README.md

---
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



```bibtex
@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