Improved DatasetGAN

This is an improved DatasetGAN for truck segmentation

For any code dependency related to Ada-Stylegan2, the license is LICENSE.

The code of DatasetGAN is released under the MIT license. See LICENSE for additional details.

The dataset of DatasetGAN is released under the Creative Commons BY-NC 4.0 license by NVIDIA Corporation. You can use, redistribute, and adapt the material for non-commercial purposes, as long as you give appropriate credit by citing our paper and indicating any changes that you've made.

Requirements

• Python 3.6
• Pytorch 1.4.0.

Quick Start

• Please refer to

• DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

• Training Generative Adversarial Networks with Limited Data

• for detailed setups

Training

cd datasetGAN

1. Interpreter Training

python train_interpreter.py --exp experiments/truck.json 

Download Checkpoints (Password:3t9q)

2. Run GAN Sampling

python train_interpreter.py \
--generate_data True --exp experiments/truck.json  \
--resume [path-to-trained-interpreter in step1] \
--num_sample [num-samples]

Example of annotations

Citations

Please ue the following citation if you use our data or code:

@inproceedings{zhang2021datasetgan,
  title     = {Datasetgan: Efficient labeled data factory with minimal human effort},
  author    = {Zhang, Yuxuan and Ling, Huan and Gao, Jun and Yin, Kangxue and Lafleche, Jean-Francois and Barriuso, Adela and Torralba, Antonio and Fidler, Sanja},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages     = {10145--10155},
  year      = {2021}
}

@inproceedings{Karras2020ada,
  title     = {Training Generative Adversarial Networks with Limited Data},
  author    = {Tero Karras and Miika Aittala and Janne Hellsten and Samuli Laine and Jaakko Lehtinen and Timo Aila},
  booktitle = {Proc. NeurIPS},
  year      = {2020}
}