This repository has all the codes needed to replicate the results from our following paper in AAAI 2023:

1. K. Seemakurthy, C. Fox, E. Aptoula, and P. Bosilj, "Domain Generalised Faster R-CNN", AAAI 2023 (accepted)

We used the following datasets for our experiments.

1. Global Wheat Head Detection (GWHD)
2. Cityscapes
3. Sim10K
4. Berkely Deep Drive 100K (BDD100K)

Our code expects the input data format to be in csv format and we provide necessary helper functions to convert the annotations from JSON (Cityscapes, BDD100K), XML (Sim10K) formats into csv format for all the datasets. But we expect the users to download the datasets from respective websites and follow the file structure mentioned below so that the code can directly access the datasets. The file requirements.txt has all the dependencies needed to run this code.

./downloads.sh  #GWHD and Sim10K can be downloaded in 'datasets' folder by execuitng this command 

BDD100K, Cityscapes, Foggy-cityscapes, Rain-Cityscapes need to be downloaded manually from their respective websites and arrange them in the following directory structure.

.
├── datasets
│   ├── bdd100k
    |     ├── images
    |     |     ├── 100k
    |     |           ├── train
    |     |           ├── val
    |     |           ├── test
    |     ├── labels
    |     |     ├── det20
    |     |           ├── det_train.json
    |     |           ├── det_val.json
    ├── sim10k
    |     ├── VOC2012
    |     |       ├── Annotations
    |     |       ├── JPEGImages
    ├── gtFine
    |     ├── train
    |     ├── val
    |     ├── test
    ├── cityscapes_clear
    |     ├── train
    |     ├── val
    |     ├── test
    ├── cityscapes_foggy
    |     ├── train
    |     ├── val
    |     ├── test
    ├── cityscapes_rain
    |     ├── train
    |     ├── val
    |     ├── test
    ├── gwhd_2021
    |     ├── images
    ├── downloads.sh
    ├── to_csv_conversion.sh
    ├── Annots
    ├── foggy_trainval_filenames.txt
    ├── rain_trainval_filenames.txt

Once above directory structure is ensured, the following command needs to be executed to convert all the annotations into csv format and place them in Annots as needed by our code.

./to_csv_conversion.sh

The above command will generate the following 12 csv files in Annots folder where a subset of them will be used by the detector during training.

1. bdd100k_car_train.csv
2. bdd100k_car_val.csv
3. cityscapes_clear_all_train.csv
4. cityscapes_clear_all_val.csv
5. cityscapes_clear_car_train.csv
6. cityscapes_clear_car_val.csv
7. cityscapes_foggy_train.csv
8. cityscapes_foggy_val.csv
9. cityscapes_rain_train.csv
10. cityscapes_rain_val.csv
11. sim10k_train_car.csv
12. sim10k_val_car.csv

We recommend the users to use 'Anaconda' to create a virtual environment. The following command can be used to create a new environment needed for replicating the results in this paper.

conda create -n DGFRCNN python

Once the environment is succesfully created, it needs to be activated using the following command.

conda activate DGFRCNN

We recommend to use the following command to install all the dependencies inside the DGFRCNN environment

pip install -r requirements.txt

In this code, we need to train additional domain specific classifiers for which we need the access to ground truth labels of each identified region proposal. We have made minor changes to the Faster-RCNN implementation in WilDS to obtain the ground truth labels of each region proposal. We initialise our ResNet backbone with COCO pretrained weights for experimenting with Cityscapes, Sim10K, BDD100K while we obtained better accuracy for GWHD using ImageNet pretrained weights. We use the Pytorch-Lightning framework to train our model.

The following command can be executed to replicate the results in Table 1 (Cityscapes-->Foggy-Cityscapes, Cityscapes-->Rainy-Cityscapes). For upper half of the Table 1, uncomment the line 707 and comment 706 in train_cityscapes.py file. For lower half of the Table 1, uncomment the line 706 and comment 707 in train_cityscapes.py file.

python train_cityscapes.py

The following commands need to be executed to replicate the results in Table 2. For the results on (S, C) --> B uncomment the lines 145-147 in train_BSC.py For the results on (S, B) --> C uncomment the lines 150-152 in train_BSC.py For the results on (B, C) --> S uncomment the lines 155-157 in train_BSC.py

python train_GWHD.py
python train_BSC.py

The following command can be executed to replicate the results in Table 3.

python train_DA.py