Pragmatic deep learning for image labelling
This talk is composed of 3 major parts: the iterative creation of a recommender engine, the labeling of images, the post processing of images. After introducing the main topic, labeling images to improve recommendation engine performances, we start with a recommendation engine discussion. We briefly describe the “classical” recommender system (collaborative filtering, content based filtering) and their advantages and limitations. We then describe the re-ranking approach we used to combine different engines into one. Re-ranking is a method (used by Google for example) that takes the different ranking as features and optimizes a certain loss. In our case we combine our different recommendations through a logistic regression that predict the probability of purchases for each tuple (user, sale). This version of the engine led to +7% revenue per customer and is now running in production. We then explain why we wanted to use images information. It seemed that sales with some given images were performing better than others. If we had labels on all images we could use them in a content-based recommender system (used itself in the re-ranking engine). We then described how to label our images using pre-trained models, transfer learning and external APIs. We also show how easy it is to steal these APIs. The final part deals with post processing of the images. Since most pre-trained models only output one class prediction, we need to reshape these into broad themes that can be used in our engine. We use a Non Negative Matrix Factorization for this purpose and show that we have very interpretable results. We conclude by comparing visually the different engines. The key take away (more information in the pitch part) are theses: - Machine learning: overview of recommender systems, re-ranking, how to label images, transfer learning. - Do iterative data science. Start simple, then try more complex systems. - Avoid rushing in deep learning without checking what you can find on Internet. Use pre-trained models and transfer learning. There is a lot of hype around deep learning and image recognition. However, there are not that many success stories for web pure player companies. In our case we explain how we started with simple recommender systems before improving them gradually and finally using images information. One of the key take away is the following: do iterative data science. Always prefer shipping a minimum viable product before creating something complex. At our clients, we commonly see teams rushing into images projects for the only purpose of doing deep learning without a clear ROI in mind. We insist on the fact that deep learning is not an end in itself. Here, it boils down to making new information available in the system. In this sense, deep learning methods are just an extension of Business Intelligence.
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Pragmatic deep learning for image labelling
- 1. Pragmatic Deep Learning for image labelling An application to a travel recommendation engine
- 2. Introduction and Context Iterative building of a recommender system Labeling Images Pragmatic deep learning for dummies Post Processing AKA: Image for BI on steroids Outline Results More images !
- 3. Dataiku • Founded in 2013 • 90 + employees, 100 + clients • Paris, New-York, London, San Francisco, Singapore Data Science Software Editor of Dataiku DSS DESIGN Load and prepare your data PREPARE Build your models MODEL Visualize and share your work ANALYSE Re-execute your workflow at ease AUTOMATE Follow your production environment MONITOR Get predictions in real time SCORE PRODUCTIO N
- 4. E-business vacation retailer Negotiate the best prize for their clients Discount luxury Key Figures Sale Image is paramount Purchase is impulsive 18 Millions of clients. Hundreds of sales opened everyday
- 5. Specificities Highly temporary sales -> Classical recommender system fail -> Time event linked (Christmas, ski, summer) Expensive Product -> Few recurrent buyers -> Appearance counts a lot
- 6. Iterative Building of a Recommender System
- 9. One Meta Model to Rule Them All Recommenders as features Machine learning to op5mize purchasing probability Combine Recommend Describe
- 10. Cleaning, combining and enrichment of data Recommendation Engines Optimization of home display the application automatically runs and compiles heterogeneous data Generation of recommendations based on user behaviour Every customer is shown the 10 sales he is the most likely to buy Customer visits Purchases Sales Images Metal model combine recommendations to directly optimize purchasing probability Meta Model Recommender system for Home Page Ordering +7% revenue Sales information (A/B testing) Batch Scoring every night
- 11. Why use Image ? We want do distinguish « Sun and Beach » « Ski » A picture is worth a thousand words
- 12. Sales Images Integrating Image Information Labeling Model Pool + Palm Trees Hotel + Mountains Pool + Forest + Hotel + Sea Sea + Beach +Forest + Hotel Sales descriptions vector CONTENT BASED Recommender System
- 13. Image Labelling For Recommendation Engine Pragma&c Deep learning for “Dummies”
- 14. Using Deep Learning models Common Issues “I don’t have GPUs server” “I don’t have a deep leaning expert” “I don’t have labelled data” (or too few) “I don’t have the time to wait for model training ” I don’t want to pay to pay for private apis” / “I’m afraid their labelling will change over time”
- 15. “I don’t have (or few) labelled data” -> Is there similar data ? Solution 1 : Pre trained models PLACES DATABASE US SUN DATABASE 205 categories 2.5 M images 307 categories 110 K images
- 16. tower: 0.53 skyscraper: 0.26 swimming_pool/outdoor: 0.65 inn/outdoor: 0.06 Solution 1 : Pre trained models If there is open data, there is an open pre trained model ! • Kudos to the community • Check the licensing Example with Places (Caffe Model Zoo) :
- 17. Solution 2 : Transfer Learning Credit : Fei-‐Fei Li & Andrej Karpathy & Jus5n Johnson hYp://cs231n.stanford.edu/slides/winter1516_lecture11.pdf
- 18. PLACES DATABASE OUR DATA SUN DATABASE Training (op5onal) Pre-‐trained model VGG16 tower: 0.53 skyscraper: 0.26 Re-‐Training Transferred Data : Last convolu5onal layer features Re-‐trained model TensorFlow 2 fully connected layers Caffe Model Zoo GPU CPU GPU Leverage existing knowledge ! Solution 2 : Transfer Learning Accuracy: 72%, Top-‐5 Acc: 90 % > state of the art on dataset alone
- 19. Post Treatment & Results (Or how we transfer the labelling information) Using Images informa&on for BI on steroids
- 20. Labels post-processing Complementary information Redondant information Issue with our approach: Solution : NMF Matrix Factorization Dimension Reduc5on Explicability Sparsity Balancedness
- 21. Image content detection Topic scores determine the importance of topics in an image TOPIC TOPIC SCORE (%) Golf course – Fairway – PuHng green 31 Hotel – Inn – Apartment building outdoor 30 Swimming pool – Lido Deck – Hot tub outdoor 22 Beach – Coast -‐ Harbor 17 TOPIC TOPIC SCORE (%) Tower – Skyscraper – Office building 62 Bridge – River – Viaduct 38
- 22. Results ? 1) Visits : • France and Morocco • Pool displayed 2) First Recommendation • Mostly France & Mediterranean • Fails to display pools 3) Only Images recommendation • Pool all around the world • Does not respect budget 4) Third column = Right Mix 1) 2) 3) 4)
- 23. Conclusion Do iterative data science ! Start simple and grow Evaluate at each steps Image labelling = BI on steroids Transfer Learning Kick-start your project Gain time and money Any Data Scientist can do it Deep Learning Don’t start from scratch ! Is there existing data ? Is there a pre-trained model ?
- 24. Learned along the way What’s next ? AYrac5veness = % visits with tag / % sales with tag For ski sales, indoor pictures performs beYer
- 26. What’s Next ? Customize the Image ! Kenya Prague Berlin Cambodia
- 27. Thank you for your attention !
- 28. Solution 3 : What about APIs ?
- 29. What about APIs ? Use for generating labels ! How to steal model: • 1) Score part of the database for training • 2) Train a model • 3) Score your entire database ! (Or don’t, it’s illegal) But I have only 5000 requests ? -> Use Transfer Learning !
- 30. What about APIs ? Use for generating labels ! Experiment: • 5000 requests on API -> 4500 for training , 500 for validation -> 180 class to predict • Transfer learning with MIT Places Pre-trained Model • Scikit learn Multilabel model • One Vs the Rest • Untuned Logistic regression (demo, not used in any real project) (Or don’t, it’s illegal)
- 31. What about APIs ? Results Accuracy 95 Recall 80 Precision 75 Label Probability Label Probability landscape 1,0000 sunset 0,9998 sky 1,0000 no person 0,9996 outdoors 1,0000 water 0,9990 nature 1,0000 park 0,9849 rock 1,0000 river 0,9678 travel 1,0000 scenic 0,8031 Label Probability Label Probability beach 1,0000 ocean 1,0000 summer 1,0000 relaxation 1,0000 sand 1,0000 island 1,0000 tropical 1,0000 idyllic 1,0000 travel 1,0000 seashore 0,9998 seascape 1,0000 water 0,9997(demo, not used in any real project)