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Datasets:
prithivMLmods
/
BnW-vs-Colored-10K

Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
B&W
Colored
Classification
art
BnW
10K
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
xet
Community
1
BnW-vs-Colored-10K
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---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- B&W
- Colored
- Classification
- art
- BnW
- 10K
size_categories:
- 1K<n<10K
---

![ChatGPT Image Apr 24, 2025, 09_44_31 AM.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/fjMyyBBCBhBEJFK41KjmJ.png)

# **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  |
|------------------------------|--------|
| ![](sample_bw.png)           | B & W  |
| ![](sample_colored.png)      | Colored|

## Usage

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

```python
from datasets import load_dataset

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

To visualize an example:

```python
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- Historical document processing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](https://www.apache.org/licenses/LICENSE-2.0).

---

*Curated & Maintained by [@prithivMLmods](https://huggingface.co/prithivMLmods). For inquiries or contributions, please open an issue or submit a pull request.*