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Repository Details

PyTorch implementation of the paper "Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval", CVPR 2019.

SEM-PCYC

PyTorch | Arxiv

PyTorch implementation of our SEM-PCYC model for zero-shot sketch-based image retrieval:
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval
Anjan Dutta, Zeynep Akata
CVPR, 2019

Demo Video

Live Demo

Retrieval Results

Sketchy






TU-Berlin






Prerequisites

  • Linux (tested on Ubuntu 16.04)
  • NVIDIA GPU + CUDA CuDNN
  • 7z
sudo apt-get install p7zip-full

Getting Started

Installation

  • Clone this repository
git clone https://github.com/AnjanDutta/sem-pcyc.git
cd sem-pcyc
  • Install the requirements (not checked)
pip3 install -r requirements.txt
[<host>]
path_dataset = <where all the datasets will be downloaded>
path_aux = <where all the auxiliary folders and files will be saved>

Download datasets

  • Sketchy
  • TU-Berlin
bash download_datasets.sh

Download pretrained models

  • Sketchy
    • sketch
    • image
    • hieremb-jcn + word2vec-google-news
  • TU-Berlin
    • sketch
    • image
    • hieremb-path + word2vec-google-news
bash download_models.sh

Test

Sketchy
python3 src/test.py --dataset Sketchy_extended --dim-out 64 --semantic-models hieremb-jcn word2vec-google-news
TU-Berlin
python3 src/test.py --dataset TU-Berlin --dim-out 64 --semantic-models hieremb-path word2vec-google-news

Train

Sketchy
python3 src/train.py --dataset Sketchy_extended --dim-out 64 --semantic-models word2vec-google-news --epochs 1000 --early-stop 200 --lr 0.0001
TU-Berlin
python3 src/train.py --dataset TU-Berlin --dim-out 64 --semantic-models word2vec-google-news --epochs 1000 --early-stop 200 --lr 0.0001

Citation

@inproceedings{Dutta2019SEMPCYC,
author = {Anjan Dutta and Zeynep Akata},
title = {Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval},
booktitle = {CVPR},
year = {2019}
}

Author