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Competition Name: Generative-AI Navigation Information Competition for UAV Reconnaissance in Natural Environments I:Image Data Generation
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Team Name: TEAM_5101
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Final Competition Results:
Testing Dataset FID Score Rank Private 89.09644 4 Public 88.878136 6 -
Key Technologies:
Tengfei Wang, Ting Zhang, Bo Zhang, Hao Ouyang, Dong Chen, Qifeng Chen, Fang Wen
2022
paper | project website | video | online demo
We present a simple and universal framework that brings the power of the pretraining to various image-to-image translation tasks.
Diverse samples synthesized by our approach.
git clone https://github.com/PITI-Synthesis/PITI.git
cd PITI
sudo apt-get update
sudo apt-get install openmpi-bin libopenmpi-dev -y
conda env create -f environment.yml
conda activate PITI
conda install -c conda-forge openmpi -y
pip install mpi4py==3.0.3 dlib==19.22.1
pip install gradio
Please download pre-trained models for both Base
model and Upsample
model, and put them in ./ckpt
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Model | Task | Dataset |
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Base-64x64 | Mask-to-Image | Trained on COCO. |
Upsample-64-256 | Mask-to-Image | Trained on COCO. |
Base-64x64 | Sketch-to-Image | Trained on COCO. |
Upsample-64-256 | Sketch-to-Image | Trained on COCO. |
If you fail to access to these links, you may alternatively find our pretrained models here.
Download the following pretrained models into ./ckpt/
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Model | Task | Dataset |
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Base-64x64 | Mask-to-Image | Trained on COCO. |
Upsample-64-256 | Mask-to-Image | Trained on COCO. |
Run the notebook preprocess.ipynb to preprocess training dataset.
Taking mask-to-image synthesis as an example: (sketch-to-image is the same)
Modify mask_finetune_base.sh and run:
bash mask_finetune_base.sh
Run the notebook generate-example.ipynb to generate output images.
If you find this work useful for your research, please cite:
@misc{
title = {Utilizing PITI for Generating Autonomous UAV Images in Natural Environments},
author = {Zhe-Yu Guo},
url = {https://github.com/Tianming8585/PITI},
year = {2024}
}
Thanks for PITI for sharing their code and pretrained models.
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@article{wang2022pretraining, title = {Pretraining is All You Need for Image-to-Image Translation}, author = {Wang, Tengfei and Zhang, Ting and Zhang, Bo and Ouyang, Hao and Chen, Dong and Chen, Qifeng and Wen, Fang}, journal={arXiv:2205.12952}, year = {2022}, }
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@misc{ title = {Apply DP-GAN on Generative-AI Navigation Information Competition for UAV Reconnaissance in Natural Environments}, author = {Wei-Chun Tsao}, url = {https://github.com/Tsao666/DP_GAN}, year = {2024} }