Update README.md

# 📷 360 Clean Dataset

A dataset of **360° equirectangular images** with corresponding **binary masks** that hide the typical artifacts introduced by 360° capture, such as:

* 🚗 Vehicles (cars, bikes, etc.),
* 🧍‍♂️ The person capturing the video (cyclist, pedestrian, etc.),
* 🎥 Camera equipment or shadows appearing at the bottom of the image.

## 🧾 Description

Each sample in the dataset contains:

* `image`: the original 360° equirectangular image (2:1 aspect ratio, typically 3040×1520),
* `mask`: a binary mask of the same resolution, where white pixels (`255`) indicate areas to ignore (e.g. person, vehicle), and black pixels (`0`) represent the usable background.

The masks were **manually created**.

This dataset is particularly useful for:

* 🗺️ 3D reconstruction tasks (e.g. NeRF, Gaussian Splatting),
* 🤖 Training vision models without human-related artifacts,
* 📍 Visual geolocation from clean, unobstructed environments.

## 📁 Data Format

```python
{
"image": Image, # equirectangular 360° scene
"mask": Image # binary mask: 1 = ignore, 0 = keep
}
```

Files are matched by filename: `xxx.jpg` and `xxx_mask.png`.

## 🏷️ Possible Use Cases

* **Object removal / Inpainting**
* **Semantic Segmentation**
* **Dynamic object filtering**
* **Preprocessing for 3D or geospatial vision tasks**

## 🪪 License

This dataset is released under the **MIT**.

README.md

@@ -7,13 +7,22 @@ dataset_info:
     dtype: image
   splits:
   - name: train
-    num_bytes: 937662970.0
+    num_bytes: 937662970
     num_examples: 231
   download_size: 919821617
-  dataset_size: 937662970.0
+  dataset_size: 937662970
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-*
----
+license: ms-pl
+task_categories:
+- mask-generation
+tags:
+- street
+- view
+- street-view
+- '360'
+pretty_name: 360 streets view with mask
+---