Plants

High resolution image subset from the Aesthetic-Train-V2 dataset, contains a broad mix of plants and leaf types with a small distribution of flowers/fruits.

Dataset Details

• Curator: Roscosmos
• Version: 1.0.0
• Total Images: 948
• Average Image Size (on disk): ~4.93 MB compressed
• Primary Content: plants / leaves
• 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 offers the following configurations/subsets:

• Default (Full train data) configuration: Contains the full, high-resolution image data and associated metadata. This is the recommended configuration for model training and full data analysis. The default split for this configuration is train. Each example (row) in the dataset contains the following fields:

• image: The actual image data. In the default (full) configuration, this is full-resolution. In the preview configuration, 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:

from datasets import load_dataset, Dataset, DatasetDict

# Login using e.g. `huggingface-cli login` to access this dataset

# To load the full, high-resolution dataset (recommended for training):
# This will load the 'default' configuration's 'train' split.
ds_main = load_dataset("ROSCOSMOS/Plants", "default")

print("Main Dataset (default config) loaded successfully!")
print(ds_main)
print(f"Type of loaded object: {type(ds_main)}")

if isinstance(ds_main, Dataset):
    print(f"Number of samples: {len(ds_main)}")
    print(f"Features: {ds_main.features}")
elif isinstance(ds_main, DatasetDict):
    print(f"Available splits: {list(ds_main.keys())}")
    for split_name, dataset_obj in ds_main.items():
        print(f"  Split '{split_name}': {len(dataset_obj)} samples")
        print(f"  Features of '{split_name}': {dataset_obj.features}")

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