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StackGAN Inception Evaluation Model

• StackGAN-pytorch
• StackGAN-tensorflow
• StackGAN-v2-pytorch

Inception evaluation model for reproducing main results in the paper StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks by Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas.

Dependencies

• python 2.7
• TensorFlow

Inception Model

• StackGAN inception model for birds and flowers evaluation. Download and save it in the current folder
• StackGAN for COCO. We use the model pretrained on the ImageNet. Same as the one used in Improved GAN.

To get the inception score

python inception_score.py --image_folder IMAGE_FOLDER_PATH

Citing StackGAN

If you find StackGAN useful in your research, please consider citing:

@inproceedings{han2017stackgan,
Author = {Han Zhang and Tao Xu and Hongsheng Li and Shaoting Zhang and Xiaogang Wang and Xiaolei Huang and Dimitris Metaxas},
Title = {StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks},
Year = {2017},
booktitle = {{ICCV}},
}

References

• Generative Adversarial Text-to-Image Synthesis Paper Code
• Learning Deep Representations of Fine-grained Visual Descriptions Paper Code
• Improved Techniques for Training GANs Paper Code