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Dot ai talk by charles ollion
- 1. Building a Visual Understanding Pipeline Charles Ollion, CoFounder at Heuritech Charles Ollion, co-Founder at Heuritech @charlesollion
- 2. AI startup building powerful image (and text) recognition technologies Our vertical business focus: Fashion and Beauty HEURITECH 18 people 50% PhDs in AI 2 co-founders
- 3. VISUAL UNDERSTANDING Top Stripes Black & white Casual Pants Light blue Denim Distressed Casual BagChanel Timeless Red Patent Contexts Opinions Products Brands Interests Styles
- 4. VARIABILITY OF IMAGES IN THE WILD Source: http://www.tamaraberg.com/street2shop/
- 5. Deep Learning Algorithms for modern Image understanding
- 6. DEEP LEARNING FOR IMAGE TAGGING Simonyan, K., and Zisserman A. "Very deep convolutional networks for large-scale image recognition." 2014
- 7. COLOR SHADES Light Blue Deep Blue Navy Blue PATTERNS Stripes Floral Cheetah STYLES Bohemian Retro Party EXAMPLE: AUTOMATIC CATALOG TAGGING
- 9. TRANSFER LEARNING Simonyan, K., and Zisserman A. "Very deep convolutional networks for large-scale image recognition." 2014 New Layers New classification task New Classes New Layers Localisation / Segmentation Bounding boxes / Masks
- 10. Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." NIPS 2015 FINDING OBJECTS IN IMAGES Image Class Bounding boxINPUT OUTPUTS
- 11. Long, J, et al. "Fully convolutional networks for semantic segmentation." CVPR 2015 SEGMENTATION OF OBJECTS Image Class Segmentation MaskINPUT OUTPUTS
- 12. Mask R-CNN, K He, G Gkioxari, P Dollár, R Girshick, 2017 (arxiv preprint) SEGMENTATION OF OBJECTS Image Class Bounding box INPUT OUTPUTS Segmentation masks
- 13. Mask R-CNN, K He, G Gkioxari, P Dollár, R Girshick, 2017 (arxiv preprint) SEGMENTATION OF OBJECTS
- 14. Implementation good approaches and tools
- 15. HEURITECH IMAGE PIPELINE Color Identification Classification Clothes Detection Stripes, Floral... Style Attributes Patterns Context Identification Segmentation Handbag, Luggage by Céline Zip, Pocket... Casual, Party... Beige Black... Human, Street...
- 16. FRAMEWORKS AND ARCHITECTURE GPU for training CPU/GPU in production Frameworks Architecture
- 17. MODELS Don’t start from scratch! Architectures: ResNet, Inception, ... Pre-trained models + fine-tuning Model architectures Training Data 100 - 1000 training example per class 100 - 200 for localisation / segmentation
- 19. Source domain: E-COMMERCE Millions of products with (almost clean) labels Target domain: WORN CLOTHES Millions of images unlabeled DOMAIN PROBLEMS
- 20. DOMAIN ADAPTATION Labels from e-commerce MODEL Good results on e-commerce ADAPTED MODEL Domain Adaptation Also good results on worn clothes Source: e-commerce Target: Worn clothes
- 21. Ganin, Y, et al. "Domain-adversarial training of neural networks." JMLR (2016) DOMAIN ADAPTATION Learn to classify (ie clothes shape...) Learn not to classify domain (e-commerce VS worn)
- 22. Source: https://phillipi.github.io/pix2pix/ GENERATIVE DESIGN Input Output Target Input Output Training Inference
- 23. Thanks for listening! ollion@heuritech.com open source deep learning lectures https://m2dsupsdlclass.github.io/lectures-labs/