Datasets:

ROSCOSMOS
/

Sports_Cars

license: mit
pretty_name: Sports Cars
tags:
  - image
  - computer-vision
  - cars
  - sports-cars
  - high-resolution
task_categories:
  - image-classification
language:
  - en
configs:
  - config_name: train
    data_files: train/**/*.arrow
    features:
      - name: image
        dtype: image
      - name: unique_id
        dtype: string
      - name: width
        dtype: int32
      - name: height
        dtype: int32
      - name: original_file_format
        dtype: string
      - name: image_mode_on_disk
        dtype: string

Sports Cars

High resolution image subset from the Aesthetic-Train-V2 dataset, contains a mix of modified street cars, high performance / super cars from various manufacturers.

Dataset Details

Curator: Roscosmos
Version: 1.0.0
Total Images: 600
Average Image Size (on disk): ~5.1 MB compressed
Primary Content: Sports 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

train split: Contains the full, high-resolution image data and associated metadata. This is the recommended split for model training and full data analysis.

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

image: The actual image data. In the train split, this is full-resolution.
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