Awesome

Bilateral Knowledge Interaction Network for Referring Image Segmentation

Environment preparation

conda create -n BKINet python=3.6
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt

Dataset Preparation

1. Download the COCO train2014 to BKINet/ln_data/images.

wget https://pjreddie.com/media/files/train2014.zip

2. Download the RefCOCO, RefCOCO+, RefCOCOg to BKINet/ln_data.

cd ln_data
wget https://bvisionweb1.cs.unc.edu/licheng/referit/data/refclef.zip
wget https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip
wget https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip
wget https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip

3. Run dataset/data_process.py to generate the annotations.

cd dataset
python data_process.py --data_root ../ln_data --output_dir ../ln_data --dataset [refcoco/refcoco+/refcocog] --split unc --generate_mask

4. Process annotations to generate *.pth.

cd dataset
python datascript.py

In line 25, 26, 27, the 'input_txt' 'dataset' 'split' should be modified.

Training

CUDA_VISIBLE_DEVICES = 0 python train_model.py --dataset [refcoco/refcoco+/refcocog]

Testing

CUDA_VISIBLE_DEVICES = 0 python test_model.py --dataset [refcoco/refcoco+/refcocog] --resume saved_models/modelname.pth.tar

Acknowledgement

Thanks for a lot of codes from onestage_grounding , VLT, CLIP, K-Net , DETR.