PyTorch Implementation of the paper Single Image Texture Translation for Data Augmentation

Related tags

Text Data & NLP SITT
Overview

SITT

The repo contains official PyTorch Implementation of the paper Single Image Texture Translation for Data Augmentation.

Authors:

Overview

Recent advances in image synthesis enables one to translate images by learning the mapping between a source domain and a target domain. Existing methods tend to learn the distributions by training a model on a variety of datasets, with results evaluated largely in a subjective manner. Relatively few works in this area, however, study the potential use of semantic image translation methods for image recognition tasks. In this paper, we explore the use of Single Image Texture Translation (SITT) for data augmentation. We first propose a lightweight model for translating texture to images based on a single input of source texture, allowing for fast training and testing. Based on SITT, we then explore the use of augmented data in long-tailed and few-shot image classification tasks. We find the proposed method is capable of translating input data into a target domain, leading to consistent improved image recognition performance. Finally, we examine how SITT and related image translation methods can provide a basis for a data-efficient, augmentation engineering approach to model training.

Usage

Environment

CUDA 10.1, pytorch 1.3.1

Dataset Preparation

dataset url
0 SITT leaves images from Plant Pathology 2020 download

Running

bash run.sh

More will be updated

If you find this repo useful, please cite:

@article{li2021single,
  title={Single Image Texture Translation for Data Augmentation},
  author={Li, Boyi and Cui, Yin and Lin, Tsung-Yi and Belongie, Serge},
  journal={arXiv preprint arXiv:2106.13804},
  year={2021}
}
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Comments
  • hyperparameters

    hyperparameters

    Dear @Boyiliee , I am trying to implement your paper in code. Some hyperparameters are omitted from the paper , so it is difficult to implement. Could you please tell me batch size and lambda values ​​of loss? Thank you.

    opened by qwopqwop200 3
  • demo data

    demo data

    Dear @Boyiliee ,

    Thanks for sharing the code.

    Would it be possible for you to provide some demo data to illustrate how to use the code with a new dataset?

    opened by JunMa11 2
  • Question about the kl divergence loss

    Question about the kl divergence loss

    According to the paper, Kl divergence loss is computed between textures t_A, t_ba and ** t_B, t_ab**, however the computation:

    loss_netG_A_texture = -0.5 * (F.kl_div(t_A, t_ba) + F.kl_div(t_ba, t_A))
loss_netG_B_texture = -0.5 * (F.kl_div(t_B, t_ab) + F.kl_div(t_ab, t_B))

    Looks more like the JS divergence but with a negative sign.

    Also the inputs for the Kl divergence loss are supposed to be in the log softmax space but the textures t_A, t_ba, t_B, t_ab come from the ReLU "space", is this the reason behind the negative sign in the equation? or am I losing some detail about the implementation?

    opened by vincent1bt 0
Owner
Boyi Li
Boyi Li
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