---
license: afl-3.0
task_categories:
  - image-segmentation
task_ids:
  - semantic-segmentation
language:
  - en
tags:
  - robotics
  - drones
  - visual-servoing
  - segmentation
  - computer-vision
  - quadrotor
  - pytorch
  - yolo
size_categories:
  - 10K<n<100K
pretty_name: NSER-IBVS Mask Splitter Dataset
annotations_creators:
  - human
  - machine-assisted
configs:
  - config_name: sim
    data_files:
      - split: train
        path: data/sim/train/**
      - split: validation
        path: data/sim/validation/**
  - config_name: real
    data_files:
      - split: train
        path: data/real/train/**
      - split: validation
        path: data/real/validation/**
default_config_name: sim
dataset_info:
  dataset_size: 11GB
  features:
    - name: image
      dtype: image
    - name: segmentation_mask
      dtype: image
    - name: front_mask
      dtype: image
    - name: back_mask
      dtype: image
    - name: scene
      dtype: string
---

<div align="center">

<h1>NSER-IBVS Mask Splitter Dataset</h1>

[![Paper](https://img.shields.io/badge/Paper-ICCV%202025-blue)](https://openaccess.thecvf.com/content/ICCV2025W/EVW/papers/Mocanu_Efficient_Self-Supervised_Neuro-Analytic_Visual_Servoing_for_Real-time_Quadrotor_Control_ICCVW_2025_paper.pdf)
[![arXiv](https://img.shields.io/badge/arXiv-2507.19878-b31b1b)](https://arxiv.org/abs/2507.19878)
[![GitHub](https://img.shields.io/badge/GitHub-mask--spliter-black?logo=github)](https://github.com/SpaceTime-Vision-Robotics-Laboratory/mask-splitter)

[![Demo](https://img.shields.io/badge/🤗%20Spaces-mask--splitter--tool-yellow)](https://huggingface.co/collections/brittleru/nser-ibvs-suite)
[![Hugging Face Models](https://img.shields.io/badge/🤗%20Models-nser--ibvs--drone-yellow)](https://huggingface.co/brittleru/nser-ibvs-drone)
[![Collection](https://img.shields.io/badge/🤗%20Collection-NSER--IBVS--Suite-yellow)](https://huggingface.co/collections/brittleru/nser-ibvs-suite)
</div>

## Dataset Description

This dataset is used to train the **Mask Splitter** neural network, a key component of the NSER-IBVS visual servoing
framework for autonomous drone control. The network learns to split a vehicle segmentation mask into **front** 
and **back** regions, enabling the analytical IBVS controller to compute precise velocity commands.

### Associated Resources

| Resource                 | Link                                                                                                                                                                                                  |
|--------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Paper                    | [ICCV 2025 Workshop](https://openaccess.thecvf.com/content/ICCV2025W/EVW/papers/Mocanu_Efficient_Self-Supervised_Neuro-Analytic_Visual_Servoing_for_Real-time_Quadrotor_Control_ICCVW_2025_paper.pdf) |
| arXiv                    | [2507.19878](https://arxiv.org/abs/2507.19878)                                                                                                                                                        |
| Models                   | [nser-ibvs-models](https://huggingface.co/brittleru/nser-ibvs-drone)                                                                                                                                  |
| Annotation and Train     | [mask-splitter](https://github.com/SpaceTime-Vision-Robotics-Laboratory/mask-splitter)                                                                                                              |
| NN Integration for drone | [nser-ibvs-drone](https://github.com/SpaceTime-Vision-Robotics-Laboratory/nser-ibvs-drone)                                                                                                            |
| Project                  | [Website](https://spacetime-vision-robotics-laboratory.github.io/NSER-IBVS/)                                                                                                                          |
| Demo                     | [Hugging Face Space](https://huggingface.co/spaces/brittleru/mask-splitter-tool)                                                                                                                      |

## Sample Data

<div align="center">

![Mask Splitter Inference (YOLO Mask left and Predicted Front & Back right) Demo](mask-splitter-inference-demo.gif)

</div>

<table align="center">
  <tr>
    <td><img src="data/sim/train/images/around-car-30-45-60-75-90-high-quality/frame_000915_5600819.png" width="200"><br>RGB Image</td>
    <td><img src="data/sim/train/segmented/around-car-30-45-60-75-90-high-quality/frame_000915_5600819.png" width="200"><br>Segmentation</td>
    <td><img src="data/sim/train/labels/around-car-30-45-60-75-90-high-quality/front/frame_000915_5600819.png" width="200"><br>Front Mask</td>
    <td><img src="data/sim/train/labels/around-car-30-45-60-75-90-high-quality/back/frame_000915_5600819.png" width="200"><br>Back Mask</td>
  </tr>
</table>


## Intended Use

This dataset is intended for:
- Training and evaluating mask-splitting or part-aware segmentation models
- Visual servoing and robotics perception research
- Simulation-to-real transfer studies

Out of scope:
- Generic object detection benchmarks
- Autonomous driving datasets


## Dataset Structure
```
data/
├── sim/                           # Simulation data (UE4 Bunker environment)
│   ├── train/
│   │   ├── images/                # RGB frames
│   │   │   ├── around-car-30-45-60-75-90-high-quality/
│   │   │   ├── around-car-30-45-60-75-90-low-quality/
│   │   │   ├── around-car-90-75-60-45-30-low-quality/
│   │   │   ├── just-environment-high-quality/
│   │   │   └── just-environment-low-quality/
│   │   ├── labels/                # Manually annotated front/back masks with mask-splitter code.
│   │   │   └── <scene>/
│   │   │       ├── front/
│   │   │       └── back/
│   │   └── segmented/             # Full segmentation masks
│   └── validation/
│       ├── images/
│       ├── labels/
│       └── segmented/
└── real/                          # Real-world data
    ├── train/
    └── validation/
```

## Data Format

| Component       | Format       | Resolution | Description                             |
|-----------------|--------------|------------|-----------------------------------------|
| `images/`       | PNG          | 640x360    | RGB frames from drone camera            |
| `segmented/`    | PNG (binary) | 640x360    | Full vehicle mask                       |
| `labels/front/` | PNG (binary) | 640x360    | Manually annotated front vehicle region |
| `labels/back/`  | PNG (binary) | 640x360    | Manually annotated back vehicle region  |

**Naming Convention:** Files share the same name across `images/`, `segmented/`, and `labels/` directories for 
simple correspondence (e.g., `frame_000000_1076195.png`).

## Scenes

### Simulation (UE4 Bunker Environment)

**Training scenes:**
- `around-car-30-45-60-75-90-high-quality` - Various angles, high render quality
- `around-car-30-45-60-75-90-low-quality` - Various angles, low render quality
- `around-car-90-75-60-45-30-low-quality` - Reverse angle sequence
- `just-environment-high-quality` - Environment-only frames (negatives)
- `just-environment-low-quality` - Environment-only frames (negatives)

**Validation scenes:**
- `around-car-45-high-quality`
- `around-car-45-low-quality`
- `around-car-45-low-quality-car-at-45`

### Real-World

Captured with Parrot Anafi 4K drone tracking a real vehicle.

**Training scenes:**
- `real-30-45-60-75-90` - Various angles
- `just-environment-real` - Environment-only frames (negatives)

**Validation scenes:**
- `real-val`

## Usage

### With git
```bash
git lfs install
git clone https://huggingface.co/datasets/brittleru/nser-ibvs-mask-splitter-dataset
```

### Direct File Access
```python
from huggingface_hub import snapshot_download

# Download entire dataset
snapshot_download(
    repo_id="brittleru/nser-ibvs-mask-splitter-dataset",
    repo_type="dataset",
    local_dir="./mask-splitter-data"
)

# Or download specific subset
snapshot_download(
    repo_id="brittleru/nser-ibvs-mask-splitter-dataset",
    repo_type="dataset",
    local_dir="./mask-splitter-data-sim",
    allow_patterns="data/sim/*"
)
```

### Load with Hugging Face Datasets
```python
from datasets import load_dataset

# Load specific config and split
ds_sim_train = load_dataset("brittleru/nser-ibvs-mask-splitter-dataset", "sim", split="train")
ds_sim_val = load_dataset("brittleru/nser-ibvs-mask-splitter-dataset", "sim", split="validation")
ds_real_train = load_dataset("brittleru/nser-ibvs-mask-splitter-dataset", "real", split="train")

example = ds_sim_val[0]
print(example["scene"])        # Scene name
example["image"].show()        # RGB image (PIL)
example["front_mask"].show()   # Front mask (PIL)
example["back_mask"].show()    # Back mask (PIL)
```

#### PyTorch DataLoader
```python
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from torchvision import transforms

ds = load_dataset("brittleru/nser-ibvs-mask-splitter-dataset", "sim", split="train")

transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
])

def collate_fn(batch):
    images = []
    targets = []
    
    for x in batch:
        # RGB image (3 channels)
        img = transform(x["image"].convert("RGB"))
        # Segmentation mask (1 channel)
        seg_mask = transform(x["segmentation_mask"].convert("L"))
        # Concatenate to get 4-channel input
        input_4ch = torch.cat([img, seg_mask], dim=0)
        images.append(input_4ch)
        
        # Stack front and back masks as target (2 channels)
        front = transform(x["front_mask"].convert("L"))
        back = transform(x["back_mask"].convert("L"))
        target = torch.cat([front, back], dim=0)
        targets.append(target)
    
    return torch.stack(images), torch.stack(targets)

dataloader = DataLoader(ds, batch_size=16, shuffle=True, collate_fn=collate_fn, num_workers=4)
```


### Training the Mask Splitter
```bash
# Clone the main repository
git clone https://github.com/SpaceTime-Vision-Robotics-Laboratory/mask-splitter.git

# Install requirements

# Run train script (check mask-splitter README.md for additional arguments)
python runnable/train_splitter_network.py --data_dir=/path/to/your/data
```

### Inference Example
```python
import cv2
from mask_splitter.nn.infer import MaskSplitterInference

splitter = MaskSplitterInference(
    model_path="path/to/mask_splitter.pt",
    device="cuda"
)

image = cv2.imread("frame.png")
mask = cv2.imread("segmented.png", cv2.IMREAD_GRAYSCALE)

front_mask, back_mask = splitter.infer(image, mask)
splitter.visualize(image, front_mask, back_mask)
```

## Dataset Statistics

| Split     | Domain | Images     | With Vehicle | Environment Only |
|-----------|--------|------------|--------------|------------------|
| Train     | Sim    | 14,693     | 10,114       | 4,579            |
| Train     | Real   | 13,760     | 9,118        | 4,642            |
| Val       | Sim    | 1,123      | 1,123        | -                |
| Val       | Real   | 1,084      | 1,084        | -                |
| **Total** | -      | **30,660** | **21,439**   | **9,221**        |

### Simulation Data

**Train:**

| Scene                                    | Images | Type        |
|------------------------------------------|--------|-------------|
| `around-car-30-45-60-75-90-high-quality` | 2,034  | Vehicle     |
| `around-car-30-45-60-75-90-low-quality`  | 3,212  | Vehicle     |
| `around-car-90-75-60-45-30-low-quality`  | 4,868  | Vehicle     |
| `just-environment-high-quality`          | 2,129  | Environment |
| `just-environment-low-quality`           | 2,450  | Environment |

*Subtotal: 14,693 images*

**Validation:**

| Scene                                 | Images | Type    |
|---------------------------------------|--------|---------|
| `around-car-45-high-quality`          | 343    | Vehicle |
| `around-car-45-low-quality`           | 391    | Vehicle |
| `around-car-45-low-quality-car-at-45` | 389    | Vehicle |

*Subtotal: 1,123 images*


### Real-World Data

**Train:**

| Scene                   | Images | Type        |
|-------------------------|--------|-------------|
| `just-environment-real` | 4,642  | Environment |
| `real-30-45-60-75-90`   | 9,118  | Vehicle     |

*Subtotal: 13,760 images*

**Validation:**

| Scene      | Images | Type    |
|------------|--------|---------|
| `real-val` | 1,084  | Vehicle |

*Subtotal: 1,084 images*


## Limitations and Biases

- Vehicle category is limited primarily to toy cars.
- Camera viewpoint is drone-mounted (top-down / oblique).
- Lighting conditions are limited by the simulator and indoor real-world captures.
- No nighttime data is included.


## Citation

If you use this dataset in your research, please cite:
```bibtex
@InProceedings{Mocanu_2025_ICCV,
    author    = {Mocanu, Sebastian and Nae, Sebastian-Ion and Barbu, Mihai-Eugen and Leordeanu, Marius},
    title     = {Efficient Self-Supervised Neuro-Analytic Visual Servoing for Real-time Quadrotor Control},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    month     = {October},
    year      = {2025},
    pages     = {1744-1753}
}
```