BnW-vs-Colored-10K

BnW-vs-Colored-10K is a curated dataset of 10,000 images designed for binary image classification tasks distinguishing between black & white (BnW) and colored images. This dataset can be used for training models in visual recognition, restoration, or filtering pipelines involving grayscale and color detection.

Dataset Summary

• Task: Binary Image Classification
• Modalities: Image
• Formats: Parquet
• Split: Train (10,000 images)
• Labels: B & W (Black and White), Colored
• Language: English
• License: Apache 2.0
• Size: 4.15 GB

Features

Column	Type	Description
image	Image	Input image (JPEG format)
label	Class	Binary label: B & W or Colored

Label Information

Label ID	Class Name
0	B & W
1	Colored

Example Entries

Image	Label
	B & W
	Colored

Usage

You can load the dataset using the datasets library from Hugging Face:

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/BnW-vs-Colored-10K")

To visualize an example:

import matplotlib.pyplot as plt

example = dataset["train"][0]
plt.imshow(example["image"])
plt.title(example["label"])
plt.axis("off")
plt.show()

Applications

• Colorization model pre/post-processing
• Restoration of old photos
• Image enhancement and filtering
• Historical document processing
• AI art stylization models

License

This dataset is made available under the Apache 2.0 License.

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Curated & Maintained by @prithivMLmods. For inquiries or contributions, please open an issue or submit a pull request.