September 2, 2019

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InstaGAN: Instance-aware Image Translation (ICLR 2019)

repo name sangwoomo/instagan
repo link
language Python
size (curr.) 61047 kB
stars (curr.) 700
created 2018-12-16
license Other

InstaGAN: Instance-aware Image-to-Image Translation

PyTorch implementation of “InstaGAN: Instance-aware Image-to-Image Translation” (ICLR 2019). The implementation is based on the official CycleGAN code. Our major contributions are in ./models/ and ./models/

Getting Started


  • Clone this repository
git clone
pip install -r requirements.txt
  • For Conda users, you can use a script ./scripts/ to install PyTorch and other libraries.

  • Acknowledgment: Installation scripts are from the official CycleGAN code.

Download base datasets

git clone ./datasets/clothing-co-parsing
# Download "LV-MHP-v1" from the link and locate in ./datasets

Generate two-domain datasets

  • Generate two-domain dataset for experiments:
python ./datasets/ --save_root ./datasets/jeans2skirt_ccp --cat1 jeans --cat2 skirt
python ./datasets/ --save_root ./datasets/pants2skirt_mhp --cat1 pants --cat2 skirt
python ./datasets/ --save_root ./datasets/shp2gir_coco --cat1 sheep --cat2 giraffe
  • Note: Generated dataset contains images and corresponding masks, which are located in image folders (e.g., ‘trainA’) and mask folders (e.g., ‘trainA_seg’), respectively. For each image (e.g., ‘0001.png’), corresponding masks for each instance (e.g., ‘0001_0.png’, ‘0001_1.png’, …) are provided.

Run experiments

  • Train a model:
python --dataroot ./datasets/jeans2skirt_ccp --model insta_gan --name jeans2skirt_ccp_instagan --loadSizeH 330 --loadSizeW 220 --fineSizeH 300 --fineSizeW 200 --niter 400 --niter_decay 200
python --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 270 --loadSizeW 180 --fineSizeH 240 --fineSizeW 160
python --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 220 --loadSizeW 220 --fineSizeH 200 --fineSizeW 200
  • To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. To see more intermediate results, check out ./checkpoints/experiment_name/web/index.html.

  • For faster experiment, increase batch size and use more gpus:

python --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 220 --loadSizeW 220 --fineSizeH 200 --fineSizeW 200 --batch_size 4 --gpu_ids 0,1,2,3
  • Test the model:
python --dataroot ./datasets/jeans2skirt_ccp --model insta_gan --name jeans2skirt_ccp_instagan --loadSizeH 300 --loadSizeW 200 --fineSizeH 300 --fineSizeW 200
python --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 240 --loadSizeW 160 --fineSizeH 240 --fineSizeW 160 --ins_per 2 --ins_max 20
python --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 200 --loadSizeW 200 --fineSizeH 200 --fineSizeW 200 --ins_per 2 --ins_max 20
  • The test results will be saved to a html file here: ./results/experiment_name/latest_test/index.html.

Apply a pre-trained model

  • You can download a pre-trained model (pants->skirt and/or sheep->giraffe) from the following Google drive link. Save the pretrained model in ./checkpoints/ directory.

  • We provide samples of two datasets (pants->skirt and sheep->giraffe) in this repository. To test the model:

python --dataroot ./datasets/pants2skirt_mhp --model insta_gan --name pants2skirt_mhp_instagan --loadSizeH 240 --loadSizeW 160 --fineSizeH 240 --fineSizeW 160 --ins_per 2 --ins_max 20 --phase sample --epoch 200
python --dataroot ./datasets/shp2gir_coco --model insta_gan --name shp2gir_coco_instagan --loadSizeH 200 --loadSizeW 200 --fineSizeH 200 --fineSizeW 200 --ins_per 2 --ins_max 20 --phase sample --epoch 200


We provide some translation results of our model. See the link for more translation results.

1. Fashion dataset (pants->skirt)

2. COCO dataset (sheep->giraffe)

3. Results on Google-searched images (pants->skirt)

4. Results on YouTube-searched videos (pants->skirt)


If you use this code for your research, please cite our papers.

    title={InstaGAN: Instance-aware Image-to-Image Translation},
    author={Sangwoo Mo and Minsu Cho and Jinwoo Shin},
    booktitle={International Conference on Learning Representations},
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