ROSCOSMOS/Classic_Cars

Classic Cars

High resolution image subset from the Aesthetic-Train-V2 dataset, contains both classic and older generation cars mostly from the US and Europe.

Dataset Details

• Curator: Roscosmos
• Version: 1.0.0
• Total Images: 660
• Average Image Size (on disk): ~5.7 MB compressed
• Primary Content: Classic cars.
• Standardization: All images are standardized to RGB mode and saved at 95% quality for consistency.

Dataset Creation & Provenance

1. Original Master Dataset

This dataset is a subset derived from: zhang0jhon/Aesthetic-Train-V2

• Link: https://huggingface.co/datasets/zhang0jhon/Aesthetic-Train-V2
• Providence: Large-scale, high-resolution image dataset, refer to its original dataset card for full details.
• Original License: MIT

2. Iterative Curation Methodology

CLIP retrieval / manual curation.

Dataset Structure & Content

This dataset is organized into two primary splits:

• train split: Contains the full, high-resolution image data and associated metadata. This is the recommended split for model training and full data analysis.
• preview split: Contains a small, random subset of images from the train split. The images in this split are downsampled and re-compressed to be viewer-compatible on the Hugging Face Hub. This split is intended for quick browsing and previewing directly in your web browser.

Each example (row) in both splits contains the following fields:

• image: The actual image data. In the train split, this is full-resolution. In the preview split, this is a viewer-compatible version.
• unique_id: A unique identifier assigned to each image.
• width: The width of the image in pixels (from the full-resolution image).
• height: The height of the image in pixels (from the full-resolution image).

Citation

@inproceedings{zhang2025diffusion4k,
    title={Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models},
    author={Zhang, Jinjin and Huang, Qiuyu and Liu, Junjie and Guo, Xiefan and Huang, Di},
    year={2025},
    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
}
@misc{zhang2025ultrahighresolutionimagesynthesis,
    title={Ultra-High-Resolution Image Synthesis: Data, Method and Evaluation},
    author={Zhang, Jinjin and Huang, Qiuyu and Liu, Junjie and Guo, Xiefan and Huang, Di},
    year={2025},
    note={arXiv:2506.01331},
}

Disclaimer and Bias Considerations

Please consider any inherent biases from the original dataset and those potentially introduced by the automated filtering (e.g., CLIP's biases) and manual curation process.

Contact

N/A