Please refer to get_started.md for the preparation of the datasets and pretrained checkpoints.
The following is an example of model training on the RefCOCOg dataset.
python -m torch.distributed.launch --nproc_per_node=2 --use_env train.py --config configs/LVVVG_R50_gref.py
We train the model on 2 GPUs with a total batch size of 64 for 90 epochs.
The model and training hyper-parameters are defined in the configuration file LVVVG_R50_gref.py.
We prepare the configuration files for different datasets in the configs/ folder.
Run the following script to evaluate the trained model with a single GPU.
python test.py --config configs/LVVVG_R50_gref.py --checkpoint LVVVG_R50_gref.pth --batch_size_test 16 --test_split val
Or evaluate the trained model with 2 GPUs:
python -m torch.distributed.launch --nproc_per_node=2 --use_env test.py --config configs/LVVVG_R50_gref.py --checkpoint LVVVG_R50_gref.pth --batch_size_test 16 --test_split val
Part of our code is based on the previous works DETR，ReSC and VLTVG.