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README.md

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----
-license: cc-by-nc-sa-4.0
----
+# gRefCOCO - Dataset for [CVPR2023 Highlight] GRES: Generalized Referring Expression Segmentation
+[![PyTorch](https://img.shields.io/badge/PyTorch-1.11.0-%23EE4C2C.svg?style=&logo=PyTorch&logoColor=white)](https://pytorch.org/)
+[![Python](https://img.shields.io/badge/Python-3.7%20|%203.8%20|%203.9-blue.svg?style=&logo=python&logoColor=ffdd54)](https://www.python.org/downloads/)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/gres-generalized-referring-expression-1/generalized-referring-expression-segmentation)](https://paperswithcode.com/sota/generalized-referring-expression-segmentation?p=gres-generalized-referring-expression-1)
+
+**[🏠[Project page]](https://henghuiding.github.io/GRES/)**   **[📄[GRES Arxiv]](https://arxiv.org/abs/2306.00968)**   **[📄[GREC Arxiv]](https://arxiv.org/abs/2308.16182)**
+
+This repository contains information and tools for the [gRefCOCO](https://henghuiding.github.io/GRES/) dataset, proposed by the **CVPR2023 Highlight** paper:
+> [GRES: Generalized Referring Expression Segmentation](https://arxiv.org/abs/2306.00968)  
+> Chang Liu, Henghui Ding, Xudong Jiang  
+> CVPR 2023 Highlight, Acceptance Rate 2.5%
+
+<div align="center">
+  <img src="https://github.com/henghuiding/ReLA/blob/main/imgs/fig1.png?raw=true" width="100%" height="100%"/>
+</div><br/>
+
+
+## Usage
+
+ - Like RefCOCO, gRefCOCO also should be used together with images from the `train2014` of [MS COCO](https://cocodataset.org/#download). 
+ - An example of dataloader [grefer.py](https://github.com/henghuiding/gRefCOCO/blob/main/grefer.py) is provided. 
+ - We will update this repository with full API package and documentation soon. Please follow the usage in the [baseline code](https://github.com/henghuiding/ReLA) for now.
+
+## Task 1 - GREC: Generalized Referring Expression Comprehension 
+- The GREC evaluation metric code is [here](https://github.com/henghuiding/gRefCOCO/blob/main/mdetr/datasets/refexp.py).
+
+
+- We provide code based on  [MDETR](https://github.com/ashkamath/mdetr), its training and inference are as follows:
+
+
+### Training (Finetuning)
+1. Process grefcoco to coco format.
+```
+python scripts/fine-tuning/grefexp_coco_format.py --data_path xxx --out_path mdetr_annotations/ --coco_path xxx
+```
+2. Training and download `pretrained_resnet101_checkpoint.pth` from [MDETR](https://github.com/ashkamath/mdetr)
+```
+python -m torch.distributed.launch --nproc_per_node=2 --use_env main.py --dataset_config configs/grefcoco.json --batch_size 4  --load pretrained_resnet101_checkpoint.pth  --ema --text_encoder_lr 1e-5 --lr 5e-5 --output-dir grefcoco
+```
+### Inference
+
+1. Obtain `checkpoint.pth` after training or download trained  model [ here ☁️ Google Drive](https://drive.google.com/file/d/14OrM3n_Oap7xCT6nxj9QEnkJOUMpBGjB/view?usp=drive_link)
+2. For test results, pass --test and --test_type test or testA or testB according to the dataset.
+```
+python -m torch.distributed.launch --nproc_per_node=2 --use_env main.py --dataset_config configs/grefcoco.json --batch_size 4  --resume grefcoco/checkpoint.pth --ema --eval
+```
+## Task 2 - GRES: Generalized Referring Expression Segmentation 
+Please refer to [ReLA](https://github.com/henghuiding/ReLA) for more details.
+
+
+## Acknowledgement
+
+Our project is built upon [refer](https://github.com/lichengunc/refer) and [cocoapi](https://github.com/cocodataset/cocoapi). Many thanks to the authors for their great works!
+
+
+## BibTeX
+Please consider to cite GRES/GREC if it helps your research.
+
+```bibtex
+@inproceedings{GRES,
+  title={{GRES}: Generalized Referring Expression Segmentation},
+  author={Liu, Chang and Ding, Henghui and Jiang, Xudong},
+  booktitle={CVPR},
+  year={2023}
+}
+@article{GREC,
+  title={{GREC}: Generalized Referring Expression Comprehension},
+  author={He, Shuting and Ding, Henghui and Liu, Chang and Jiang, Xudong},
+  journal={arXiv preprint arXiv:2308.16182},
+  year={2023}
+}
+```
+We also recommend other highly related works:
+```bibtex
+@article{VLT,
+  title={{VLT}: Vision-language transformer and query generation for referring segmentation},
+  author={Ding, Henghui and Liu, Chang and Wang, Suchen and Jiang, Xudong},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  year={2023},
+  volume={45},
+  number={6},
+  publisher={IEEE}
+}
+@inproceedings{MeViS,
+  title={{MeViS}: A Large-scale Benchmark for Video Segmentation with Motion Expressions},
+  author={Ding, Henghui and Liu, Chang and He, Shuting and Jiang, Xudong and Loy, Chen Change},
+  booktitle={ICCV},
+  year={2023}
+}
+```

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