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Computer Vision

Decision Framework For Data Labeling Strategy

Busting common misbeliefs about data labeling workflows and budgets to provide a realistic decision framework that will take your data labeling strategy from flawed to flawless.
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Machine Learning

Bounding Boxes for Object Detection and Classification

2D Bounding Boxes is perhaps the most ubiquitous annotation type one might encouter in computer vision. As the name suggests, the annotator is asked to draw a box over the objects of interest based on the requirements of the client.
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Inside Playment

Improve Facial Recognition Using Semantic Segmentation and Landmark Annotation

Firms in the security and surveillance sector which build facial recognition models require high-quality landmark annotations across a variety of classes. Dot annotation (a.k.a Landmark annotation) and semantic segmentation is used to determine shape variations of objects.
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Machine Learning

What Is Human-In-The-Loop for Machine Learning

Computers are incredibly fast, accurate and stupid; humans are incredibly slow, inaccurate and brilliant; together they are powerful beyond imagination hence the concept human-in-the-Loop.
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Computer Vision

List of LiDAR Datasets for Autonomous Vehicles Till 2018

Although 2D camera data is used to teach autonomous vehicles to find their way from Point A to PointB, it comes with its own set of drawbacks. For eg: camera images are not very useful when it is dark or there are reflections due to strong sunlight
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Data Security

What Does GDPR Really Mean for the Autonomous Vehicle Industry

On May 25, 2018, the general data protection regulations (GDPR)—Europe’s new data, privacy, and user consent regulations—take effect. Since we know that, autonomous vehicles are set to have a global impact on our relationship with mobility. At the same time, you will be generating a lot of personal data whilst driving,
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Smart Outsourcing

Framework to Evaluate a Data Labeling Partner for Machine Learning

There are many challenges in building AI that works in the real-world scenarios. One of those is the quality of the data that is needed to train your model.
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Computer Vision

Loss Functions for Computer Vision Models

Machine learning algorithms are designed so that they can “learn” from their mistakes and “update” themselves using the training data we provide them. But how do they quantify these mistakes?
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Computer Vision

What is Training Data, Really?

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.
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Computer Vision

Comparing Image Annotation Types by Computer Vision Use Cases

The most important thing that the computer vision expert does is to decide which image annotation type is needed to build the most accurate model(s) for object detection.
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