DODA

Official implementation of Diffusion for Object-detection Domain Adaptation in Agriculture

DODA is a data synthesizer that can generate high-quality object detection data for new domains in agriculture, and help the detectors adapt to the new domains.

Pretrained Models

Model	Dataset	Resolution	Training Iters	Downlad Link
DODA-L2I	COCO	512x512	30K	Google drive
DODA-L2I	COCO	256x256	100K	Google drive
DODA	GWHD2021	256x256	80K	Google drive
DODA-ldm	GWHD2021	256x256	315K	Google drive

Evaluation

Setup Environment

conda create -y -n DODA python=3.8.5
conda activate DODA
pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
pip install -r requirements.txt

Download Datesets

bash Download_dataset.sh

Prepare Datesets

python prepare_coco.py
python prepare_wheat_trainset.py   # If you only want to test the model`s performance on GWHD, there is no need to run this line
python prepare_Terraref_testset.py

Generate Images for Evaluation

Generate images according to the bounding boxes of the COCO 2017 validation set: First download the pretrained DODA-L2I to /models folder, then run:

python generate_coco_testimg.py

Generate images according to the bounding boxes and reference images of the Terraref domain: First download the pretrained DODA to /models folder, then run:

python prepare_Terraref_testset.py

If you want to generate data to train the detector, first generate layout images using random_generate_layout_images.py, then use generate_data_for_target_domain.py to generate the data. If you want to generate data for your own domain, please refer to generate_data_for_target_domain.py

Generate images in GUI

You can try our method to generate images for wheat through the GUI:

python wheat_gradio_box2image.py

Please upload BOTH the reference image and layout image image respectively as shown:

PS: The demo reference image and layout image can be found in the /figures folder. More images can be found in /dataset folder after run prepare_wheat_trainset.py

Or you can simply draw it yourself through drawing software. Each item should have a distinguishable color (with maximized values of the R, G, B channels), for example, (0, 0, 255), (255, 0, 255), etc. Below are some examples of possible layout images:

Train your own DODA

You can download the pretained DODA-ldm, and run tool_add_control.py to add the ControlNet to the model:

python tool_add_wheat_control.py

the train the model by running:

python train_wheat.py