Error code:   ConfigNamesError
Exception:    ValueError
Message:      Please provide either num_classes, names or names_file.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1004, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 605, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2031, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1876, in from_dict
                  obj = generate_from_dict(dic)
                        ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1463, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                               ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1482, in generate_from_dict
                  return class_type(**{k: v for k, v in obj.items() if k in field_names})
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "<string>", line 5, in __init__
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1027, in __post_init__
                  raise ValueError("Please provide either num_classes, names or names_file.")
              ValueError: Please provide either num_classes, names or names_file.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

🧪 Prosopo Large Dataset (MS1MV3)

5.1 Million high-quality aligned face images for state-of-the-art model training.

This dataset serves as the core training engine for the Prosopo face recognition system. It contains the MS1MV3 (MS-Celeb-1M Cleaned) dataset, pre-aligned and packed into the high-performance MXNet RecordIO format.

📊 Dataset Statistics

Metric	Value
Identities	93,431
Total Images	5,179,510
Image Size	112 x 112 px
Alignment	RetinaFace (5-point landmark)
Format	MXNet RecordIO (Packed Binary)
Total Size	~28 GB (Unpacked)

📁 Content Structure

The dataset is provided as a ZIP archive containing the following RecordIO files:

train.rec: The Data (27.3 GB) - All images packed
train.idx: The Index (97 MB) - Offsets for random access
train.lst: The Metadata (411 MB) - Path/Label/Index map

It also includes standard validation benchmarks:

lfw.bin, agedb_30.bin, cfp_fp.bin

🚀 Usage

You can download the zip file and extract it to your training environment.

from huggingface_hub import hf_hub_download
import zipfile

# Download
zip_path = hf_hub_download(repo_id="inanxr/prosopo-large-dataset", filename="prosopo-large-dataset.zip", repo_type="dataset")

# Extract
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
    zip_ref.extractall("./ms1m_dataset")

📜 Acknowledgements

Original Data Source: MS-Celeb-1M (Cleaned by InsightFace/DeepGlint)

InanXR/Prosopo re-hosting for reproducibility. Guo, Yandong, et al. "Ms-celeb-1m: A dataset and benchmark for large-scale face recognition." ECCV 2016.

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