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README.md

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 ---
 license: mit
+pretty_name: "Classic Cars"
+tags: ["image", "computer-vision", "cars", "classic-cars", "high-resolution", "europe", "us"]
+task_categories: ["image-classification"]
+language: ["en"]
+configs:
+  - config_name: default
+    data_files: "preview/**/*.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
+  - 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: image_mode_on_disk
+        dtype: string
+      - name: original_file_format
+        dtype: string
 ---
+
+# 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.
+
+### Who are the source data producers?
+* Original Dataset Creators: Refer to the original dataset card.
+* Curator and Refiner: Roscosmos
+
+## 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).
+
+## Usage
+
+To download and load this dataset from the Hugging Face Hub:
+
+```python
+from datasets import load_dataset
+
+# To load the full, high-resolution dataset (recommended for training):
+dataset_full = load_dataset("ROSCOSMOS/Classic_Cars", split="train")
+
+# To load the smaller, viewer-compatible preview dataset for quick browsing:
+dataset_preview = load_dataset("ROSCOSMOS/Classic_Cars", split="preview")
+
+print("Full Dataset (train split):", dataset_full)
+print("Preview Dataset (preview split):", dataset_preview)
+
+# You can then access individual examples, e.g., dataset_full[0]
+# The 'image' column will contain PIL Image objects.
+```
+
+## 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

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