FashionAI2018-TianChi

Introduction

This is the main Gluon code of 阿里天池竞赛——服饰属性标签识别. Note that this code is just a part of our final code, but provides the one of our best single model. Final submission is a ensemble model of two model: One is this resnet152v2 model ,and the other is the Inceptionv4 model from my teammates. The code is based on hetong007's code which provides a good baseline in the competition. This is my first time to use Gluon and thanks to hetong007~

Team name：时尚时尚最时尚 Rank: 10/2950 (Season1) 17/2950 (Season2)

Software:

• ubuntu14.04，cuda8.0，cudnn6.5
• python2.7
• mxnet-cu80
• numpy
• pandas

Highlights

• Higher performance: Improve the result by many modifications for a pure single model(without backbone ensemble).
• Faster training speed: Use gluon.data.vision.transforms for data augmentation which is faster than original code.
• Soft label: Define our own cutom dataset and treat 'maybe'(m) label as 'soft label' when training which can boost the result.
• Muti-scale train & test: Use more scale augmentations when training and testing, especially for TTA(Test time augmentation).
• mAP defination: Define mAP by ourselves according the competetion illustrate.
• Random erasing: Gluon version code defined by ourselvers.
• Heatmap strategy: Find the circumscribed square of the largest connected block, framing the entire heat map area for classification finetuning.

Training in a few lines

1. Download data and extract it into data1/(season1) and data2/(season2).
2. python2 prepare_data.py Prepare dataset for trainset, valset and testset.
3. bash benchmark.sh

• num_gpus，set to 1 for single GPU training