This is the official repository for the paper Physically Realizable Natural-looking Clothing Textures Evade Person Detectors via 3D Modeling.

All the codes are tested in the following environment:

• Linux (Ubuntu 16.04.6)
• Python 3.8.13
• PyTorch 1.10.1
• pytorch3d 0.6.2
• CUDA 11.0
• TensorboardX 2.5.1

The data and checkpoints are shared by Google Drive. You need to download it and place the data folder in the root directory of this project. If you want to evaluate the checkpoints, place the results folder also in the root directory and follow the instructions in the section of Evaluation.

If you are going to use yolov3, you need to download its weights by running

./arch/weights/download_weights.sh

We provide the command to optimize AdvCaT for different target detectors.

python train.py --nepoch 600 --save_path 'results/rcnn_sr07' --ctrl 50 --arch "rcnn" --seed_type variable --clamp_shift 0.01 --loss_type max_iou --seed_ratio 0.7

python train.py --nepoch 600 --save_path 'results/deformable_detr_07' --ctrl 50 --arch "deformable-detr" --seed_type variable --clamp_shift 0.01 --loss_type max_iou --seed_ratio 0.7

python train.py --nepoch 600 --save_path 'results/yolov3_07' --ctrl 50 --arch "yolov3" --seed_type variable --clamp_shift 0.01 --loss_type max_iou --seed_ratio 0.7

We provide the command to evaluate AdvCaT and visualize the result. For example, to evaluate the pattern saved in directory 'results/rcnn_sr07' targeting FasterRCNN, run

python train.py --device --checkpoint 600 --save_path 'results/rcnn_sr07' --ctrl 50 --arch "rcnn" --seed_type variable --clamp_shift 0.01 --seed_ratio 0.7 --test

To visualize the evaluation results, run

python visualize.py