Today, almost all autonomous vehicle companies targeting level-5 autonomy use a setup involving LiDARs calibrated together with cameras working in sync to perceive the world around them. As a result, we’re seeing a huge surge in the demand for data to train the deep learning systems built around these sensors.

To this end, we built out our sensor fusion annotation toolkit. It enables labelling of complex scenes while ensuring the quality that ground truth data should have.


The Playment survey on task difficulty suggests that Semantic segmentation is one of the difficult tasks for our annotators. This inspired us to automate the ground-truth annotation to reduce the workforce efforts and efficiently handle our resources. To this extent, we decided to leverage the existing interactive segmentation methods to retrieve apriori map so that our annotators could correct the machine decisions at will instead of labeling from scratch. Thus making the approach an example of our philosophy human-in-the-loop for machine learning.

To begin with, the task involves assigning pixel level labels to an input image. …


This article would succinctly describe the best ten datasets used for certain fundamental computer vision problems such as classification, detection and segmentation. Considering traditional computer vision approaches and also to encourage audience who are resource constrained and to seed an idea of getting started with computer vision, this article is planned and crafted in such a way that the list also includes some smaller datasets.

Open Image Dataset Resources

IMAGENET [Classification][Detection]

Imagenet is more or less the de facto in the computer vision problem of classification since the deep learning revolution. It contains more than 14M images with 21841 synsets. To enable you download…


Deep learning is a type of machine learning that mimics the neuron of the neural networks present in the human brain. Computer Vision Deep learning models are trained on a set of images a.k.a training data, to solve a task. These deep learning models are mainly used in the field of Computer Vision which allows a computer to see and visualize like a human would.

Deep learning models can be visualized as a set of points each of which makes a decision based on the inputs to the node. …


Back in 2013, Flipkart had started hiring in bulk from top undergrad colleges of the country. I was a part of 100 “graduate trainees” who together underwent a 2-week long induction program. Naturally, we made good friends and then all of were divided into various teams such as operations, product, business, marketing, finance, supply chain etc. We used to catch up frequently — some of us talked about the role very enthusiastically while a few didn’t. Looking back, I realized

  1. Almost all freshers (non-techies) out of college aren’t really sure what do they want to do in life.
  2. Everyone starts…


Autonomous driving is no longer sci-fi, it’s become a reality and soon to be hitting our streets. Every week, Autotech companies are announcing their plan for self-driving tech. But, no two autonomous driving technologies are exactly alike.

“What we actually mean when we say a vehicle is self-driving, fully automated, and so on?

Do we mean that the vehicle can drive itself anywhere at anytime, with no-assistance inside it?

Or, that the vehicle always needs someone inside ready to take over just in case?

Or, that the vehicle can drive itself, as long as it is within certain constraints, such…


Most of the enterprise companies use the crowd to get work done. Infact, most Playment customers used Mechanical Turk. So how is Playment different?

Crowdsourcing certainly has its own merits of flexibility, scalability, and cost-effectiveness. But it fails at “Quality Assurance” which is a bigger deal than all of them put together.

That’s why Playment has been smartly devised to combine merits of crowdsourcing along with guaranteed quality assurance.

Salient features that differentiate Playment are,

State-of-the-art Tools:

  1. Efficient tools to support various classification, annotations and transcription tasks with custom features for varied task types.
  2. Gamified-interface that amplifies the speed of…


With so much research in AI and evolving applications, it can be difficult to keep track of all the confusing terms in artificial intelligence. In this post, I attempt to pen-down common terms and their definitions that crops up when discussing artificial intelligence. You can use this as a handy reference tool in 2017 and beyond.

So, whether you’re still hung up on the difference between artificial intelligence, machine learning, and deep learning, check out the following roundup of artificial intelligence terms to keep yourself in the know.

Popular Artificial Intelligence Terms

  1. Advanced Driving Assistance Systems(ADAS) — Systems developed to help the driver in…


We are excited to announce that Playment has recently raised $1.6M in pre-series A funding from Y-Combinator, Sparkland Capital, and angels such as Ryan Petersen, Max Altman, David Petersen and others. Existing investors SAIF Partners participated in this round as well to take the total funding to $2.2M till date. With this round of funding, we plan to become the leading provider of training data to AI companies with a special focus on autonomous driving.

We provide high-quality human curated training data for Computer Vision. Essentially, we provide human-powered training for machines to see, think and make decisions like humans…

Playment

Data labeling partner for Computer Vision teams. #HumanInTheLoop #AI, #TrainingData for #MachineLearning.

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