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HiRes-50K

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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label HiRes-50K@4ed6b4731b2348f24eac4b551a5a8a0d1573a7b2
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
                  return encode_nested_example(self, example)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
                  example_data = self.str2int(example_data)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
                  output = [self._strval2int(value) for value in values]
                            ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label HiRes-50K@4ed6b4731b2348f24eac4b551a5a8a0d1573a7b2

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HiRes-50K Dataset

HiRes-50K is a cross-domain evaluation-only dataset designed to assess the generalization capability of AI-generated image (AIGI) detection models and their performance on high-resolution, high-fidelity images. This dataset is not intended for model training and should only be used for evaluation purposes.

Dataset Overview

HiRes-50K consists of 50,568 images, covering long-edge resolutions from below 1K to over 10K pixels, with some reaching up to 64 megapixels.

The dataset is collected from the following publicly accessible communities:

All images were collected in compliance with the Terms of Service and Privacy Policies of their respective sources at the time of access.

Dataset Composition

1. AI-Generated Images

  • Quantity: ~25,000 images
  • Content categories:
    • Portraits (close-ups, upper-body, full-body, and group images)
    • Landscapes (mountains, beaches, cities, rural areas, deserts, various weather conditions)
    • Architecture (urban scenes, skyscrapers, villas, neighborhoods)
    • Vehicles and animals

Resolution distribution:

Resolution range (px, long edge) Image count
[0, 900) 845
[900, 1200) 6,665
[1200, 1500) 6,399
[1500, 2000) 5,262
[2000, 2500) 3,674
[2500, 3000) 571
[3000, 5000) 1,196
[5000, ∞) 472

All images were filtered to ensure high JPEG quality (quality factor ≥ 75).

2. Real Images

To ensure a fair comparison, real images were matched with AI-generated images in both resolution and JPEG compression level. Real images were resized to match the pixel count of their synthetic counterparts while preserving aspect ratios. JPEG compression was applied with identical quality settings

Citation

If you use this dataset in your research, please cite the following paper:

@article{zhang2025nopixel, title={No Pixel Left Behind: A Detail-Preserving Architecture for Robust High-Resolution AI-Generated Image Detection}, author={Lianrui Mu, Zou Xingze, Jianhong Bai, and others}, journal={arXiv preprint arXiv:2508.17346}, year={2025}, url={https://arxiv.org/abs/2508.17346} }
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