Leveraging Two Types of Global Graph for Sequential Fashion Recommendation, ICMR 2021

Related tags

Deep LearningDGSR
Overview

This is the repo for the paper: Leveraging Two Types of Global Graph for Sequential Fashion Recommendation

Requirements

  1. OS: Ubuntu 16.04 or higher version
  2. python3.7
  3. Supported (tested) CUDA Versions: V10.2
  4. python modules: refer to the modules in requirements.txt

Code Structure

  1. The entry script for training and evaluation is: train.py
  2. The config file is: config.yaml
  3. The script for data preprocess and dataloader: utility.py
  4. The model folder: ./model/.
  5. The experimental logs in tensorboard-format are saved in ./logs.
  6. The experimental logs in txt-format are saved in ./performance.
  7. The best model for each experimental setting is saved in ./model_saves.
  8. The recommendation results in the evaluation are recorded in ./results.
  9. The ./logs, ./performance, ./model_saves, ./results files will be generated automatically when first time runing the codes.
  10. The script get_all_the_res.py is used to print the performance of all the trained and tested models on the screen.

How to Run

  1. Download the dataset, decompress it and put it in the top directory with the following command. Note that the downloaded files include two datasets ulilized in the paper: iFashion and amazon_fashion.

    tar zxvf dgsr_dataset.tar.gz. 
    
  2. Settings in the configure file config.yaml are basic experimental settings, which are usually fixed in the experiments. To tune other hyper-parameters, you can use command line to pass the parameters. The command line supported hyper-parameters including: the dataset (-d), sequence length (-l) and embedding size (-e). You can also specify which gpu device (-g) to use in the experiments.

  3. Run the training and evaluation with the specified hyper-parameters by the command:

    python train.py -d=ifashion -l=5 -e=50 -g=0. 
    
  4. During the training, you can monitor the training loss and the evaluation performance by Tensorboard. You can get into ./logs to track the curves of your training and evaluation with the following command:

    tensorboard --host="your host ip" --logdir=./
    
  5. The performance of the model is saved in ./performance. You can get into the folder and check the detailed training process of any finished experiments (Compared with the tensorboard log save in ./logs, it is just the txt-version human-readable training log). To quickly check the results for all implemented experiments, you can also print the results of all experiments in a table format on the terminal screen by running:

    python get_all_the_res.py
    
  6. The best model will be saved in ./model_saves.

Owner
Yujuan Ding
Yujuan Ding
Real-Time Semantic Segmentation in Mobile device

Real-Time Semantic Segmentation in Mobile device This project is an example project of semantic segmentation for mobile real-time app. The architectur

708 Jan 01, 2023
Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021

Deep Representation One-class Classification (DROC). This is not an officially supported Google product. Tensorflow 2 implementation of the paper: Lea

Google Research 137 Dec 23, 2022
Pytorch Implementation for Dilated Continuous Random Field

DilatedCRF Pytorch implementation for fully-learnable DilatedCRF. If you find my work helpful, please consider our paper: @article{Mo2022dilatedcrf,

DunnoCoding_Plus 3 Nov 13, 2022
Commonsense Ability Tests

CATS Commonsense Ability Tests Dataset and script for paper Evaluating Commonsense in Pre-trained Language Models Use making_sense.py to run the exper

XUHUI ZHOU 28 Oct 19, 2022
[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects YouTube | arXiv Prerequisites Kaolin is available here:

Denys Rozumnyi 107 Dec 26, 2022
This is a repository of our model for weakly-supervised video dense anticipation.

Introduction This is a repository of our model for weakly-supervised video dense anticipation. More results on GTEA, Epic-Kitchens etc. will come soon

2 Apr 09, 2022
1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

Lihe Yang 209 Jan 01, 2023
A PaddlePaddle version of Neural Renderer, refer to its PyTorch version

Neural 3D Mesh Renderer in PadddlePaddle A PaddlePaddle version of Neural Renderer, refer to its PyTorch version Install Run: pip install neural-rende

AgentMaker 13 Jul 12, 2022
Official Implementation of DDOD (Disentangle your Dense Object Detector), ACM MM2021

Disentangle Your Dense Object Detector This repo contains the supported code and configuration files to reproduce object detection results of Disentan

loveSnowBest 51 Jan 07, 2023
Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

Nerdy Rodent 2.3k Jan 04, 2023
Structured Edge Detection Toolbox

################################################################### # # # Structure

Piotr Dollar 779 Jan 02, 2023
a pytorch implementation of auto-punctuation learned character by character

Learning Auto-Punctuation by Reading Engadget Articles Link to Other of my work 🌟 Deep Learning Notes: A collection of my notes going from basic mult

Ge Yang 137 Nov 09, 2022
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification [NeurIPS 2021] Abstract Multiple instance learn

132 Dec 30, 2022
Multimodal Temporal Context Network (MTCN)

Multimodal Temporal Context Network (MTCN) This repository implements the model proposed in the paper: Evangelos Kazakos, Jaesung Huh, Arsha Nagrani,

Evangelos Kazakos 13 Nov 24, 2022
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Adversarial Machine Learning 9 Nov 27, 2022
This is a code repository for the paper "Graph Auto-Encoders for Financial Clustering".

Repository for the paper "Graph Auto-Encoders for Financial Clustering" Requirements Python 3.6 torch torch_geometric Instructions This is a simple c

Edward Turner 1 Dec 02, 2021
[CVPR'22] Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast

wseg Overview The Pytorch implementation of Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast. [arXiv] Though image-level weakly

Ye Du 96 Dec 30, 2022
Pytorch reimplementation of the Mixer (MLP-Mixer: An all-MLP Architecture for Vision)

MLP-Mixer Pytorch reimplementation of Google's repository for the MLP-Mixer (Not yet updated on the master branch) that was released with the paper ML

Eunkwang Jeon 18 Dec 08, 2022
Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically.

Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically. The collected data will then be used to train a deep neural network that can

Martin Valchev 3 Apr 24, 2022
State-to-Distribution (STD) Model

State-to-Distribution (STD) Model In this repository we provide exemplary code on how to construct and evaluate a state-to-distribution (STD) model fo

<a href=[email protected]"> 2 Apr 07, 2022