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---
license: apache-2.0
---
# **NICO++ DG Benchmark Subset (Unofficial)**
## Dataset Summary
This is a **non-official subset** of the [NICO++ dataset](https://arxiv.org/abs/2204.08040), designed for **Domain Generalization (DG)** evaluation.
We select **20 categories** across **6 domains**.
The dataset can be used to benchmark algorithms for **domain generalization, domain adaptation, and robustness testing**.
⚠️ **Note:** This dataset is **not the official release of NICO++**, but a re-organized subset curated for research purposes.
---
## Supported Tasks and Leaderboards
* **Domain Generalization (DG)**
* **Out-of-Distribution (OOD) Robustness**
* **Representation Learning with Multiple Contexts**
---
## Languages
* Images contain natural objects and scenes; no text annotations.
* Labels are in **English**.
---
## Dataset Structure
### Data Fields
Each sample contains:
* `image`: the input image (RGB)
* `label`: the class label (integer)
* `category`: the semantic category (string, one of 20)
* `domain`: the environment/domain (string, one of 6)
### Domains
We follow the DG benchmark setup:
```python
"domains" = ['autumn', 'dim', 'grass', 'outdoor', 'rock', 'water']
```
### Categories
The selected 20 categories are:
```python
"categories" = [
'kangaroo', 'dolphin', 'sailboat', 'pumpkin','gun','sheep','tent','mailbox','cactus','car',
'spider','tortoise','fox','lion','elephant','racket','umbrella','crab','giraffe','chair'
]
```
---
## Data Splits
The dataset is split into domains rather than standard train/val/test.
* Researchers may adopt **leave-one-domain-out** DG evaluation, where training uses 5 domains and testing uses the held-out one.
* Example: Train on {autumn, dim, grass, outdoor, rock}, Test on {water}.