Acknowledgement

This codebase has been developed on ViDT. We thanks the authors of ViDT for providing the code.

Installation Instructions

This codebase has been developed with the setting used in Deformable DETR:
Linux, CUDA>=9.2, GCC>=5.4, Python>=3.7, PyTorch>=1.5.1, and torchvision>=0.6.1.

We recommend you to use Anaconda to create a conda environment:

conda create -n deformable_detr python=3.7 pip
conda activate deformable_detr
conda install pytorch=1.5.1 torchvision=0.6.1 cudatoolkit=9.2 -c pytorch

Compiling CUDA operators for deformable attention

cd ./ops
sh ./make.sh
# unit test (should see all checking is True)
python test.py

Other requirements

pip install -r requirements.txt

Training

The training script is provided in this code base. Run the following script

CUDA_VISIBLE_DEVICES=0 python main.py \
                        --method vidt \
                        --backbone_name swin_tiny \
                        --epochs 12 \
                        --lr 1e-4 \
                        --min-lr 1e-7 \
                        --batch_size 7 \
                        --num_workers 14 \
                        --aux_loss True \
                        --with_box_refine True \
                        --coco_path /path/to/coco \
                        --output_dir /path/to/output/dir \
                        --start_epoch 0 \
                        --lr_drop 20 \
                        --warmup-epochs 0 \
                        --resume /path/to/model \
                        

Evaluation

For evaluation run the following script

CUDA_VISIBLE_DEVICES=0 python main.py \
                        --method vidt \
                        --backbone_name swin_tiny \
                        --batch_size 7 \
                        --num_workers 14 \
                        --coco_path /path/to/coco \
                        --output_dir /path/to/output/dir \
                        --resume /path/to/model \
                        --eval True
                     

Datasets

We use the folowing datasets in our work:

1. MS-COCO
2. Sketchy
3. QuickDraw