Update README.md

README.md

@@ -13,4 +13,77 @@ tags:
 - 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   |
+|------------------------------|---------|
+| ![](image_sample1.png)       | Indoor  |
+| ![](image_sample2.png)       | 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.
+
+---
+
+*Maintained by [@prithivMLmods](https://huggingface.co/prithivMLmods).*
+```