Acknowledgments: Google AI, CVDF, Samasource and Fashionpedia.
You can find my code at my github
Visual analysis of clothing is a topic that has received increasing attention in recent years. Being able to recognize apparel products and associated attributes from pictures could enhance the shopping experience for consumers, and increase work efficiency for fashion professionals.
The organizers present a new clothing dataset with the goal of introducing a novel fine-grained segmentation task by joining forces between the fashion and computer vision communities. The proposed task unifies both categorization and segmentation of rich and complete apparel attributes, an important step toward real-world applications.
Mask R-CNN, which is based on top of Faster R-CNN. Also tried U-Net for segmentation.
The implementation was based on this Pytorch tutorial. And obviously, using the magic words
- Mask R-CNN pretrained on COCO (https://www.github.com/matterport/Mask_RCNN)
- Training using 1 Tesla P100 (8-9hr)
- Resnet101 Backbone
- Images resized to 512x512 and using Horizontal Flip Augmentations
Public Demo video by DataScience (Youtube channel)