| --- |
| 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}, |
| } |
| ``` |
