README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_bytes: 840243388.6753247
    num_examples: 207
  - name: test
    num_bytes: 97419523.32467532
    num_examples: 24
  download_size: 919773656
  dataset_size: 937662912.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- mask-generation
tags:
- street
- view
- street-view
- '360'
pretty_name: 360° streets view with mask
---
# 📷 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**

The model `Jour/sam-vit-base-equirectangular-finetuned` is trained using this dataset.

## 🪪 License

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