README.md

---
size_categories:
- n<1K
task_categories:
- image-to-image
pretty_name: DOR-Bench
tags:
- dense-scenes
- object-removal
- mask
configs:
- config_name: default
  data_files:
  - split: test
    path: metadata.jsonl
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: visualization
    dtype: image
  splits:
  - name: test
    num_examples: 400
---

# DOR-Bench

DOR-Bench is an image benchmark for evaluating object-removal methods in dense scenes, introduced in the paper [DORS: Dynamic Attention Routing for Diffusion-based Object Removal in Dense Scenes](https://huggingface.co/papers/2607.16656).

- **Project page:** https://httang1224.github.io/DORS/
- **Code:** https://github.com/httang1224/DORS
- **Paper:** https://huggingface.co/papers/2607.16656

This release contains 400 test cases. Each case consists of an input image and a binary mask that identifies the object region to be removed. The benchmark focuses on dense scenes containing visually similar instances, where object-removal methods may incompletely erase the target or introduce duplicated structures and residual artifacts.

## Dataset structure

```text
.
├── images/             # 400 input images (PNG)
├── masks/              # 400 binary masks (PNG)
├── visualizations/     # 400 preview images (JPEG)
├── metadata.jsonl      # File correspondence and sample IDs
└── README.md
```

Files are paired by a shared sample ID:

```text
images/dor_001.png
masks/dor_001.png
visualizations/dor_001.jpg
```

The complete ID range is `dor_001` through `dor_400`.

## Data fields

Each row in `metadata.jsonl` contains:

- `id`: unique sample identifier.
- `image`: path to the input image.
- `mask`: path to the corresponding binary object mask.
- `visualization`: path to a three-panel preview image.

Masks contain two pixel values: `0` for the background and `255` for the target region.

Each visualization is arranged from left to right as:

```text
input image | binary mask | mask overlaid on the input image
```

The overlay uses a light-green fill and a green boundary for visual inspection only. Visualizations are not required when evaluating a method.

## Intended use

DOR-Bench is intended for evaluating whether an object-removal method completely erases the target specified by a binary mask while producing visually coherent content in the removed region.

The benchmark is provided as a single `test` split. It is not intended to be used as training data unless explicitly permitted by the applicable data licenses.

## Loading with Hugging Face Datasets

After replacing the repository ID below with the published Hugging Face dataset ID:

```python
from datasets import load_dataset

dataset = load_dataset("qc1752/DOR-Bench", split="test")
sample = dataset[0]

image = sample["image"]
mask = sample["mask"]
visualization = sample["visualization"]
```

## Citation

If you find this work useful for your research, please consider citing:

```bibtex
@inproceedings{tang2026dors,
  title     = {{DORS}: Dynamic Attention Routing for Diffusion-based
               Object Removal in Dense Scenes},
  author    = {Tang, Haitong and Liu, Haipeng and Wang, Yang},
  booktitle = {Proceedings of the 34th ACM International Conference
               on Multimedia},
  year      = {2026}
}
```