Add dataset card for Inst2Seg

README.md

@@ -0,0 +1,54 @@
+---
+task_categories:
+- image-segmentation
+tags:
+- instance-segmentation
+- instruction-following
+---
+
+# Inst2Seg
+
+Inst2Seg is a high-quality and large-scale instruction-based instance segmentation dataset and benchmark introduced in the paper [InstructSAM: Segment Any Instance with Any Instructions](https://huggingface.co/papers/2605.26102). 
+
+The dataset couples free-form instructions with instance-level masks to support training and evaluation for unified instruction-driven segmentation. It covers several types of instructions:
+- **Category prompts**
+- **Referring expressions**
+- **Reasoning-style instructions**
+
+## Project Resources
+
+- **Paper:** [InstructSAM: Segment Any Instance with Any Instructions](https://huggingface.co/papers/2605.26102)
+- **Code:** [GitHub - InstructSAM](https://github.com/DCDmllm/InstructSAM)
+
+## Dataset Description
+
+Inst2Seg is designed to address the challenges of multi-instance segmentation under arbitrary natural-language instructions. It enables models to perform high-level instruction understanding and instance-level set prediction. Unlike traditional semantic segmentation, which focuses on regions, Inst2Seg requires models to resolve compositional reasoning and enumerate specific instances based on the provided instructions.
+
+## Data Structure and Usage
+
+According to the [official repository](https://github.com/DCDmllm/InstructSAM), the dataset typically consists of annotation files in JSON format. To use the dataset:
+
+1. Download the training and evaluation annotation JSON files from the project's data repository.
+2. Download the raw images from the official sources of the original datasets (e.g., COCO, RefCOCO, etc.).
+3. Place the JSON files under `data/training` and `data/eval` as specified in the setup instructions.
+
+Evaluation can be performed using the provided scripts:
+```bash
+bash evaluation/scripts/eval_inst2seg.sh
+bash evaluation/scripts/eval_reasonseg.sh
+bash evaluation/scripts/eval_grefcoco_ap.sh
+bash evaluation/scripts/eval_roborefit.sh
+```
+
+## Citation
+
+If you find this dataset or the associated framework useful, please cite:
+
+```bibtex
+@article{yuan2026instructsam,
+  title     = {InstructSAM: Segment Any Instance with Any Instructions},
+  author    = {Yuqian Yuan, Wentong Li, Zhaocheng Li, Yutong Lin, Juncheng Li, Siliang Tang, Jun Xiao, Yueting Zhuang, Wenqiao Zhang},
+  year      = {2026},
+  journal   = {arXiv},
+}
+```