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UAV-AdversarialLearning
PyTorch Code for 'Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach'
Introduction
PyTorch Implementation of our ICCV 2019 paper "Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach ".
Environment
- Python 2.7 or 3.6
- Pytorch 0.4.0
- CUDA 8.0 or higher
Multiple GPU Training
#!/bin/bash
CUDA_VISIBLE_DEVICES=0,1,2,3 python trainval_net_monitor.py --use_tfb --cuda --mGPUs --monitor_discriminator --use_restarting --use_adversarial_loss --gamma_altitude 0.01 --gamma_angle 0.01 --gamma_weather 0.01 --angle_thresh 0.9 --altitude_thresh 0.9 --weather_thresh 0.9 --bs 8
Single GPU Testing
#!/bin/bash
for ((i=1; i<=7; i++))
do
epoch=$(($i*1000/3138+1))
ckpt=$(($i*1000%3138))
echo "$epoch"
echo "$ckpt"
CUDA_VISIBLE_DEVICES=1 python test_net.py --cuda --checkepoch "$epoch" --checkpoint "$ckpt" --gamma_altitude 0.0 --gamma_angle 0.01 --gamma_weather 0.01 --use_restarting
done
Pretrained Model on UAVDT
Google Drive: https://drive.google.com/file/d/1kw-QpnBW5RkKfoG9iH83DxH9uhCgIxix/view?usp=sharing
UAVDT Data (Training+Testing) in Pascal VOC Format
Google Drive: https://drive.google.com/file/d/1pvMjEr6LsrISpx-GuOLJW53toF35JkRy/view?usp=sharing
UAVDT Trained Model (w/o Adversarial Loss and w/ Adversarial Loss)
Google Drive: https://drive.google.com/file/d/1rxqr0Cq0y9cXhdWyNd_R_8cd68exD1wn/view?usp=sharing
VisDrone Meta-data Link
Google Drive: https://drive.google.com/file/d/1FcdPJXggs31HpYsNfqyZFMsI3qwPB54Q/view?usp=sharing
Project Directory Layout
.
├── cfgs
├── data # UAVDT dataset with annotation
├── images
├── lib
├── logs # TensorBoard event files
├── models # Trained model (w/ adversarial loss and w/o adversarial loss)
├── output
├── summaries # Summary files recording the training and validation performance
├── README.md
├── _init_paths.py
├── bash_run.sh # Run the testing in batch
├── demo.py
├── requirements.txt
├── test_net.py
├── trainval_net.py
└── trainval_net_monitor.py
Citation
If you find this code useful, please cite the following paper:
@article{wu2019delving,
title={Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach},
author={Wu, Zhenyu and Suresh, Karthik and Narayanan, Priya and Xu, Hongyu and Kwon, Heesung and Wang, Zhangyang},
journal={arXiv preprint arXiv:1908.03856},
year={2019}
}