README.md

---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
dataset_info:
- config_name: full
  features:
  - name: image_id
    dtype: int64
  - name: image
    dtype: image
  - name: width
    dtype: int32
  - name: height
    dtype: int32
  - name: objects
    sequence:
    - name: id
      dtype: int64
    - name: area
      dtype: int64
    - name: bbox
      sequence: float32
      length: 4
    - name: category
      dtype:
        class_label:
          names:
            '0': sun
  splits:
  - name: train
    num_bytes: 116033440.923
    num_examples: 4047
  - name: validation
    num_bytes: 10697357.0
    num_examples: 374
  - name: test
    num_bytes: 5486934.0
    num_examples: 184
  download_size: 124477992
  dataset_size: 132217731.923
- config_name: mini
  features:
  - name: image_id
    dtype: int64
  - name: image
    dtype: image
  - name: width
    dtype: int32
  - name: height
    dtype: int32
  - name: objects
    sequence:
    - name: id
      dtype: int64
    - name: area
      dtype: int64
    - name: bbox
      sequence: float32
      length: 4
    - name: category
      dtype:
        class_label:
          names:
            '0': sun
  splits:
  - name: train
    num_bytes: 92832.0
    num_examples: 3
  - name: validation
    num_bytes: 92832.0
    num_examples: 3
  - name: test
    num_bytes: 92832.0
    num_examples: 3
  download_size: 292941
  dataset_size: 278496.0
configs:
- config_name: full
  data_files:
  - split: train
    path: full/train-*
  - split: validation
    path: full/validation-*
  - split: test
    path: full/test-*
- config_name: mini
  data_files:
  - split: train
    path: mini/train-*
  - split: validation
    path: mini/validation-*
  - split: test
    path: mini/test-*
---

<div align="center">
  <img width="640" alt="SamuelM0422/SunDataset" src="https://huggingface.co/datasets/SamuelM0422/SunDataset/resolve/main/thumbnail.jpg">
</div>

### Dataset Labels

```
['sun']
```


### Number of Images

```json
{'valid': 374, 'test': 184, 'train': 4047}
```


### How to Use

- Install [datasets](https://pypi.org/project/datasets/):

```bash
pip install datasets
```

- Load the dataset:

```python
from datasets import load_dataset

ds = load_dataset("SamuelM0422/SunDataset", name="full")
example = ds['train'][0]
```

### Roboflow Dataset Page
[https://universe.roboflow.com/samuelm0422/sundetection-bwqjs/dataset/1](https://universe.roboflow.com/samuelm0422/sundetection-bwqjs/dataset/1?ref=roboflow2huggingface)

### Citation

```
@misc{
                            sundetection-bwqjs_dataset,
                            title = { SunDetection Dataset },
                            type = { Open Source Dataset },
                            author = { SamuelM0422 },
                            howpublished = { \\url{ https://universe.roboflow.com/samuelm0422/sundetection-bwqjs } },
                            url = { https://universe.roboflow.com/samuelm0422/sundetection-bwqjs },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2025 },
                            month = { apr },
                            note = { visited on 2025-04-10 },
                            }
```

### License
CC BY 4.0

### Dataset Summary
This dataset was exported via roboflow.com on April 10, 2025 at 4:19 PM GMT

Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 4605 images.
Sun-3Qf4-ywwQ-sun are annotated in COCO format.

The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)

The following augmentation was applied to create 3 versions of each source image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
* Randomly crop between 0 and 20 percent of the image
* Random rotation of between -15 and +15 degrees
* Random shear of between -10° to +10° horizontally and -10° to +10° vertically
* Random brigthness adjustment of between -15 and +15 percent