Categories
Data Labeling

Digital Annotation Services

Data labeling involves the process of tagging data with specific labels that helps to identify certain properties or classifications or characteristics of objects contained within an image or a video. The process comprises of tasks such as annotation, tagging, transcription or processing of data.

Labeled data highlights data features such as properties, classifications or characteristics that help to discover underlying patterns for identifying a target. This is particularly useful in building Computer Vision models. Data labeling helps to curate data for Machine Learning and Artificial Intelligence applications.

Data Labeler is one of the top data labeling companies in Philadelphia that offers the following services:

Bounding Boxes for Object Detection

Bounding box annotation mainly helps in the detection, localization, and classification of objects in images and videos. We at Data Labeler specialize in detecting objects through the 2D box and 3D cuboid annotations and help you in detecting objects of interest with high-quality and precision. By leveraging our data annotation services, you can build world-class Computer Vision models.

Use Cases

Object Detection

Detecting lanes, potholes, pedestrians, traffic, etc. for training autonomous vehicular models

E-Commerce

Autonomously tag furniture, clothes & accessories for training visual search models in E-commerce.

Insurance Claims

Training ML models to detect the degree of damage like identifying roof or car damage during accidents for insurance claims.

Polygons for Semantic Segmentation & Instance Segmentation

For image analysis tasks, Data Labeler offers semantic segmentation and instance segmentation with polygons. With Semantic segmentation services, we will help you classify regions within an image into specific object categories. In the case of instance segmentation, we help you in producing pixel-level category annotations along with specific instances of particular object classes. With our polygonal data annotation services, we will help you achieve accurate segmentation.

Use Cases

Self Driving Cars

Assigning semantic labels like road, person, car or sky to train the autonomous driving models.

Drone Navigation

Teaching drones to easily navigate through trees and around birds & rooftops.

Training Robots

Equips robots to tackle new horizons like manufacturing and healthcare

Points for Facial Recognition and Body Pose Detection

Integrating facial recognition and body pose detection has never been as simple as it is today. Data Labeler offers key points for facial recognition that enables your ML models to analyze a series of face related attributes like age, gender, head pose, smile intensity, emotion, eye status, ethnicity, and others. Our body pose detection services help you to train your ML models to identify and track a person’s body position.

Use Cases

Self-detecting Surveillances

Automatically spot a particular face from a crowded surveillance video

Hospitality

Improve functionality and security at hotel room entrance and automation of the check-in process.

Coaching Sportsperson

To create AI-powered coaches for the fitness and sports industry

Improving Airlines

Streamline the workflows at airports with improved speed for check-in & departures and reduced queues.

Texts for Image Captioning

Data Labeler helps to create datasets consisting of image caption annotations which are pulled across from millions of web pages. This ensures accuracy, a wider variety of image-caption styles and helps you to train your automatic image captioning models easily given the challenging nature of the task.

Use Cases

Helping the Visually Impaired

Describe images to people who have low vision or blind and rely on sounds & text for describing a scene

Enhancing Videos

Describes the happenings in a video in real-time

Making Web Content Accessible

Image description can be heard or seen when loading images is prohibited due to slow internet speeds.

Select for Image Classification

We provide datasets that empower your models to characterize an image and classify it efficiently and effectively. Data Labeler can categorize photos and images at large scale with accuracy.

Use Cases

Ecommerce Tagging

Train your models on how to categorize a product using its images.

Multi-Select for More Complex Image Classification

We classify images based on multi-level taxonomies. Data Labeler offers data in a structured format and this helps you train your models on complex image classification.

Use Cases

Landscape Classification

Classify landscape into the water, agricultural lands and forest.

About Data Labeler

At Data Labeler, we offer world-class video annotation services and specialize in producing best-in-class training datasets for building ML-based Computer Vision models. Reach out to us at sales@datalabeler.com for best-in-class data labeling services in New York.

Categories
Data Labeling

Data Labeling Approaches

Data labeling involves the process of tagging data with specific labels that helps to identify certain properties or classifications or characteristics of objects contained within an image or a video. The process comprises of tasks such as annotation, tagging, transcription or processing of data.

Labeled data highlights data features such as properties, classifications or characteristics that help to discover underlying patterns for identifying a target. This is particularly useful in building Computer Vision models. Data labeling helps to curate data for Machine Learning and Artificial Intelligence applications.

Data Labeler is one of the top data labeling companies in Philadelphia that offers the following services:

Bounding Boxes for Object Detection

Bounding box annotation mainly helps in the detection, localization, and classification of objects in images and videos. We at Data Labeler specialize in detecting objects through the 2D box and 3D cuboid annotations and help you in detecting objects of interest with high-quality and precision. By leveraging our data annotation services, you can build world-class Computer Vision models.

Use Cases

Object Detection

Detecting lanes, potholes, pedestrians, traffic, etc. for training autonomous vehicular models

E-commerce

Autonomously tag furniture, clothes & accessories for training visual search models in E-commerce.

Insurance Claims

Training ML models to detect the degree of damage like identifying roof or car damage during accidents for insurance claims. 

Polygons for Semantic & Instance Segmentation

For image analysis tasks, Data Labeler offers semantic segmentation and instance segmentation with polygons. With Semantic segmentation services, we will help you classify regions within an image into specific object categories. In the case of instance segmentation, we help you in producing pixel-level category annotations along with specific instances of particular object classes. With our polygonal data annotation services, we will help you achieve accurate segmentation.

Use Cases

Self-driving Cars

Assigning semantic labels like road, person, car or sky to train the autonomous driving models.

Drone Navigation

Teaching drones to easily navigate through trees and around birds & rooftops.

Training Robots

Equips robots to tackle new horizons like manufacturing and healthcare

Points for Facial Recognition and Body Pose Detection

Integrating facial recognition and body pose detection has never been as simple as it is today. Data Labeler offers key points for facial recognition that enables your ML models to analyze a series of face related attributes like age, gender, head pose, smile intensity, emotion, eye status, ethnicity, and others. Our body pose detection services help you to train your ML models to identify and track a person’s body position.

Use Cases

Self-detecting surveillances

Automatically spot a particular face from a crowded surveillance video

Hospitality

Improve functionality and security at hotel room entrance and automation of the check-in process.

Coaching Sportsperson

To create AI-powered coaches for the fitness and sports industry

Improving Airlines

Streamline the workflows at airports with improved speed for check-in & departures and reduced queues.

Texts for Image Captioning

Data Labeler helps to create datasets consisting of image caption annotations which are pulled across from millions of web pages. This ensures accuracy, a wider variety of image-caption styles and helps you to train your automatic image captioning models easily given the challenging nature of the task.

Use Cases

Helping the Visually Impaired

Describe images to people who have low vision or blind and rely on sounds & text for describing a scene

Enhancing Videos

Describes the happenings in a video in real-time

Making Web Content Accessible

Image description can be heard or seen when loading images is prohibited due to slow internet speeds.

Select for Image Classification

We provide datasets that empower your models to characterize an image and classify it efficiently and effectively. Data Labeler can categorize photos and images at large scale with accuracy.

Use Cases

Ecommerce Tagging

Train your models on how to categorize a product using its images.

Multi-Select for More Complex Image Classification

We classify images based on multi-level taxonomies. Data Labeler offers data in a structured format and this helps you train your models on complex image classification.

Use Cases

Landscape Classification

Classify landscape into the water, agricultural lands and forest.

About Data Labeler

At Data Labeler, we offer world-class video annotation services and specialize in producing best-in-class training datasets for building ML-based Computer Vision models. Reach out to us at sales@datalabeler.com for best-in-class data labeling services in New York.

Categories
Artificial Intelligence

Inside a Neural Network’s Mind

Neural Networks are a set of algorithms modeled after the human brain that recognizes the underlying patterns in a data set. Similar to a brain, the neural network learns all by itself without the need for explicit programming. What happens inside a neural network has intrigued many and research has been dedicated to seeing how the neural nets perform what they are intended to.

In this blog, we will explore the inner workings of a neural network that processes language. MIT in collaboration with Qatar Computing Research Institute has released several papers on an interpretive technique that analyzes neural networks trained for translation and speech recognition. Through the research, they could find some support for some of the common notions about how a neural network works.

Lower Level Vs Higher Level

Neural Networks concentrate on lower-level tasks before moving on to higher-level tasks. For instance, they seem to concentrate on sound recognition or a part of speech recognition before moving on to translation.

As per the researchers, the translation neural network considers a certain type of data which leads to the omission of some part of the data. By correcting the omission helps to improve the performance of the network which in turn helps to improve the accuracy of artificial intelligence systems. 

Neural Networks are typically arranged into layers with each layer consisting of nodes which are nothing but simple processing units. Each node is connected to several other nodes in the above and below layers. Different weights are assigned to the connections between the layers which determines how much a node’s output is considered for the calculation performed by the next node.

Since there are thousands to millions of nodes and connections involved, finding out what algorithm those weights give rise to highly impossible. The technique used by the MIT and QCRI researchers involved taking a trained network and using the output of each of its layers to corresponding individual training examples to train another neural network to perform the same task. This helps to understand what each layer is optimized to perform.

The researchers found through the technique that in translation networks, lower levels performed better at recognizing phones than higher ones and also were good at identifying the parts of speech and morphology. The higher levels were found to be good at semantic tagging.

About Data Labeler

At Data Labeler, we provide fully managed data labeling services and specialize in the production of high-volume and best-in-class training datasets for AI and ML initiatives. Reach out to us at sales@datalabeler.com for high-quality data labeling services.