‘What Women Want’ – Saree Buyology

There are over 80 ways of draping a saree. These are mostly styles based on the region. And with the region come the designs and the material. So, the permutations and combinations that one has to go through to choose “one perfect saree” are numerous. So, we set out to understand ‘What Women Want’ while choosing a saree. This was to better understand the social, economical and cultural perceptions towards the saree today and to attempt a solution using new technologies like AI & Vision computing to make the experience of buying that “One perfect saree” memorable, social and hassle-free.

Sarees are one of the oldest clothing articles on the face of the earth. It pre-dates most of the clothing cultures we now have. This traces back to around 2000 – 1800 bc (https://en.wikipedia.org/wiki/Sari). Sarees have not just lasted so long but have also modernized in trends and design with time. Even the manufacturing and the sale of sarees has gotten more sophisticated over time. So, you might think choosing a saree should be just as simple as choosing a shirt/trouser. It’s just not that simple.

Understanding Who Wear Sarees Today

The NY times, claims saree draping to be a nationalist agenda (https://www.nytimes.com/2017/11/12/fashion/india-nationalism-sari.html) but as stated before, saree draping predates all of this. So, we decided to take the subcontinent into consideration, on multiple factors and with information from surveys and trends our findings on who wears sarees can be seen as below:

  • Religion divide
    A survey by the NSSO states that saree is not just a hindu attire but christians and muslim households spend considerable share of women’s clothing budget on sarees
  • Economic divide
    Saree breaches the class divide. The effluent class’s saree buying is at 77% which is only slightly higher than the bottom class’s 72%.

Understanding What Women Look For In A Saree

From our earlier understanding that most southern states in the subcontinent favor sarees than the northern ones, we conducted a survey with a small sample size of women, Majorly from tier 2 and tier 3 towns to understand what do they look for in a saree.

who is interested in sarees

In the towns, it’s a growing trend that the majority of women in their 20’s are preferring to move to the cities for work and education. The women above 50’s in the towns are parents to the children who are moving to the cities. So, Majority of sarees are being purchased by women who are over 40 years old.

what design are they looking for

The graph states that the majority of people looking to buy a saree are always looking for thread works on their sarees. This could be because it is easier to maintain than stones and is not as simple as checks and prints. The prints fall second but lag by a fair margin.

Is Design Everything

In every other branding that we see for sarees, we see all the bells and whistles. The shiny rocks on the saree, the glossy silk, the simple prints, but what are people actually looking for?

Designs of the saree seem to be the least important when it comes to preferences, The feel, the material and the quality are what are looked for. In other terms, longevity and usability of the sarees are important than the design. Also, 80% of women choosing design fall between 20-35 years.

Apart from the above insights we have also discovered the following:

  • Gifting a saree is very common in india
  • Majority of women buying sarees buy it for everyday usage and the purchase of fancy sarees is for special occasions where all the classes tend to spend more than usual for that one saree.

So, now that we know what women look for in a saree, Lets look at their buying behavior.

Understanding How They Shop For Sarees

A process of buying starts in the minds of the consumer, which leads to the finding of alternatives between products that can be acquired with their relative advantages and disadvantages. From earlier, we know that the quality factor prevails in the first position, color and design, comfort and style and price are securing successive ranks respectively.

From more surveys and interviews, we understand the general shopping patterns. 

The graph here shows how the market has been growing with larger name brands across all the classes scaling ethnic wear in india. This also shows how affordable ethnic brands are in comparison to western wear.

(Source: Technopak, Wazir Advisors, Equity research and Avendus analysis)

The saree market in india is one of the largest apparel market in the country. There is a significant shift away from traditional sarees towards ethnic wear and western wear. Though the growth seems to be slower for sarees, it still would be the market leader in time to come.

The Influencers

  • Increasing number of occasions

With the growing social boundaries, the number of occasions have increased in india. Formal, informal and traditional occasions have made women increase their wardrobe.

  • Impulsive buying

With offers everywhere and the technology being present in your palm, attraction towards any commodity has fueled impulsive buying for the average indian. 

  • Influence of media

Soaps, Movies, Ads, Social media, Personal messages. The visual format of content sharing is enabling users with millions of options and is contributing towards this change in behaviour 

  • Increase in fashion sense

With the evolving fashion and media, people are not just looking for utility but for aesthetics too. And with larger brands spreading across the country with scale production, aesthetic clothing is affordable to everyone

  • Aspirational buying

Women today are empowered with the ability of higher spending. Along with it, good clothing is aspirational too. A memorable occasion needs aspirational clothing to complete it. 

Where Do They Buy Sarees From

From sales of sarees by local vendors on instagram, facebook marketplace  and amazon and flipkart to larger chains and stores, Women today are shopping for sarees in every vertical available. The online market is one amongst the most important reasons in the growth of sarees in India. Since the adoption of Sarees is majorly in rural areas where penetration on internet is increasing day by day, this may result in opening of a brand new revenue pockets for stockholders in Indian sari business The increasing penetration of

While the online market and popup stores mostly takes care of the impulse buying and everyday needs of sarees, when it comes to shopping for occasions and events; women still prefer buying sarees in larger stores or from reputed brands. They dont mind the extra effort (and/or) the overhead cost that retail stores bear.

Internet, the increasing buying power of women, high brand consciousness and fashion sense has made e-commerce a crucial medium of shopping.

Customer saree shopping journey

From the customer’s shoes, Buying a saree is a very deeply embedded process with numerous points of friction and points of leverage. Customers interaction with the shopkeeper is only a part of a larger journey that they’re on.

The above mentioned is just an outlier of the shopping experience. The nuances and the conditions they evaluate change with every customer. 

Conclusion: (This Is) What Women Want.

  • Quality and assurance of the commodity plays a major factor on the buying
  • Emotionally, validation and feedback on what they wear plays a great role in the choice that women make while buying a product
  • Validation and feedback on a product are observed to be attained through conversation on the look and feel and the costing of a saree
  • Though buying a saree requires the evaluation of quality and feel, women prefer the design, work and other visual elements to look at a saree
  • Brand names play a major role
  • The idea behind fashionable clothing is to make someone look beautiful so the search is always for a saree that one looks beautiful in

Pose Estimation Benchmarks on intelligent edge

Benchmarks on Google Coral, Movidius Neural Compute Stick, Raspberry Pi and others


In an earlier article, we covered running PoseNet on Movidius. We saw that we were able to achieve 30FPS with acceptable accuracy. In this article we are going to evaluate PoseNet on the following mix of hardware:

  1. Raspberry Pi 3B
  2. Movidius NCS + RPi 3B
  3. Ryzen 3
  4. GTX1030 + Ryzen 3
  5. Movidius NCS + Ryzen 3
  6. Google Coral + RPi 3B
  7. Google Coral + Ryzen 3
  8. GTX1080 + i7 7th Gen

This is a comparison of PoseNet’s performance across hardware, to help decide which hardware to use for a specific use case, if optimizations can help. It also gives a glimpse into hardware capabilities in the wild. The hardware included a range from baseline prototyping platforms to tailored for edge to production-grade CPUs.

Hardware Choices

  1. Raspberry Pi: The board of choice for prototyping, although low powered, gives a good initial understanding of what to expect and what to choose for production. It may not be able to run the DNN models, but it sure is fun.
  2. Movidius NCS + RPi 3B: Movidius Neural Compute Stick is a promising candidate if the model is to be run on the edge. NCS has Vision Processing Units (VPU) which are optimized to run deep neural networks.
  3. Ryzen 3: AMD’s quad-core CPUs are not a conventional choice for neural networks, but it is worth checking how the networks perform on the platform.
  4. GTX1030 + Ryzen 3: Adding an Nvidia GPU to the rig (granted, it is comparatively old but it is cheap) allows us to benchmark what is possible on older cuDNN versions and GPUs.
  5. Movidius NCS + Ryzen 3: A desktop system allows for better and faster interfacing with the NCS. This setup is preferred during prototyping your edge application. Having a high performance CPU allows rapid application development while NCS gives the ability to run your models on your development laptop.
  6. Google Coral + RPi 3B: Google’s answer to on-edge ML is their Coral board which has TPUs. Tensor Processing Units are used by Google’s gigantic AI systems. Coral puts the compute power of TPUs on small form factor. It has native support for Raspberry Pi too.
  7. Google Coral + Ryzen 3: As we mentioned in Movidius NCS + Ryzen 3 section, it is going to be insightful to see how Coral interfaces with Ryzen 3 based computer.
  8. GTX1080 + i7 7th Gen: Top of the line system with GTX1080 and Intel i7 CPU. This is the highest performing combination in the list.

Repositories and models used:

  1. PoseNet — tfjs version
  • Based on MobileNetV1_050
  • Based on MobileNetV1_075
  • Based on MobileNetV1_100

2. PoseNet — Google Coral version

3. Read our previous blog post to get Movidius versions of PoseNet

Comparing Edge Compute Units

Google Coral’s PoseNet repository provides a model based on MobileNet 0.75 which is optimized specifically for Coral. At the time of writing, the details of the optimizations have not been provided and it is not possible to generate models for MobileNet 0.50 and 1.00.

Google Coral vs Intel Movidius

The optimized Coral model gives an exceptional performance of 77FPS with Ryzen 3 system. However, the same model gives ~9FPS when running on Raspberry Pi.

Movidius shows differences in performance with RPi and Ryzen, with the general pattern being faster on the Ryzen 3 system

Comparing Desktop CPUs and GPUs

The results are aligning with expectations while comparing CPU with GTX 1030 and GTX 1080. The high-end GPU outperforms the other candidates by a huge margin. However, the competition between Ryzen 3 and GTX 1030 is close.

Ryzen vs GTX 1030 vs GTX 1080

Final Thoughts

The following chart shows frames per second for a standard video input:

Frames per second

Google Coral, when paired with a desktop computer outperforms every other platform — including GTX1080.

Other noteworthy results are:

  1. When paired with Raspberry Pi 3, Coral gives ~9FPS. The reason behind the result is not yet explained but is being looked into.
  2. GTX1080 performs almost equally regardless of the model size.
  3. Movidius NCS performs better than GTX1030.
  4. Raspberry Pi is not able to run the models at all.

Different hardware gives a different flavor of performance, and there is scope for model optimization (quantization for example). It may not always be necessary to go with a high-end GPU such as GTX 1080 if your use case allows for a good trade-off between accuracy and speed/latency.

Our analysis shows that choosing the right hardware coupling with a well-optimized neural network is essential and may require in-depth comparative analysis.