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Written by
Prerak Mody
I help Playment make decisions on the business and product front in the context of the field of Computer Vision.
Machine Learning

Semantic Segmentation: Wiki, Applications, and Resources

In recent years, machine learning technology centered on deep learning has attracted attention. Self driving cars have inculcated deep learning processes that requires the algorithm to identify and learn from the images fed as raw data. Let’s look at how the need for semantic segmentation has evolved.
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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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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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Autonomous Driving

List of Semantic Segmentation Models for Autonomous Vehicles

State-of-the-Art Semantic Segmentation models need to be tuned in terms of memory consumption and fps output to be used in time-sensitive applications like autonomous vehicles. Here we study models like FCN, SegNets, ENets, and ICNets.
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Autonomous Driving

Semantic Segmentation Datasets for Autonomous Driving

An understanding of open data sets for urban semantic segmentation shall help one understand how to proceed while training models for self-driving cars. Explore datasets like Mapillary Vistas, Cityscapes, CamVid, KITTI and DUS.
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