Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 2,184 Bytes
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---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- Indoor
- Outdoor
- Scene
- Classification
- 20K
- Net
size_categories:
- 10K<n<100K
---
# **IndoorOutdoorNet-20K**
**IndoorOutdoorNet-20K** is a labeled image dataset designed for the task of **image classification**, particularly focused on distinguishing between **indoor** and **outdoor** scenes. The dataset is publicly available on [Hugging Face Datasets](https://huggingface.co/datasets/prithivMLmods/IndoorOutdoorNet-20K) and is useful for scene understanding, transfer learning, and model benchmarking.
## Dataset Summary
- **Task**: Image Classification
- **Modalities**: Image
- **Labels**: Indoor, Outdoor (2 classes)
- **Total Images**: 19,998
- **Split**: Train (100%)
- **Languages**: English (metadata)
- **Size**: ~451 MB
- **License**: Apache-2.0
## Features
| Column | Type | Description |
|--------|--------|---------------------------------|
| image | Image | Input image file |
| label | Class | Scene label: `Indoor` or `Outdoor` |
## Example
| Image | Label |
|------------------------------|---------|
|  | Indoor |
|  | Outdoor |
> Note: For full visualization, visit the dataset viewer on Hugging Face.
## Usage
You can use this dataset directly with the `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset("prithivMLmods/IndoorOutdoorNet-20K")
```
To visualize a sample:
```python
import matplotlib.pyplot as plt
sample = dataset['train'][0]
plt.imshow(sample['image'])
plt.title(sample['label'])
plt.axis('off')
plt.show()
```
## Applications
- Scene classification
- Image context recognition
- Smart surveillance
- Autonomous navigation
- Indoor-outdoor transition detection in robotics
## Citation
If you use this dataset in your research or project, please cite it appropriately. (You can include a BibTeX entry here if available.)
## License
This dataset is licensed under the Apache 2.0 License.
---
*Curated & Maintained by [@prithivMLmods](https://huggingface.co/prithivMLmods).* |