ControlAug

Official Implementation for "Data Augmentation for Object Detection via Controllable Diffusion Models" (accepted as poster in WACV 2024).

Preparations

Setup

Clone current repository

git clone https://github.com/FANGAreNotGnu/ControlAug.git

Clone ControlNet repository. Make sure all repositories are under the same folder.

git clone https://github.com/FANGAreNotGnu/ControlNet.git

Clone MMDetection repository. Make sure all repositories are under the same folder.

git clone https://github.com/FANGAreNotGnu/mmdetection.git

Create a Conda Environment for ControlNet (environment name: ControlAug_control)

conda env create -f ControlNet/environment.yaml

Create a Conda Environment for CLIP (environment name: ControlAug_clip). conda env create -f ControlAug/environment/ControlAug_clip.yaml

Create a Conda Environment for Diffuser (environment name: ControlAug_diffuser). conda env create -f ControlAug/environment/ControlAug_diffuser.yaml

Create a Conda Environment for MMDetection (environment name: ControlAug_mmdet).

conda env create -f ControlAug/environment/ControlAug_mmdet.yaml
conda activate ControlAug_mmdet
mim install mmcv==2.0.1
pip install mmdet==3.1.0
conda deactivate ControlAug_mmdet

Export Paths

source ./ControlAug/scripts/export_paths.sh

Download

Download Data

Download COCO FSOD. Make sure paths are exported.

bash ./ControlAug/scripts/download_coco_fsod.sh

Download ControlNet Checkpoints

Download ControlNet Checkpoints. Make sure paths are exported.

bash ./ControlAug/scripts/download_cnet_ckpts.sh

Run Augmentation Pipeline

bash ./coco10_cat_pipe.sh 0 1 10 512 333 HED blip_large_coco 30 control_sd15_hed.pth