My name is Oleksander Miroshnychenko. I am a machine learning engineer at GlobalLogic, Ukraine. I work on developing and applying machine learning models for solving regression, classification, text generation, image segmentation problems.
Also, I have experience in creating multi-armed bandits for site optimization in e-commerce.
My background is in Probability Theory, Statistics, Actuary math (Taras Shevchenko National University of Kyiv)
May 28, 2021 10:30 AM PT
Wehkamp is an online department store with more than 500,000 daily visitors. A wide variety of products presented on the Wehkamp website aims to meet the many customers’ needs.
An important aspect of any customer visit to the website is a qualitative and accurate visual experience of the products. To achieve this, thousands of product photos, especially of fashion garments, are processed in the local photo studio. Since these images' backgrounds are highly varied, background removal is one of the steps in the processing pipeline.
If done manually, this is very tedious and time-consuming work and when it comes to millions of images, the time and resources needed to manually perform background removal are too high to sustain the dynamic flow of the newly arrived products.
In our presentation, we describe our automated end-to-end pipeline which uses machine learning models for removing the background in images.
Data preparation: In the early beginning, after the dataset cleaning, each image was resized to 320*320 pixels. Afterward, we made use of kmeans algorithm to split the data into 6 clusters. We applied various augmentation techniques for classes with a low amount of images.
Background removal model: Our model is built on an architecture inspired by the paper: "U^2 -Net: Going Deeper with Nested U-Structure for Salient Object Detection".
Training process: We worked in a Databricks environment and used workers with graphical processing units. Horovod and Pytorch helped us to make the training process distributed. To avoid OOM errors, for each epoch it was used a batch training technique. The trained model is stored in S3 bucket.
In this speech, we want to share how to create an efficient pipeline for deep learning image processing within the Databricks environment.