Surya2706/image-metadata-extract

MetaColorModel

Overview

MetaColorModel is a Hugging Face-compatible model designed to extract metadata and dominant colors from images. It is built using PyTorch and the Hugging Face transformers library, and can be used for image analysis tasks, such as understanding image properties and identifying the most prominent colors.

Model Details

• Model Type: Custom image feature extraction model
• Configuration: Includes parameters to specify the number of dominant colors (k), metadata size, and color size (e.g., RGB).
• Dependencies:
  • transformers
  • Pillow
  • numpy

Example Use Case

The model can be used in:

• Image search and indexing
• Content moderation
• Color scheme analysis for design and marketing
• Metadata extraction for organizing photo libraries

Installation

To use this model, first install the required dependencies:

pip install transformers Pillow numpy

Usage

Here is an example of how to use MetaColorModel:

from transformers import AutoConfig
from meta_color_model import MetaColorModel

# Load the model
config = AutoConfig.from_pretrained("Surya2706/meta_color_model")
model = MetaColorModel.from_pretrained("Surya2706/meta_color_model", config=config)

# Input image path
image_path = "example_image.jpg"

# Extract metadata and dominant colors
result = model.forward(image_path)
print("Metadata:", result["metadata"])
print("Dominant Colors:", result["dominant_colors"])

Inputs

• Image Path: A file path to the image you want to process.

Outputs

• Metadata: Extracted EXIF metadata (if available).
• Dominant Colors: A list of the top k dominant colors in RGB format.

Training

This model can be trained further or fine-tuned for specific tasks.

Dataset

To train or fine-tune the model, you can prepare a dataset of images and their metadata, structured as follows:

data/
├── images/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
├── metadata_colors.csv

The metadata_colors.csv file should contain metadata and dominant color labels for the images.

Training Script

Use the Trainer class from Hugging Face or implement a custom PyTorch training loop to fine-tune the model.

License

This model is released under the Apache 2.0 License.

Citation

If you use this model in your work, please cite:

@misc{MetaColorModel,
  title={MetaColorModel: A Hugging Face-Compatible Image Analysis Model},
  author={Surya},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/surya2706/image-metadata-extract}}
}

Acknowledgments

• Built with the Hugging Face transformers library.
• Uses Pillow for image processing and numpy for numerical operations.

Feedback

For questions or feedback, please contact [[email protected]] or open an issue on the GitHub repository.