rav009 / fashionai2018-tianchi

FashionAI Global Challenge—Attributes Recognition of Apparel—Ranked 10st solution.

介绍

基于原作者gd2016229035参加2018天池FashionAI服装属性识别竞赛的开源代码，我做了一定修改以适配我的应用场景。 所有"my_"开头的py文件和bat文件都是我修改后的内容。

主要修改包括：

1. 适配Windows 10下，python3.7 + mxnet1.7 + cuda10.2 的单GPU（NVidia GTX 1060 6G显存）学习环境。
2. 重写了预处理的prepare_data.py文件，重写了加载数据和label的customdataset.py文件。重写后的文件前缀了"my_"。
3. 新增一个my_predict.py文件，用于预测和导出模型。
4. 其他一些我训练和预测过程中发现的小bug的修正。

The following passages are the orginal README. 以下是原作者的README。

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