometrical variations. The deep learning algorithms work by first identifying and locating the fruits and then moving to classify them appropriately.

In order to equip AI-powered algorithms, accurately annotated images of plants, crops, and floras are fed into the models. Through bounding box annotation services, AI-powered robots are trained to recognize and detect different crops, weeds, fruits, and vegetables. 

Sorting Fruits and Vegetables

Once all the fruits and vegetables have been collected, the task of sorting is done by AI-powered robots to separate the healthy from the rotten fruits and vegetables. Through the use of training data, accurately labeled images based on deep learning are used to sort and grade farm produce. 

Likewise, AI robots are able to sort flowers, stems, and buds of various breeds, shapes, and sizes. These models are trained to be at par with the strict international rules and standards of the different crops, fruits, and flower markets. 

Monitoring Soil, Crop, and Animal Health

Through the application of Geosensing technology, drones and other autonomous flying objects are able to determine the health condition of both crops and soil. This aids in informing farmers on what is the right time for sowing and what actions ought to be taken to save vulnerable crops. To maximize crop yields, the correct soil conditions and timely insecticides are key. In addition, AI-powered technology makes it easier to highlight the health of both crops and animals.

 

The health of animals is typically given by veterinarians. This is due to the fact that animal health often dictates the animal’s reproductive health, milk production, and feed intake making livestock rearing ever more profitable.

Crop Yield Forecasting Using Deep Learning

By applying AI in agriculture using deep learning data sets, predicting the expected crop yield using smart devices to analyze the data. 

Developing deep learning platforms needs in-depth knowledge to facilitate the training of accurate and reliable predictions. To train these algorithms, ample amounts of accurately annotated data are needed to develop such computer models.

AI in Forest Management

Artificial Intelligence is used in forest management by utilizing areal images taken by planes, satellites, and drones. Images captured by the said sources provide the raw training data to be later annotated/labeled. 

When Machine learning models are trained using accurately annotated data, the models are able to better detect illegal activities like tree cutting which eventually leads to deforestation damaging the Ecosystem. Assessing the growth and overall health of trees using AI models equips forest management stakeholders to make informed and better decisions when monitoring forests.

Image Labeling in Deep Learning for Agriculture

Getting a hand on top quality training data for machine learning is no mean feat for Machine Learning companies who work to develop AI models. But this Goliath of a challenge is made simpler with the help of data annotation companies like Impact Outsourcing. 

We assist AI companies to annotate and labeling training data for computer vision at a fraction of your initial cost while maintaining the same high-quality levels as expected.

Impact Outsourcing is famous for providing training datasets for machine learning in numerous fields. These include; Agriculture, Healthcare, Retail, Autonomous Vehicles, Autonomous Flying, and Satelite Imagery.