---
license: apache-2.0
task_categories:
- object-detection
tags:
- multi-domain
---
# Multi-Domain Perception Dataset

Unlabeled raw image dataset organized across domain environments, perspectives, and source origins. Designed for downstream annotation (e.g., Label Studio) and computer vision pipelines.

## Repository Structure

Data files are organized cleanly by domain, provenance type, and collection date to prevent naming collisions and facilitate subset loading:

```text
data/
├── <domain>/
│   └── <source_type>/
│       └── <date_collected>/
│           └── <raw_data_files>
└── metadata.jsonl
```

### Dataset Schema (`metadata.jsonl`)

Each line in `metadata.jsonl` tracks individual file paths alongside collection provenance:

```json
{
  "file_name": "data/underwater/real_world/2026-08-19/camera_front/frame_0001.webp",
  "metadata": {
    "author": "Collector Name",
    "domain": "Underwater",
    "perspective": "Planar",
    "date_collected": "2026-08-19",
    "notes": "Field trial under ambient light",
    "md5": "e99a18c428cb38d5f260853678922e03",
    "provenance": {
      "type": "real_world",
      "platform": "sensor_rig"
    }
  }
}
```

## Supported Fields

* **Domain:** `Surface`, `Ground`, `Underwater`, `Aerial`, `Other`
* **Perspective:** `Horizon (Looking to fore/aft/sides)`, `Nadir (Looking down)`, `Zenith (Looking up)`, `Not Applicable`
* **Source Type:** `real_world`, `synthetic`, `simulated`

## Usage with Hugging Face `datasets`

```python
from datasets import load_dataset

# Load full dataset with metadata features
ds = load_dataset("username/repo-name")

# Filter for real-world underwater data
underwater_real = ds.filter(
    lambda x: x["metadata"]["domain"] == "Underwater" 
    and x["metadata"]["provenance"]["type"] == "real_world"
)
```