Diving into the deep end of clothing styles (PyData NYC 2017)
Diving into the deep end of clothing styles (PyData NYC 2017)
Asking if two items of clothing have a “similar style” is a subjective and difficult question to answer. One can attempt to use implicit signals such as purchases, clicks, and views in order to learn product similarities, like in the ubiquitous “People who bought this also bought that” section of any ecommerce product page. Such techniques prove difficult in the world of fashion where clothing can have a short shelf-life. One must then resort to using knowledge of the clothing itself in order learn these similarities. Sometimes this knowledge takes the form of images, and sometimes techniques get deep. I will discuss this journey from implicit signals to an image-based approach that we embarked on at Dia&Co, a fashion retailer. I will first start with the multiple, non-deep models that failed to produce satisfactory results. These will build the foundation and rationale for resorting to admittedly more complicated, deep learning-based methods. This represents a tradeoff between performance and difficulty. While deep learning may be extremely popular, the need for more complex infrastructure (i.e. GPUs), rapidly changing frameworks, and an arguably reduced level of interpretability are significant costs in a production system. The final, successful technique was written from scratch in PyTorch, and results from training this model on our database of product images will be presented. Lastly, details of training and serving model results via an API will be discussed. https://pydata.org/nyc2017/schedule/presentation/18/
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Diving into the deep end of clothing styles (PyData NYC 2017)
- 1. PyData NYC 2017 Diving into the Deep End of Clothing Styles Ethan Rosenthal Dia&Co PyData NYC 2017 eprosenthal EthanRosenthal blog.ethanrosenthal.com
- 2. PyData NYC 2017 Nearly 70% of women in the U.S. are plus-size, but they represent only 16% of apparel spend.
- 3. PyData NYC 2017 Dia&Co is the leading digital-first retailer dedicated to serving women sizes 14+.
- 5. PyData NYC 2017 User Item
- 6. PyData NYC 2017 Styling Algorithms: Power to the Stylists!
- 7. PyData NYC 2017 Item Item
- 8. PyData NYC 2017 Similarity is Subjective!
- 11. PyData NYC 2017 https://github.com/benfred/implicit Python Easy to install Fast AF Industry-standard Algo
- 12. PyData NYC 2017 Problem - Temporal Separation
- 13. PyData NYC 2017 Problem - Temporal Separation
- 14. PyData NYC 2017 Solution? - Past <> Present sneakers=True sneaker_color=White pants_fit=Relaxed etc...
- 15. PyData NYC 2017 Python Easy to install Fast AF MF + user and product metadata https://github.com/lyst/lightfm
- 16. PyData NYC 2017
- 17. PyData NYC 2017 Product Metadata - Purchases + Samplings? Product Metadata + Purchases + Samplings? Product Metadata + Purchases + User Metadata? Product Metadata + Purchases + Samplings + User Metadata?
- 18. PyData NYC 2017 Generating High Quality Metadata is Hard
- 20. PyData NYC 2017
- 21. PyData NYC 2017 Standing on the Shoulders of Giants Lyst - PyData 2016 London https://www.slideshare.net/CalvinGiles/finding-needles-in-haystacks-with-deep-n eural-networks ThreadGenius - NYC Machine Learning Meetup Feb 2017 https://www.slideshare.net/AhmadQamar3/using-deep-neural-networks-for-fashi on-applications
- 22. PyData NYC 2017 FaceNet - Verification ? Schroff, CVPR (2015)
- 23. PyData NYC 2017 FaceNet - Recognition ? George Jerry Kramer Elaine Schroff, CVPR (2015)
- 24. PyData NYC 2017 FaceNet - Clustering Schroff, CVPR (2015)
- 25. PyData NYC 2017 ProductNet - Clustering
- 29. PyData NYC 2017
- 30. PyData NYC 2017
- 31. PyData NYC 2017
- 33. PyData NYC 2017 Requirements Easy to Debug Simple Sampling Dynamic Decision-Making Pretrained Models
- 34. PyData NYC 2017
- 35. PyData NYC 2017 Simple to Debug
- 36. PyData NYC 2017 Flexible Sampling
- 37. PyData NYC 2017 Easily Make Dynamic Decisions
- 38. PyData NYC 2017 Pretrained Models
- 40. PyData NYC 2017
- 43. PyData NYC 2017 True Positive False Positive
- 44. PyData NYC 2017 Loss Now What?
- 46. PyData NYC 2017 Explore http://0.0.0.0:8777/product_style?psid=8100 www.rec-a-sketch.science (demo) https://github.com/EthanRosenthal/rec-a-sketch (code)
- 48. PyData NYC 2017 http://book.paddlepaddle.org/03.image_classification/ When fine-tuning, cache what you can!
- 49. PyData NYC 2017 Use Real Data Don’t Mock the IO Regression Tests It’s hard!
- 51. PyData NYC 2017 tensor.numpy() Pretrained Neural Network Learned Vector Learned Vector Mean S3
- 54. PyData NYC 2017 Deep Learning
- 55. PyData NYC 2017 Thank you Ethan Rosenthal Dia&Co www.dia.com eprosenthal EthanRosenthal blog.ethanrosenthal.com