README.md

---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- color-detection
- big
pretty_name: datadive
size_categories:
- 100K<n<1M
---
# datadive

![banner](banner.png)

**Datadive** is a large-scale **color detection dataset** containing **360,000 images**. Each image is 128x128 pixels and generated with a variety of colors, shapes, and realistic background effects. It’s ideal for testing and training models related to **color recognition, object detection, and image classification**.

## Dataset Details

- **Number of images:** 360,000  
- **Image size:** 128 x 128 pixels  
- **Categories / Colors:** red, green, blue, yellow, cyan, orange, black, white, gray, lime, navy, brown  
- **Format:** PNG  
- **Purpose:** Color detection, classification, and computer vision experiments

## Features

- Randomized **backgrounds**: solid, gradient, noise, corruption, and perlin noise.  
- Random **shapes**: circles, rectangles, and triangles.  
- **Lighting, shadow, and noise effects** applied for realism.  
- Optional **texture overlays** for variety (stored in the `textures/` folder).

## Structure

```
datadive/
├── banner.png
├── black/
│   ├── black_1.png
│   ├── black_2.png
│   └── ...
├── blue/
│   ├── blue_1.png
│   └── ...
├── ...
├── red/
├── white/
└── yellow/
```

Each color has its own folder containing 30,000 images.

## Usage

### Using Hugging Face `datasets` (if stored as Parquet)
```python
from datasets import load_dataset
import io
from PIL import Image

dataset = load_dataset("Mafu-Labs/datadive", split="train")

for example in dataset:
    img_bytes = example["image"]
    img = Image.open(io.BytesIO(img_bytes))
    img.show()
```

### Direct file access
```python
from PIL import Image
img = Image.open("datadive/blue/blue_1.png")
img.show()
```

## License

This dataset is released under the **Apache 2.0 License**.