This repo will adress the following aspects of using AWS for training deep learning models

• General setup for AWS EC2 (Elastic Compute Cloud) and S3 Storage Classes for training deep learning model using Pytorch
  • The implementation for paper Deep Fashion Analysis with Feature Map Upsampling and Landmark-driven Attention (2018) is taken as example to show the workflow
  • Manage the remote terminal sessions with tmux.
• Data preprocessing for duplicated attributes.
• Load the GPU-trained model and run on local machine.

General setup for AWS EC2 (Elastic Compute Cloud) and S3 Storage Bucket.

Data preprocessing for duplicated attributes.

After following the post's creating info.csv step, it is necessary to do some data cleaning since the original DeepFashion Dataset has a lot duplicated labels, such as : stripe, stripes, striped are listed as separate attributes.

The demostration for running the scripts are inside the data_cleaning file.

Using preprocessed data for training could significantly improve the accuracy, the results are follows:

The following table shows the category classification and attribute prediction results on the DeepFashion dataset for the original dataset and our pre-processed dataset. The two numbers in each cell stands for top-3 and top-5 accuracy.

Methods	Category	Texture	Fabric	Shape	Part	Style	All
Liu.et al.	91.16 | 96.12	56.17 | 65.83	43.20 | 53.52	58.28 | 67.80	46.97 | 57.42	68.82 | 74.13	54.69 | 63.74
Ours	90.99 | 95.88	69.92 | 78.86	50.42 | 60.91	64.02 | 72.43	59.03 | 68.87	37.42 | 46.40	31.13 | 46.40

Load GPU-trained model on local machine

The workflow for this specific example will provide a standard practice for loading trained model and put it into use on your local machine. the code is in file Model-Implementation.

The demostration for using the classification model is in the readme.md inside Model-Implementation file.