When Lora McCarville left LifeTime Fitness as a yoga instructor in 2009 to start her own studio, little did she know that opening a yoga studio was way different than teaching yoga as a salaried teacher. Eleven years later, her hard work has finally paid off. McCarville now runs classes in Yoga Rocks at Park Omaha that are often sold out in advance.

McCarville’s success is a dream for many yoga instructors — like you — who dream of opening up their yoga studio. Starting your own business is always a courageous decision, but the fruit of its labor is rewarding.

According to Yoga Journal, Americans spend a staggering $2.5 billion on yoga instruction annually while the market continues to grow. By opening a yoga studio, you will not only unlock new doors of financial prosperity for yourself but also touch more lives with your unique gift as an independent teacher.

The only thing standing between you and your plan to open a yoga studio is your decision to act. Overthinking is natural, but don’t let it stop you from reaching for your dreams. Of course, there are things that you need to figure out first. In this article, we will try to help you do just that.

After reading this step-by-step guide, you will inch closer to achieving your goal of opening a yoga studio.

So take a deep breath — inhale the knowledge that you are about to consume and exhale it by applying your ideas to realize your dream. Let go of your tension and worries for the next 15 minutes as you dive deep into the following 10 steps on how to open your yoga studio:

Step 1. Start by preparing a detailed business plan for your Yoga business

The first critical step to opening a yoga studio — or starting any new store for that matter — is to come up with a clear business plan. There is power in documenting your ideas on paper. When you put words to your dreams, you send strong signals to the universe, saying that you want to achieve them badly.

Describing your business plan in vivid detail is also a form of positive visualization — a self-fulfilling prophecy for your future. Preparing a business plan helps you refine your thinking, create your roadmap for success, and identify your roadblocks and opportunities.

A business plan is especially crucial if you are a first-time entrepreneur because it can serve as the keystone of your business strategy. Most banks demand that you write them a loan purpose statement or a detailed business plan for them to consider financing your business.

Keep in mind that a business plan is not just self-expression, but a point-by-point action step to set up a business. For instance, include the following things when you write down your business plan:

1. Your short- and long-term plans for growing your yoga studio
2. The milestones you want to achieve
3. Your marketing and pricing strategies, funding channels, hiring strategy, etc.

More often than not, your business plan is a foundational document that helps you pitch your business to potential investors that explains your strategic and tactical plans for growth.

Keep iterating the plan until you have a concrete understanding of what you wish to create.

If you want help to get started, here is a comprehensive guide from the U.S. Small Business Administration on how to write a business plan.

Step 2. Find your capital investment to stay financially afloat

One of the bitter truths about opening a yoga studio is that it will operate in negative cash flows during the first few months of its inception. Thankfully, your business plan can come to your rescue to counterbalance this business conundrum.

Do your due diligence in identifying and calculating the initial funding you will need to set up your yoga studio. Depending on your past financial preparedness, your investment sources can be your bank loan, families and friends, or 401K savings.

Make sure you have a few good options you can tap into in case you face rejections. Once you figure out the funding, your focus should be on keeping your yoga studio business afloat for the first 3–6 months and being accountable for the repayment.

Step 3. Pick a unique brand name and register your yoga studio

What are you going to call your yoga studio — Yogi Bear, LLC, or Third Eye, Inc.? Branding is essential, but it’s the second half of the story. You first have to register your yoga studio as a legal business entity.

Registering your yoga studio as a formal business entity might look like a perfunctory legal step, but it has long-lasting repercussions.

For instance, the tax benefits for registering your yoga business as a limited liability company (LLC) are better than registering it as a Corporation (Inc.). Instead of filing a corporate tax return, LLC owners can simply make a mention of their profits and losses in their individual tax returns.

For more information related to registering a small business, read this business guide from the U.S. Small Business Administration.

On average, the cost of registering your business under any formal structure is less than $300 — a one-time fee. The good news is, the World Bank ranks the U.S. at 6 out of 190 countries on the global index of ease of doing business.

It means that you have less bureaucratic hurdles and paperwork to get your business up and running. In most cases, you can start your business within 2–15 days of your filing for registration, depending on the State you are operating from.

It’s completely up to you to choose how you want to register your yoga business; there is no right or wrong way of doing this. Whatever you do, make sure you open a business bank account and get a company card as the next step so that you can claim business expenses and file for relevant tax benefits in the future.

Step 4. Get your occupancy permit, fire exit signages, and zoning license

Any recreational business like a yoga studio or a gym needs a set of documents including occupancy permits, parking and signage placements, fire safety clearance, and so on. The law requires you to obtain an occupancy permit to ascertain that your business complies with the zoning laws and ordinances and is safe for conducting yoga classes.

The requirements vary from one city, country, or State to another. In general, you have to submit the following documents to obtain an occupancy permit:

1. Your business income and receipts tax ID
2. A commercial activity license, and
3. Zoning and use permit

Besides, you will have to paste workplace labor law posters in your property to comply with the U.S. Department of Labor regulations.

The perils of not meeting the standard compliance can not only incur monetary losses for your business; it could also lead to costly lawsuits and/or permanent revocation of your business license.

Step 5. Rent or lease a premium location for your yoga studio

You must have heard of this business adage many times before, especially in the domain of real estate: there are three most important things you should consider — i) location, ii) location, and iii) location.

The importance of location is highly relevant for a yoga business since it operates in the consumer market for recreational activities.

Consider all the places where you want to open your yoga studio. Later, shortlist the best neighborhood based on the following criteria:

1. Number of households
2. Total population
3. Demographic information such as median age, gender, and professional
4. Average household income

Factoring in these considerations will help you calculate the best return on investment (ROI) for your yoga school.

You would be lucky if you already own a place in a neighborhood that guarantees high foot traffic. In such a case, all you have to do is remodel the property into an energy-exuding yoga studio.

If you don’t, you should find a spacious place that you can rent or lease. Rental agreements are short-term — usually paid every month — while leasing is long-term and mostly on annual contracts.

Of course, you can also follow the suit of many yoga entrepreneurs like McCarville, who use open-air public spaces after securing the necessary permits from the local authorities.

Step 6. Talk to your to-be customers as early and as often as possible

A common trap most of the first-time business owners fall into is when they fail to test their business ideas with their potential market.

Creating a direct feedback loop with your customers even before you start your business helps you validate your business idea of opening a yoga studio, understand their existing pain points, tap into the buyer’s psychology, and research the right pricing strategy.

In startup incubation programs such as Y Combinator, the mentors often dedicate a large section of their lectures on two things:

1. How to talk to your users before building a minimal viable product (MVP)?, and
2. How to iterate quickly and as often as possible?

Slack, Groupon, and YouTube are a few brands that pivoted early based on customer feedback. If talking to customers has helped tech giants of Silicon Valley, it should also help small business owners like yours.

To pull this off, you can leverage social media to run online polls or hire an agency to conduct anonymous surveys. The more you communicate with your prospective customers, the higher your chances of creating a lasting impact on their lives.

In addition to this, talking to your customers early on and directly also helps you personalize your marketing strategy and offer bespoke yoga regimens suitable for your niche market.

Step 7. Hire an A-team of yoga instructors for your studio

If you assume that you can be a one-person Swiss Army Knife to handle all the chores of your yoga school, you might wear yourself out sooner than you expect.

You can’t shoulder all the responsibilities of running your business on your own, especially when you have to take care of several legal and operational aspects of the company.

Being a yoga expert, you must be aware of some of the main principles of yoga, i.e., wisdom, ego sacrifice, and contentment. You shouldn’t start a yoga business at the cost of risking your physical and mental well-being; that would be short-sighted and egoistic.

And let’s face it — even superheroes need sidekicks.

As a yoga entrepreneur, your aim should be to hire a team of talented people to help you establish a value-driven yoga school and grow it to its true potential.

Build a team of excellent yoga instructors, admin staff, and backend office professionals from the get-go. You might want to hire your ex-colleagues from your past training batches or family members who are invested in your success.

Hiring is one of the most challenging aspects of growing your business. Therefore, if you think that it’s something you are not particularly good at, your focus should be to find someone who can do it for you.

Once your studio is up and running, you can set up a process to evaluate your staff performance.

Step 8. Create the right yoga programs and classes to teach

This step becomes easy if you collect meaningful insights after talking to your prospective customers. Similarly, you can also tie this with the collective experience of the instructors that you may hire.

Combine these two factors to develop your courses, define the average class size, and course schedules. Ask yourself the following questions:

1. Do you want to offer all-year-long beginner’s classes? Or, do you want to offer progressive courses starting from basic, medium, and advanced level yoga?
2. What about offering certificates of completion?
3. Will you be open to offering bespoke classes for people who want to become certified trainers?
4. How about the scheduling process? Do you want people to manually register for monthly memberships at the yoga centre or offer them the ease of doing it through an online appointment app?
5. Will you offer the old-school Hatha Yoga or short bursts of power yoga for busy working moms?

These are questions you should consider when designing a program for your yoga school.

Step 9. Develop a solid marketing strategy to ensure a steady flow of customers

In today’s hyper-competitive business environment, you can no longer bank on sticking tear-off fliers in your local neighbourhoods or displaying your business card in the grocery store’s pin-up boards.

Remember that 43% of the Americans who practice yoga are between the age of 30–49; they are internet-savvy and spend most of their waking hours in digital spaces.

In that sense, devising a marketing strategy for your yoga studio is of utmost priority even before you roll out the yoga mats for your first clients. To develop a failsafe marketing strategy, identify a niche market you can cater to, such as AntiGravity yoga, Ashtanga, Vinyāsa, or prenatal yoga.

Next, you have to create an excellent digital presence for your yoga school. Building a website is a great first start, but it needs some depth.

For instance, include compelling call-to-action (CTA) on your website to help customers take desired actions. The best way to do this is to give them an option to book an appointment for free.

Use an appointment scheduling software to help your customers volunteer their name, phone number, address, and email to book free classes as a try-before-you-buy incentive.

There are several benefits to doing this. The free incentive will attract them to your yoga studio, increasing their likelihood of converting visitors into a customers.

If they somehow don’t show up for or don’t come back after the trial, you can leverage their information to start a nurture campaign through direct mail, email, or text messages.

Step 10. Introduce your brand merchandise to sell in-store or online

To make the most out of your yoga studio business, you can introduce your own line of yoga merchandises for sale such as custom tees, mugs, yoga mats, clothing, Mandala posters, meditation rugs or cushions, branded keychains, water bottles — the options are immense.

You can have an in-store to exhibit and sell the merchandise, or you can create an online storefront on your studio’s main website.

Yoga and fitness enthusiasts are really into branded merchandise, especially activewear and athleisure wear. According to research stats, they are a massive part of the booming yoga economy.

There are multiple advantages to selling your brand merchandise:

It can help you spread the word about your yoga studio when your clients use these merchandise in public places.

Selling brand merchandise will help you generate a steady stream of revenue for your yoga school. You can also grow your merchandise business as an independent online e-commerce store.

You can also leverage the merchandise inventory as free incentives in your direct response ad campaigns to get more customers.

For instance, you can create coupon codes to drive new registrations to your yoga school or run enter-to-win giveaway campaigns in hyperlocal marketplaces such as Groupon, Yelp, Living Social, or even Facebook ads.

Direct response ads are very useful for conversions since they require 21% less cognitive effort to process a decision and offer 29% more ROI than other ad channels.

Ready to open your own yoga studio?

We are perhaps living in the Golden Age of the capitalist economy. If you are passionate about opening your own yoga studio, you should not keep it as a pipedream anymore.

The business and legal efforts of opening a yoga studio are minuscule compared to the skill that you offer as a tutor and your passion for the practice. Your focus should be more on creating a lasting impact on people who will come to learn yoga from you.

Ask yourself — in a world where there are dozens of YouTube channels teaching yoga for free, why should people pay to learn yoga from you?

Just like the body chakras, align your value system with your entrepreneurial aspirations, and soon the hurdles to opening a yoga studio become an effortless task.

Once you have that clarity, go with confidence. We wish you the best in your business endeavors.

Namaskar!

Was this article helpful for you?

Do you want us to cover other aspects of opening a yoga studio? Leave a comment below to let us know.

Originally published at, https://www.goschedule.io/opening-yoga-studio/

Autonomous vehicles, the imminent future that everyone is looking towards has implications in day-to-day life of an average joe commuting between work and home, to mission critical life-saving applications. Being a part of such exciting journey, Playment builds products that enable perception engineers create highly accurate datasets at scale to train their models.

Based on the robustness of the sensors and the algorithmic performance, the car might be able to perform tasks more or less by itself, with minimal to non-existent human intervention. These varying levels of human intervention is the reason behind the segregation of AV technology into levels of Autonomy.

In order for a car to “see”, different types of sensors are required that one way or the other allow it to safely navigate: radars, high-resolution video cameras, high-precision inertial GPS, ultrasonic sensors and LiDARs. Today we will explain what is a LIDAR, one of the most ubiquitous sensors in autonomous cars, and how it works.

LIDAR stands for ‘Laser Imaging Detection and Ranging’. Interestingly, it is an acronym in which there is another acronym (LASER: Light Amplification by Stimulated Emission of Radiation ). Similar to a RADAR that emits radio waves that “reflect” on hitting the objects, a LIDAR emits beams of infrared laser light rays.

Types of LiDAR

In some other context, you may have heard about them described as laser scanner. They are quite common in fields of Topography, Geology, Architecture and Geo-Spatial informatics. Those of this type happen to be very similar in appearance to a theodolite or a total station and are also placed on a tripod, stationed at a certain vertex.

Why LiDAR

Environmental factors play a great role in deciding how good of a driver are we. We all have difficulties in driving during nights and so do the image(camera) based autonomous driving systems. LiDAR based systems do not rely only on the reflectance value of the object surface to perceive the environment, they sample the 3D world at regular intervals to capture illumination invariant object features.

Of course, LiDARs are not all mighty and no feeble devices. They often encounter difficulties while driving in rainy conditions and have a limited range. They could be bulky and require expensive setup rigs on the vehicles.

How a LIDAR works

A LIDAR in a very basic way is a focussed emitter of infrared beams (and that therefore cannot be seen with naked-eye), and a receiver to capture laser beams. Under the conditions of intended use, they are not dangerous to the eyes, so calm down.

Although there are some stationary LIDARs, for example, the most basic models are mounted on the roof of a car. Some models rotate 360-Degrees about itself to cover the whole environment.

The laser beams that hit the objects are reflected, and the reflected rays are detected by the lens. A radar emits radio, a sonar emits acoustic and similarly, LiDAR emits infrared waves that reflect when then encounter an object in their line of sight. The time difference between the emission and reception of a light wave is used to calculate the relative position of the object. This is simultaneously performed with multiple vertical lasers sometimes rotating at high speed.

This way, a LIDAR device obtains a cloud of points from the environment, with which the computer generates a three-dimensional image, this is done multiple times per second(fps) to determine the moving objects and their profile.

A LiDAR gives you a precise description of the environment through millions of points.

LiDARs have been considered very useful in autonomous cars, as it not only allows computers to understand exactly how far each of them is with great precision (by measuring the time it takes for each laser beam to come back) but also identify the objects. This way you can anticipate the situations that will occur (for example the movement of other vehicles or pedestrians whose trajectory might intersect the ego-car’s path), or determine if there is a danger of grazing or hitting something.

Comparing Top LiDAR Sensors

On the one hand we have a stationary LiDAR. They consist of a fairly compact device unit with lenses for the emission of laser beams and a lens for capturing the reflected beams. They are placed on the roof of the car, in front of the rear view mirror, to have a better visual. Sometimes the unit combines LIDAR with a video camera to recognize lane lines, pedestrians or traffic signals. They are used primarily for autonomous emergency braking systems. For example, Volvo and Ford often use such devices.

On the other hand, we have the 360-degree rotating LIDAR. This is an adaptation of the topographic type LIDAR for automotive purposes.

Three LIDAR models from the Velodyne brand (from left to right): HDL-64E, HDL-32E, and VLP-16 (PUCK).

Google’s self-driving car: Their LiDAR is a mushroom kind of structure placed on the roof of cars, which through successive redesigns is being integrated into the vehicle’s design.

Ouster LiDAR: This a peculiar device that captures the environment as an image and then generates the LiDAR point cloud from the images.

The Velodyne’s HDL-64E model emits 64 laser beams and covers 360 degrees, at 900 turns per minute, to capture the entire environment of the car, with up to 2.2 million points per second. It has a range of 50m for the pavement and 120m for vehicles, pedestrians and trees. A single unit in principle is enough to meet AV needs.

Velodyne has also developed other compact and cheaper LIDARs. For example, in the first prototype of Ford Mondeo autonomous, 4 units of the HDL-32E model are used. Each emits 32 laser beams, they also rotate around themselves 360 degrees at 600 turns per minute, capturing up to 0.7 million points per second. The range is between 80 to 100m from objects vehicles, pedestrians and trees. A total of more than 2.5 million points are processed per second.

The latest model is also the most compact and the cheapest, VLP-16 . There are three variants: Puck, Puck Lite and Puck Hi-Res. It emits 16 laser beams, rotates 360 degrees, covers up to 0.3 million points per second and reaches up to 100 meters range.

Not all car manufacturers use 360-degree LiDAR devices in their prototypes of autonomous cars (for their price, or for their difficulty of aesthetic integration). Sometimes they prefer to use multiple high resolution video cameras, stereoscopic frequently, complemented by radars of different scope and aperture. Another day we will talk about these alternatives to LIDAR.

Typical LiDARs involve an array of lasers with mechanical support to enable them capture the 360 degree scene. These are bulky, fragile(the mechanical setup could causes wobbling based perturbations in the data) and expensive. However, with the advent of the cheap solid state based LiDARs, we could achieve the same functionality at a great form factor(compact device). However, they are limited in the field-of-view, typically ranging between 90–120 degrees. This could be compensated by using multiple devices to capture the scene.

The individual point cloud based information is fused using point cloud “registration” algorithms to achieve an output similar to that of the mechanical LiDARs.

Despite the limitations in the object based range, self-driving cars are required to detect them as long as they are in the LiDAR’s range. A number of annotation difficulties arise because of this.

LiDAR Point Cloud data labeling challenges

• Annotation of LiDAR requires understanding of camera 3D → 2D projection to reduce the false negatives (missing annotations).
• Working on futuristic and low resolution or fewer beam LiDARs might not give the complete profile to understand the object.
• The LiDAR echo, a scene and device based distortion might occlude real objects. Annotating for segmentation task in such conditions require a near point level accuracy which sometimes is difficult when multiple scenes are merged due to echo and stray points.
• Indoor LiDAR environments do not really change at the high frame-rate that the LiDAR is designed to capture. Hence, sequences could be labelled at lower rate to avoid overfitting your model. Our experience helps you to cut-down the costs for such cases.

Written by Mothi Venkatesh, Reviewed & Edited by Maneesh, CV Researcher at Playment.

Given that there have been huge advances in the development and accuracy of machine-driven systems, they still tend to fall short of the desired accuracy rates. This is the philosophy behind the concept of Human-in-the-Loop for Machine Learning.

Human-in-the-Loop (HITL)

This concept leverages both human and machine intelligence to create machine learning models. In this approach, humans are directly involved in training, tuning and testing data for a particular ML algorithm.

The intention being, to use a trained crowd or general human population to correct inaccuracies in machine predictions thereby increasing accuracy, which results in higher quality of results.

Research suggests that a variant of Pareto’s 80:20 rule is consistent with most accurate machine learning systems to date, with 80% AI-driven, 19% human input and 1% randomness.

HITL = SML + AL

Wondering what was the weird formula you just read? What the formula essentially delivers is that HITL is the combination of Supervised Machine Learning (SML) and Active Learning (AL).

Supervised ML, curated (labeled) data sets used by ML experts to train algorithms by adjusting parameters, in order to make accurate predictions for incoming data.

In Active Learning, the data is taken, trained, tuned, tested and more data is fed back into the algorithm to make it smarter, more confident, and more accurate. This approach–especially feeding data back into a classifier is called active learning.

A combination of AI and Human Intelligence gives rise to an extremely high level of accuracy and intelligence (Super Intelligence). This combination is powerful beyond imagination.

When does HITL come into play?

1. When the cost of errors is too much. An ML algorithm can have absolutely no margin for error. Any room for error leads to dire situations.
2. When there are Class Imbalances. There are many situations where the thing you are looking for is quite rare, machines cannot answer this question with a high level of confidence. Humans can help resolve matters and, in doing so, retrain the ML model.
3. When there’s little data available at present. For example, classification of social media posts, for a new business in its early stages, by machines might not be a viable option due to the scarcity of data. Humans will make much better judgments in the early stages, but, over time, machines can learn and can take over the task.

Potential of HITL in Machine Learning applications

• Traffic cameras that automatically detect lane violations.
• Fitness applications that automatically log your calorie count from pictures of the food you eat. You don’t have to input the amount and type of food anymore.
• Security cameras that annotate the root cause of motion sensor triggers (e.g. whether it was an animal, human, falling leaves, a car driving by, etc.) and react accordingly. It also helps decrease the frequency of false alarms.
• Text messaging apps that transcribe voice to text with high accuracy. With a HITL, it would be easier to transcribe voices carrying a particular jargon or slang.

That’s exactly why we built Playment!

The One-stop data labelling solution built with the human-in-the-loop machine learning. We support a wide range of annotation types like bounding boxes, cuboids, polygons, poly lines, landmarks and semantic segmentation. We provide a fully managed, hassle free solution to all your training data needs. Since inception, we successfully offloaded over 36 million annotation tasks with our 300k+ user base.

What is Human-in-the-Loop for Machine Learning? was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

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.

Being in the AV industry, in order to stay at the top, machine learning models need to be trained on representative datasets that include all the needed all possible circumstances and possibilities of roads, weathers, traffic and road conditions and other situations.

There are three steps commonly followed by companies around the world to build safer autonomous vehicles.

1. Perception: To perceive vehicles and other smaller objects in the environment. This task can be accomplished using radars and cameras or LiDAR.
2. Mapping: This task involves constructing high-definition (HD) maps. Captured data is (manually) analyzed to generate semantic data.
3. Localisation: By identifying the environment and the exact position of objects in the surrounding, effective decisions can be made about where and how to navigate.

It is almost impossible to know how much data is needed for an algorithm to be trusted with road conditions. But, estimates from Waymo suggest a minimum of 3 million miles of live test drives, 1 billion miles of simulated test drives along with a disengagement rate of 0.2 per 1000 miles (on average a human had to intervene every 5000 miles). It’s extremely important for self-driving cars to have an extremely high level of accuracy. Failing to have a high level of accuracy can prove to be life-threatening and fatal.

This calls for a very good data labeling partner. In-house teams can be an alternative but the process can be really slow and the cost of asking employees to take out time for labeling can be huge and is not a very efficient investment of resources.

So,

How do you choose your data labeling partner?

Level 1:

1. Annotation quality (in terms of Precision/Recall %) High-quality training data is all you need for building AI for the real world.
2. Smarter annotation tools you may need bounding boxes, polygons or semantic segmentation masks for your data. Whatever image annotation type, how much complex it can be, we’ve got you covered.
3. Trained workforce After working with 50+ enterprise clients, we realized the cost of time incurred in your development. Look for someone, who has the capacity to generate tonnes of data with assured quality. We own the army of 300K trained workforce who can handle millions of annotations in a day.

Level 2:

1. Enterprise-grade SLAs when it comes to data sharing, and project requirements you must need SLAs. So, it definitely makes sense to onboard your vendor with standard SLAs.
2. Classes we support we understand you require a deep understanding of images.
3. Pricing Our lean approach and product engineering by design made our service more viable for our customers.
4. Customer support Our experts are ready to serve 24×7 support for customer needs.

Level 3:

1. Data Security Crowdsourcing/Captive Workforce or, both.
2. Ease of Data transfer we support APIs, CSV, FTP and anything you prefer
3. Ease of results evaluation we provide an exclusive client dashboard to track all stats
4. Additional value-added services unlimited free re-runs, and complete project management makes our customer’s life much easier.

Outsource Smartly

Playment offers high-quality training and validation data to enable AI work in the real world. You can better understand how it’s different from other traditional crowdsourcing to scale your training data needs.

Get your data labeling vendor evaluation form and choose wisely.

With the rise in popularity of Autonomous Driving technology, Computer Vision, and Deep Learning — The potential of AI has time and again proven to be colossal. This flourishing industry has given rise to influencers, researchers and a number of conferences. Conferences are one of the most prudent ways for researchers to present papers, conduct workshops and network with like-minded people.

Here are some of the top Autonomous Vehicle conferences held worldwide which you should consider attending (Listed in upcoming order):

Autonomous Vehicle Software Symposium

June 5–7 | Stuttgart, Germany

The Autonomous Vehicle Software Symposium — held alongside Autonomous Vehicle Technology World Expo, the Autonomous Vehicle Test & Development Symposium and the Autonomous Vehicle Interior Design & Technology Symposium in Stuttgart — will discuss, highlight and debate the challenges involved with programming autonomous vehicle software, and look at how one can dramatically reduce development timescales while boosting security and effectively tackling AI challenges and the decision-making processes.

Speakers: Florian Baumann (Technical director, Adasens Automotive GmbH), Herman Coomans (Senior solutions architecture manager, Amazon Web Services), Thomas Scharnhorst (Spokesperson, AUTOSAR Development Partnership), Vignesh Radhakrishnan (Senior ADAS/AD systems engineer, AVL), Zhao Lu (CTO, BestMile)

TU Automotive Detroit

June 6–7 | Detroit, MI

TU-Automotive Detroit is the world’s biggest conference and exhibition showcasing the future of the connected & autonomous vehicles. This year will feature in-depth tracks on ADAS & Autonomous Vehicles, Smart Cities & Urban Mobility, Connected Services and more. With 3250 executives attending from automakers, tier 1’s, startups, investors, TSPs, software developers and more, don’t miss this valuable networking opportunity.

Speakers: Zack Hicks (President and CEO at Toyota Connected, Inc.), Alison Pascale (Senior Policy Strategist at Audi of America), Radovan Miucic (Technical Specialist at Changan US R&D Center, Inc), Joseph Buck (Director, Product Cybersecurity at General Motors)

CVPR 2018 (Computer Vision and Pattern Recognition)

June 18–22 | Salt Lake City, UT

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. With over 3300 main-conference paper submissions and 979 accepted papers, CVPR 2018 offers an exciting program covering a wide variety of state-of-the-art work in the field of computer vision. In addition to the main program, CVPR 2018 includes 21 tutorials, 48 workshops, an annual doctoral consortium, and an industrial exhibition featuring over 115 companies.

Speakers: Dr.Michael S. Brown (Professor, Canada Research Chair in Computer Vision), Bryan S. Morse (Professor in Computer Science, Brigham Young University), Shmuel Peleg (Professor in Computer Science, Hebrew University, Aude Oliva (Principal Research Scientist, CSAIL | Executive Director, MIT-IBM Watson AI Lab), Deva Ramanan (Associate Professor, Robotics Institute, Carnegie Mellon University)

Automated Vehicles Symposium

July 9–12 | San Francisco, CA

This conference gathers industry, government, and academia from around the world to address complex technology, operations, policy issues. It aims to inform, engage and support progress towards safe and automated mobility. The keynote topics of this conference include law, infrastructure, human factors, shared mobility, cybersecurity, public policy and consumer acceptance. Now in its fourth year, the symposium is the largest dedicated automated vehicles meeting in the world.

Speakers: Hajime Amano (President and CEO, ITS Japan), Dr. Thomas A. Dingus (Director, Virginia Tech Transportation Institute), Iain Forbes (Head, Centre for Connected and Autonomous Vehicles, UK Department for Transport), Neil Pedersen (Executive Director, Transportation Research Board),
Kristin Kolodge(Executive Director, Driver Interaction and Human Machine Interface, J.D. Power)

ECCV (European Conference on Computer Vision)

September 8–14 | Munich, Germany

ECCV, the European Conference on Computer Vision, is a biennial research conference with the proceedings published by Springer Science+Business Media. Similar to ICCV in scope and quality, it is held those years which ICCV is not.

Like other top computer vision conferences, ECCV has tutorial talks, technical sessions, and poster sessions. The conference is usually spread over five to six days with the main technical program occupying three days in the middle, and tutorial and workshops, focused on specific topics, being held in the beginning and at the end.

Speakers: Yet to be decided

AutoSens Brussels

September 17–20 | Brussels, Belgium

Join 450+ engineers and technologists at Auto world in Brussels, Belgium for the international edition of this world-class conference — leading technical discussions set to transform the future of vehicle perception technology. AutoSens connects technologists in all disciplines of vehicle perception to network, collaborate, solve shared challenges and advance ADAS technologies more rapidly.

The event brings together engineers from several engineering disciplines including automotive imaging, LiDAR, radar, image processing, computer vision, in-car networking, testing and validation, certification and standards. AutoSens is a collaborative environment geared towards supporting engineering activities.

Speakers: Yet to be decided

IROS (International Conference on Intelligent Robots)

October, 1–5 | Madrid, Spain

The conference provides an international forum for worldwide robotics community to explore the frontier of science and technology in intelligent robots and smart machines and to stimulate innovative ideas, exchange technological perspectives and assess future directions. In addition to technical sessions and multi-media presentations, IROS conferences also hold panel discussions, workshops, tutorials, exhibits and technical tours to enhance technical communications among the attendees.

Speakers: Yet to be decided

SAE 2018 ADAS to Automated Driving Symposium

October 9–11 | Detroit, MI

This highly technical event supports the industry in its efforts to increase the adoption of Advanced Driver Assist Systems (ADAS) and fully automated driving. This focused event takes engineers, system developers, and management through the evolutionary steps of ADAS technology as it transitions to autonomous vehicles in the future.

The 2018 event will focus on automated driving and active safety for a more comprehensive look at the evolutionary technology. Listen as experts from Europe, Asia, and North America discuss the challenges and opportunities this technology faces such as consumer acceptance, government regulation, and global harmonization.

Speakers: Yet to be decided

TU Automotive Europe

October 30–31 | Munich, Germany

TU-Automotive Europe is Europe’s biggest conference and exhibition showcasing the future of the connected car and automobility. With 750 attendees, 80+ speakers and over 40 presentations, this conference is a hub for the most innovative minds in connected cars & autonomous vehicles. This conference is a must-attend for those who want to get a taste of disruptive tech and unmissable networking.

Speakers: Heiko Huettel (Head of Connected Car, Volkswagen), Sally Leathers (Chief Engineer — Electrical at Aston Martin Lagonda Ltd), Christian Clauss (Manager BMW CarData at BMW), Carlo Iacovini (Marketing Director at Local Motors), Nicola Porciani (Head of Connected Car & Digitalization at Lamborghini), Sebastian Peck (Managing Director at InMotion Ventures)

ICCV (International Conference on Computer Vision)

ICCV, the International Conference on Computer Vision, is a research conference sponsored by the Institute of Electrical and Electronics Engineers (IEEE) held every other year. It is considered, together with CVPR, the top level conference in computer vision.

This conference aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Computer Vision. It also provides a premier interdisciplinary platform for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Computer Vision.

We at Playment, aim to participate in the above-mentioned conferences and dwell deeper into the world of Autonomous Vehicles. Do watch out for our stall in the conferences!

Our pick of the Top AI Conferences can be useful in case you are into AI and Machine Learning.

Top Autonomous Vehicle Conferences to attend in 2018–19 was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

This is a simple definition of Training Data.

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.

Machine learning models are not too different from a human child. When a child observes a new object, say for example a dog and receives constant feedback from its environment, the child is able to learn this new piece of knowledge.

Machines too can learn when they see enough relevant data. Using this you can model algorithms to find relationships, detect patterns, understand complex problems and make decisions.

Eventually, the quality, variety, and quantity of your training data determine the success of your machine learning models.

The form and content of the training data often referred to as labeled or human labeled data or ground truth dataset is designed for to train specific ML models with an end application in perspective. Here, from a few examples of labeled images to train various types of computer vision models.

1. Lane detection for autonomous driving

2. Facial features recognition

3. Pixel level scene understanding

Why Training Data matters?

Training Data is nothing but enriched or labeled data you need to train your models. You might just need to collect more of it to sharpen your model accuracy. But, the chances of using your data is pretty low because, as you build a great model you need great training data at scale.

Publicly available datasets are unorganized and semantically difficult to classify into very specialized classes automatically (rain at night?, players in soccer uniform?, top view shots of images?) The only way is to go over them all and select manually.

How to collect Training Data?

And, we could be a good partner in your journey just after all we annotated millions of images a day for some of the world’s most innovative companies. Whether it’s bounding boxes, dots, semantic segmentation or any sorts of shape, we can help you collect high-quality training data with high precision and recall value.

What kind of data you want us to label? Here are some use cases we do,

Image Annotation for autonomous vehicles, drones, agriculture, satellite imagery, video surveillance and sports analytics.

We also support all image annotation types,

1. 2D bounding boxes
2. Polygonal Segmentation
3. 3D boxes/cuboids
4. Line annotation
5. Landmark/Point annotation
6. Semantic segmentation
7. 3D point cloud annotation. (coming soon, in April 2018)

Training Data FAQs

What is Training Data?

Training Data is labeled data used to train your machine learning algorithms and increase accuracy.

What is a Test set?

Every machine learning model needs to be tested in the real world to measure how robust its predictions are. This is data that it has never seen before. Just as a student comes across fresh problems while in an exam, models too, need to be similarly challenged so as to evaluate their performance.

What is Validation data?

While training a model on a particular dataset, we need to ensure that it does not overfit on that data distribution. Thus, the annotated data which we feed into the model is split into training and validation data. This ensures that the learning of the machine learning model is generalized across the dataset.

How should you split up the dataset into test and training sets?

Every dataset is unique in terms of its content. With a fair bit of domain knowledge, one should decide how to split their annotated dataset into train and test pairs. The ratio of the split is usually around an 80:30 (or) 75:25 depending on how stringently you wish to test the performance of your models.

How much training data is enough?

Every domain has different algorithms and different kinds of data that it requires. But, the general consensus within the machine learning community is that more the data, the better off you are creating a robust model.

How can I get free training data?

There are numerous domains online where you can find training datasets. A lot of research groups also share the labeled image datasets they have collected with the rest of the community to further machine learning research in a particular direction.

What is the difference between training data and big data?

With the widespread adoption of technology, it is now become comparatively easier to collect vast amounts of data. Such a drastic change in the volume, variety, and veracity of data has been termed by the community as Big Data. Once you annotate and enrich this data, it can be used as training data for your algorithms.

Difference between training data and test data in Machine learning

Training data, as we mentioned above, is labeled data used to teach AI models (or) machine learning algorithms. Whereas, the Test dataset is the sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.

Training Data - Hacker Noon

What is Training Data, Really? was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

Every company needs its own data to build models so as to achieve various identification tasks. This need arises, due to the variability within the problem domain.

Options to Scale Training Datasets

1. Open source Datasets
2. Customized Datasets with Human validation
3. Synthetic Data Generation

Although obtaining raw datasets is relatively much easier nowadays, enriching or annotating this data poses a whole new set of logistical challenges. As mentioned in our previous blog post there are 3 ways,

1. Built-in image annotators (e.g., Facebook’s image tagging, Google’s reCaptcha)
2. Traditional BPOs (e.g., you know better)
3. Fully Managed Annotation experts (e.g., Playment)

But, how does one go about making a decision?

Read on.

In the future, I hope there will be a central system which would have seen so much data. It’s just an API call to fetch whatever and how much data we need in real-time.

But, such a system does not exist today. And, so there is no escaping the human role in scaling of training data.

What to look for when scaling up Training Data!

Scalable Trained Workforce + Seamless API integration + Dedicated Project Manager + Stringent QC to ensure Data Accuracy = High Quality Training Data at Scale

Here’s a use case from one of our customer,

“The new Autonomous driving startup, building AI Brain for self-driving cars, who needs a huge amount of data to be annotated datasets for vehicles and pedestrians.”

Breaking this down we got,

Traffic signs, pedestrians, different types of vehicles… around 45 different classes.

The problem with existing datasets is, the autonomous vehicles have been trained for say., Europe roads but, what if to put the same car on American roads?

This would lead to contextually irrelevant data to build for localization, categorization with high precision and recall accuracy.

So after freezing the scope of work, our project manager started analyzing the nuances and clarifying queries.

With our trained workforce, the only issue was to train our users for new cases, then the qualifiers.

How does Playment fit in?

Getting hundreds of thousands of annotators are easy. The hard part is training them for the annotation tools and complex labeling tasks.

Solving User Training Complexities

When we get more no. of classes this becomes really a challenge for our project teams.

So if the classes are more, we try grouping them to ease user training.

Like mentioned above, The total M no. of classes are clubbed into N groups. With this, we complete all object localization tasks and again break the groups into individual M no. of classes for performing categorization tasks.

1. On an average day, we deliver 500+ man hours
2. Implemented consensus logic to qualify annotations
3. We do daily in-house QC and if there is any discrepancy we redo it completely.

Closing Thoughts

We understand how large quantity of datasets is essential for high-performing machine learning algorithms.

We do prep all the training data you need so that you can only focus on innovation instead. For more information on data labeling problems, feel free to get in touch.

Originally published at blog.playment.io on February 19, 2018.

Every company needs its own data to build models so as to achieve various identification tasks. This need arises, due to the variability within the problem domain.

Options to Scale Training Datasets

1. Open source Datasets
2. Customized Datasets with Human validation
3. Synthetic Data Generation

Although obtaining raw datasets is relatively much easier nowadays, enriching or annotating this data poses a whole new set of logistical challenges. As mentioned in our previous blog post there are 3 ways,

1. Built-in image annotators (e.g., Facebook’s image tagging, Google’s reCaptcha)
2. Traditional BPOs (e.g., you know better)
3. Fully Managed Annotation experts (e.g., Playment)

But, how does one go about making a decision?

Read on.

In the future, I hope there will be a central system which would have seen so much data. It’s just an API call to fetch whatever and how much data we need in real-time.

But, such a system does not exist today. And, so there is no escaping the human role in scaling of training data.

What to look for when scaling up Training Data!

Scalable Trained Workforce + Seamless API integration + Dedicated Project Manager + Stringent QC to ensure Data Accuracy = High Quality Training Data at Scale

Here’s a use case from one of our customer,

“The new Autonomous driving startup, building AI Brain for self-driving cars, who needs a huge amount of data to be annotated datasets for vehicles and pedestrians.”

Breaking this down we got,

Traffic signs, pedestrians, different types of vehicles… around 45 different classes.

The problem with existing datasets is, the autonomous vehicles have been trained for say., Europe roads but, what if to put the same car on American roads?

This would lead to contextually irrelevant data to build for localization, categorization with high precision and recall accuracy.

So after freezing the scope of work, our project manager started analyzing the nuances and clarifying queries.

With our trained workforce, the only issue was to train our users for new cases, then the qualifiers.

How does Playment fit in?

Getting hundreds of thousands of annotators are easy. The hard part is training them for the annotation tools and complex tasks.

Solving User Training Complexities

When we get more no. of classes this becomes really a challenge for our project teams.

So if the classes are more, we try grouping them to ease user training.

Like mentioned above, The total M no. of classes are clubbed into N groups. With this, we complete all object localization tasks and again break the groups into individual M no. of classes for performing categorization tasks.

1. On an average day, we deliver 3000+ man hours
2. Implemented consensus logic to qualify annotations
3. We do daily in-house QC and if there is any discrepancy we redo it completely.

Closing Thoughts

We understand how large quantity of datasets is essential for high-performing machine learning algorithms.

We do prep all the training data you need so that you can only focus on innovation instead. For more information on data labeling problems, feel free to get in touch.

Originally published on Playment blogs Feb 19, 2018.

How to Scale Training Data for AI Race was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

In 2017, we set out to make it faster and easier to gather training data with Playment. We gained 16 new team members, nearly doubling the team size to 40 folks. Nearly 300, 000+ qualified players joined the platform performing more than a million annotations for our customers every week.

Before getting a peek into 2018, let’s look back at the highlights and product releases in the past year.

Looking Back: New product features & enhancements

2017 has been a glorious year for the AI-first world. The same can be said for the Playment family. As we gathered the increasing demand for annotation on computer vision models, we started building bounding box annotation tool and a significant number of improvements in our overall platform.

After being featured on TechCrunch, we saw huge interest from visionary startups and global enterprises for our platform to realize the need for good data to build trustable AI solving huge real-life problems.

Helping computer vision teams build better models

We’ve shipped some awesome things in 2017 which you might not see but, definitely experience. Here’s what happened:

• For one customer, we offered 50,000 man-hours of annotation work in just 10 days and optimized for 99% Recall.
• Offered 10,000 man-hours in just 3 days (Customer was stunned by this lightening turn around time).

Rolling out the new Image Annotation types

We’ve built the comprehensive set of image annotation tools, solving a wide range of use cases.

1. 3D Cuboids to help you localize vehicles, pedestrians, etc, along with spatial distance estimation from a 2D image without sensors like LIDAR or RADAR.
2. Points & Lines to train commercial AV’s on highways for lane detection.
3. Polygonal Segmentation to generate detailed object outline for even more accurate object localization and complete scene understanding.

Targets for 2018

Here’re some important targets we planned to achieve this year.

1. Best in class AI infrastructure company

No matter how great your algorithm could be, you still need to feed an enormous amount of good data to achieve pixel-perfect recognition and detection capabilities. And, that’s what Playment is focussed on.

2. Full ecosystem for Computer Vision

We’re launching Video and LiDar 3D point cloud annotation tools in the coming month. Our new tools with improved user experience (UX) will make image annotations easier and faster than ever before.

Better the experience, faster the turnaround time would be, lower the cost.

If you’re looking for an annotation partner, we’d encourage you to request a free demo.

Originally published on Playment blog.

“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 as good weather and in a defined area of operations?”

To bring clarity to such situations, the Society for Automotive Engineers (SAE) outlined 6 levels of automation for automakers, suppliers, and policymakers to use to classify a system’s sophistication via their standard titled J3016.

SAE’s Six Levels of Autonomy

The driver assistance systems of level 1 are very common today. Some cars even offer steering and lane-keeping assistance, as well as remote-controlled parking — all systems defined as level 2 “Partly Automated Driving (PAD)”. A good example is Tesla’s “Autopilot”, an industry-leading driver assistance system.

A crucial shift occurs between Levels 3 and 4 when the driver releases responsibility for monitoring the driving environment to the system. Level 3 “Highly Automated Driving”, level 4 “Fully Automated Driving” and level 5 “Full Automation” are still in the testing phase.

Level 0: No Automation

This is where the human driver controls the car completely without any support from a driver assistance system.

Level 1: Driver Assistance

SAE’s Level 1 is an automated system on the vehicle that can sometimes assist the human driver to conduct some parts of the driving task. In fact, many top-of-the-range car models offer level one automation. You’re still the driver with full-control on driving however, you may call upon technology like adaptive cruise control for active safety.

At Level 1, a computer can control either steering or acceleration/braking, but it is not programmed to do both at the same time. To sum it up, you still have full responsibility to monitor road situations and assume all driving functions if the assistance system cannot do so for any reason.

Level 2: Partly Automated Driving

Functions that make partial automation possible are already a reality and in practice. Semi-autonomous driving assistance systems, such as the Steering and Lane Control Assistant including Traffic Jam Assistant, make daily driving much easier. They can brake automatically, accelerate and, unlike level 1, take over steering.

With the remote-controlled parking function, it’s possible to pull into tight spots without a driver for the first time. In level 2, the driver continues to remain in control of the car and must always pay attention to traffic.

Here’s the list of some driving assistance systems,

1. Adaptive Cruise Control automatically adapts speed to maintain a safe distance from vehicles in front.
2. Autonomous Emergency Breaking detects an obstacle, warn the driver or automatically brake to avoid or mitigate a crash.
3. Lane Detection using a forward camera to detect lane markings on the road.
4. Lane Keeping Assist combines a forward-facing camera to detect lane markings with an electric steering system, keeping the vehicle in the center of the lane.
5. Parking Assistance systems are designed to help a driver park. Some perform the entire job automatically, while others simply provide advice so that the driver knows when to turn the steering wheel and when to stop.
6. Parking Line Detection system that detects markers on the road surface in order to determine the exact location of parking lots.

Level 3: Highly Automated Driving

In the third development stage, drivers gain more freedom to completely turn their attention away from the road under certain conditions. In other words, they will be able to hand over complete control to the car. The driver, however, must be able to take over control within a few seconds, such as at road construction sites.

Level 4: Fully Automated Driving

Level 4 is considered to be fully autonomous driving, although a human driver can still request control, and the car still has a cockpit. In level 4, the car can handle the majority of driving situations independently. The technology in level 4 is developed to the point that a car can handle highly complex urban driving situations, such as the sudden appearance of construction sites, without any driver intervention.

The driver, however, must remain fit to drive and capable of taking over control if needed, yet the driver would be able to sleep temporarily. If the driver ignores a warning alarm, the car has the authority to move into safe conditions, for example by pulling over. While level 4 still requires the presence of a driver, cars won’t need drivers at all in the next, final level of autonomous driving. However, their operations will be constrained to certain situations,

• Limited to a geo-fenced area, such as paved streets in a defined area of town.
• Limited by adverse weather, such as falling snow, snow-packed roads, intense rain, thick fog, etc.
• Limited to a maximum speed, e.g., vehicles limited to 35 mph may be able to travel on most streets, but not on roads posted at 40 mph and above.

Level 5: Full Automation (Driverless)

Unlike levels 3 and 4, the “Full Automation” of level 5 is where true autonomous driving becomes a reality.

Here drivers don’t really even need to have a license. and everyone in the car is a passenger. Cars at this level will clearly need to meet stringent safety demands, and will only drive at relatively low speeds within populated areas. They are also able to drive on highways but initially, they will only be used in defined areas of city centres.

Originally posted on Playment blog.