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:
```BibTex
@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}
}