Deep Learning Hackathon
•Download as PPTX, PDF•
0 likes•144 views
Convolutional neural network for social media image recognition
Recommended
Recommended
More Related Content
Similar to Deep Learning Hackathon
Similar to Deep Learning Hackathon (20)
More from Vera Ekimenko
More from Vera Ekimenko (13)
Recently uploaded
Recently uploaded (20)
Deep Learning Hackathon
- 1. Emirates Advanced Analytics Hackathon Vera Ekimenko
- 2. Hackathon Use Case 1: Problem statement Use case #1 Social media image analytics to uncover valuable customer insights Description More than 3 billion photos are shared daily on social media and an estimated 85% of those photos lack text references. It becomes increasingly important to incorporate social images analytics to draw better customer insights. Use of image analytics to identify and categorize the scenes, faces, logos, objects, and actions that passengers post to social media every day. Classify images from passenger posts as places, brands, food, events and action. The model should be able to identify the places the passengers visited / like, the brands the passenger like, the events the passenger attended. Business Benefits Provide exciting opportunities to uncover more valuable consumer insights from social media What places passengers visited What kind of places does passengers like Special events passengers attending What kind of food does the passenger like What brands does the passenger like Whom does the passenger travel with And much more. This will help in target marketing to increase revenue Data set to use Social Media posting of passengers Success criteria Able to identify at least 3 of the above benefits
- 3. Social profiles provide information about customer’s interests, hobbies, music preferences, places they like, role models they follow and etc. This will allow you to segment your customers going beyond demographics and communicate with them in a new very personalized manner. Enrich Customer Genome with social IDs Target customers with the most relevant offers Detect and predict emerging trends among the target audience Turn new customers into loyal ones through personalized up-sell and cross-sell offers.
- 4. Random public Instagram account - 1 Brands
- 5. Random public Instagram account - 2 Food Food Food BrandsFood FoodFood Food
- 6. Random public Instagram account - 3 Events Brands Food Food Food
- 7. Image categories I picked to train my models Concert Show Theatre Opera Ballet Football Rugby TennisHorse racing Racing Gucci Fendi Chanel Hermes Dior Events Brands Food Dessert Wine Coffee Seafood Pizza Salad Pasta Steak Cake Chicken Burger Fish Sushi Chocolate Beer Meat Cheese Ice cream Beef Vegetables Fries salmon Rice Bread Pork Soup Pancakes Tacos Sandwich Sports
- 12. All food images are multi-labelled Bread Sea food Fries
- 13. The prediction system architecture (17 models) Food/Non-food Multi-label food Fendi Dior Chanel Gucci Hermes Ballet Opera Concert Show Theatre Football Rugby Tennis Horses Racing Multi-class brands Multi-class events Transfer Learning using Keras Places365 model Version 1 Version 2
- 14. The projects stages and resources Collection • Download Instagram images Labelling • Manually group images Training • Light version of EGG16 Lenovo X220 64-bit Windows 10 Memory: 12GB No GPU
- 15. Collection 15,000 images for each hashtag (total number 18) 150 150 Concert Show Theatre Opera Ballet Football Rugby TennisHorse racing Racing Gucci Fendi Chanel Hermes Dior EatingOut Food Restaurant 270,000 images (10GB)
- 16. Labelling Manual labelling • Manual labelling 200 images for each category: • 100 - ‘positive’ • 100 - ‘negative’ Helper model • Trained a helper model • 5-10 epochs • 85-90% accuracy Final labelling • With the helper model predict/ label all 15,000 images • Manually correct the final labelling chanel not chanel 100 + 100
- 17. Labelling – Instagram noise ratio hashtag labelled ratio concert 627 1/24 show 1046 1/14 theatre 768 1/20 opera 225 1/66 ballet 969 1/15 football 1247 1/12 rugby 245 1/60 tennis 368 1/40 horseracing 1550 1/10 racing 339 1/44 gucci 492 1/30 fendi 348 1/43 chanel 307 1/50 hermes 1862 1/8 dior 350 1/43
- 19. Food labelling Hashtags processing • Using NLTK define a word category for each hashtag • Select images with hashtag category ‘food’ Manual labelling • Group and select top 50 food hashtags • For each food category manually label images (true/false) Fries Stake Bread Red wine
- 20. Training – VGG16 for Brands and Events (v1)
- 21. Training – a unified model for Events It didn’t train well no matter of parameters or base model used
- 22. Training – Places365 for Events (v2) Places: A 10 million Image Database for Scene Recognition Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. IEEE Transactions on Pattern Analysis and Machine Intelligence
- 24. Next step - Segmentation Customer profile
- 25. Thank you