AnjanDutta / Sem Pcyc
Licence: mit
PyTorch implementation of the paper "Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval", CVPR 2019.
Stars: ✭ 76
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SEM-PCYC
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
- Update config.ini (see example)
[<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}
}
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