The Neuromation team continues the Asian Road Show and keeps you updated on the latest news on Neuromation’s platform development. This week, read about our business trip to China, our latest improvements and exciting achievements!

Neuromationa and OSA DC continue the roadshow that will open doors to the markets of China, Korea, and Japan.

Our Moscow team has completed the following:

1. Token site:

Aggregation of token turnover from markets that were announced in official channels

2. Created marketing materials for Asian Roadshow.

3. Implemented a QR scanner in demo mobile application.

4. OSA HP:

— Working on whitepaper for synthetic data and statistic efficiency of synthetic data.

— Analysis and creation of tools and scripts for testing photoset.

— Preparing photoset for labeling and markup

— Creation of list for purchase for OSA HP according to contract for 1,000 elements.

— Approximately 3,000 images were marked.

Our St. Petersburg team has done the following:

User story for platform and internal usage for research.

2. Retail

— Generating feature spaces from the Faster R-CNN architecture for search task. Constructed t-SNE and k-means, no positive results yet. Perhaps the problem was in feature vector extraction; testing different approaches now.

— Trained U-Net to search for price tags. Positive results, see below

— First steps in Yandex. Toloka crowdsourcing service for data labeling, learning the theory on generative models and GANs.

3. CVPR

— Linknet implementation with experiments, research on the issue, new approach, new ideas

— Review of Kaggle-winning satellite imaginary solutions

— Superpixel extraction for pre-segmentation

Results on price tag segmentation

Price Tag Segmentation:

As part of our ongoing project to recognize items on supermarket shelves, we plan to augment our models for the items themselves with textual information from price tags. This can help solve hard cases of object classification, but information from price tags is also interesting in its own right.

The first step to read a price tag is to find it. On the picture, you see the results of our segmentation model based on the U-Net architecture. The model was trained on our own synthetic dataset of renderings of supermarket shelves, this time with price tags. As you can see, it transfers well to real data and this opens up new opportunities for our retail projects.

Having achieved these great results this week, we are continuing to move the AI industry towards democratization and affordability.

Keep an eye on our weekly updates!