AiFashion

• Author: VictorLi, yuanyuan.li85@gmail.com
• Code for FashionAI Global Challenge—Key Points Detection of Apparel 2018 TianChi
• Rank 45/2322 at 1st round competition, score 0.61
• Rank 46 at 2nd round competition, score 0.477

Images with detected keypoints

Dress

Blouse

Outwear

Skirt

Trousers

Basic idea

• The key idea comes from paper Cascaded Pyramid Network for Multi-Person Pose Estimation. We have a 2 stage network called global net and refine net who are U-net like. The network was trained to detect the heatmap of cloth's key points. The backbone network used here is resnet101.
• To overcome the negative impact from different category, input_mask was introduced to zero the invalid keypoints. For example, skirt has 4 valid keypoints: waistband_left, waistband_right, hemline_left and hemline_right. In input_mask, only those valid masks are 1.0 , while other 20 masks are set as zero.
• On line hard negative mining, at last stage of refinenet, only take the top losses as consideration and ignore the easy part (small loss)

Dependency

• Keras2.0
• Tensorflow
• Opencv/Numpy/Pandas
• Pretrained model weights, resenet101

Folder Structure

• data: folder to store training and testing images and annotations
• trained_models: folder to store trained models and logs
• submission: folder to store generated submission for evaluation.
• src: folder to put all of source code.
  src/data_gen: code for data generator including data augmentation and pre-process
  src/eval: code for evaluation, including inference and post-processing.
  src/unet: code for cnn model definition, including train, fine-tune, loss, optimizer definition.
  src/top:top level code for train, test and demo.

How to train network

• Download dataset from competition webpage and put it under data.
  data/train : data used as train. data/test : data used for test
• Download resnet101 model and save it as data/resnet101_weights_tf.h5.
  Note: all the models here use channel_last dim order.
• Train all-in-one network from scratch

python train.py --category all --epochs 30 --network v11 --batchSize 3 --gpuID 2

• The trained model and log will be put under trained_models/all/xxxx, i.e trained_models/all/2018_05_23_15_18_07/
• The evaluation will run for each epoch and details saved to val.log
• Resume training from a specific model.

python train.py --gpuID 2 --category all --epochs 30 --network v11 --batchSize 3 --resume True --resumeModel /path/to/model/start/with --initEpoch 6

How to test and generate submission

• Run test and generate submission Below command search the best score from modelpath and use that to generate submission

python test.py --gpuID 2 --modelpath ../../trained_models/all/xxx --outpath ../../submission/2018_04_19/ --augment True

The submission will be saved as submission.csv

How to run demo

• Download the pre trained weights from BaiduDisk (password 1ae2) or GoogleDrive
• Save it somewhere, i.e trained_models/all/fashion_ai_keypoint_weights_epoch28.hdf5
• Or use your own trained model.
• Run demo and the cloth with keypoints marked will be displayed.

python demo.py --gpuID 2 --modelfile ../../trained_models/all/fashion_ai_keypoint_weights_epoch28.hdf5

Reference

• Resnet 101 Keras : https://github.com/statech/resnet