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End of preview. Expand in Data Studio
datadive
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)
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
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.
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