Uroš Valant has almost 20 years of experience in planning, managing and delivering of various IT projects. He has the best and richest experience in the field of business analytics, project planning and implementation, database design and the management of development teams. In the last years, his focus is the field of predictive analytics, machine learning and applying the AI solution to a practical use in different field of work. In his talk he will present to us interactive case study of the image recognition use and AI assisted design techniques in the textile industry.

1. Prediction of good
patterns for future sales
using image recognition.
Uroš Valant
Use case in textile
manifacturing and design

2. 1
Data Scientists
• CREA pro – Founded in 2010 as a consulting company
• Specializes in Performance Improvement, Business Process Optimization & Data Science
• Already 20+ successful projects in the field of data science
• Combining business & technology expertise
• Using different data science platforms
2016
NLB
2017
2019
TEKSTINA
NLB
SBERBANK
A1
EKWB
3DVA
ADDIKO
NLB BEOGRAD
SBERBANK SRBIJA
APTAR
HELIOS
T2
RAIFFEISEN BIH

3. 2
Textile industry in Slovenia
• One of the premium industry branches in pre and post WW2 era
• Steep decline after 1991
• One of the best one is Tekstina d.o.o. from the town of Ajdovščina
• They produce shirt textiles …
…and make their own designs
• Only a handful of textile factories remained

4. 3
The business problem
• Tekstina designers create pattern designs for
two collections a year
• Usually they create between 300-400
patterns
• Extremely subjective process of selection
• Catalogue for collection contains 150 final
patterns
• The questions for our AI team:
– Can AI help us with the selection
process?
– Can we predict the sales of the
patterns?
– Can we use the AI for creating new
patters? COLOR VARIANTS
DESIGN BOOK

5. 4
Business understanding
• A complex process from the idea to a selling design pattern
• Different kinds of materials and prints
• Also printing the shirt materials for other clients
• Subjective selection process
MOOD BOARD
INSPIRATION BOARD
PRIMARY DESIGN COLOR VARIANTS FINAL SHIRT PATTERN
COLLECTION CATALOGUE

6. 5
Data understanding
• Relevant data:
– Design images
– Financial data from the ERP
– Soft data (fashion trends, market peculiarities)
• A demanding job of data preparation
– Pairing of pictures (more than 3000) with the data in the ERP by hand!
– Finacial data audit and attribute selection
– ERP working process
FILE SYSTEM ON THE SERVER
ERP

7. 6
Analytical dataset preparation
• The task – transforming pictures into the suitable data format
• Solution – vectorization with deep CNN
• Process – transform original design into a vector representation
• Purpose - seeking for similarities with the best selling designs
• Image preparation:
– Adjusting the picture size for all designs
– Normalization for better recognition

8. 7
Modelling
• Not enough data for training the neural network from scratch
• Use of pretrained neural network (ResNet 18)
• The features (vector) are then paired with hystorical sales data
• Sales prediction for a design – classification target
ORIGINAL
PREPARED IMAGE
IMAGE TILING &
RESIZING NORMALIZATION
PRETRAINED NEURAL
NETWORK
RESNET18
FEATURE
DETECTION
CUSTOM NEURAL
NETWORK
HISTORIC
SALES DATA
FULLYCONNECTEDLAYER
YES
NO
SALES
CLASSIFICATION

9. 8
Deployment
• Special web
application for
browsing the designs
• Connection with ERP
data
• Basic information
– Single design
– All color variants
• Interface
– Filters
– Sorting
• Users
– Marketing
– Sales
– Designers

10. 9
Sales prediction results
• Evaluation of submitted
designs for the collection
• Additional tool in the
selection process
• Design ranking based on
the sales probability from
the model
• Sales numbers from ERP
• User interface
– Sorting

11. 10
Comparing the designs – special visualisation
• Remember the feature
detection?
• Usually there are more than
1000 features detected in a
single design!
• PCA (principal component
analysis) method reduces the
number of features
• Representation in a 2D space
• Position - similarity of designs
• Color – the sales
• User interface
– Filter

12. 11
Autonomous design
• The second major request from the customer
• The suggested model – style transfer
– Two pictures – the content and the style
• Extracting feactures from different layers of neural network
• The same neural network creates new image by detecting common features form both images on
every level

13. 12
Using style transfer
• Five suggestions for every
couple of images
• Automation:
– Continuous image creation
combining existing designs
– Evaluation through sales
prediction model
– Winner of the day
– Suggestions for collection
• Many possible improvements

14. 13
The final process
• Two lanes:
– Sales prediction
– AI design
Ai
Sales
prediction
Ai
Feature
extraction
File System
Designs
ERP Sales data
Analytical
data set
Other data
Selection
process
Customers
Sales data
Ai
Style transfer
Style transfer design scoring
Daily winners

15. CREA pro d.o.o.
Ukmarjeva ulica 6
1000 Ljubljana
T: +386 (0)590 74 270
info@creapro.ai
Uroš Valant
+386 40 483 153
uros.valant@creapro.si
Srđan Mlađenović
+381652015665
Srdjan.Mladjenovic@comtrade.com
Comtrade System Integration
Savski nasip 7,
11000 Belgrade, Serbia
T: +381 11 201 56 00
info.rs@comtrade.com