Datasets:

Mayank022
/

Cropped_Face_Dataset_128x128

Modalities:

Image

License:

Dataset card Data Studio Files Files and versions

xet

Community

Dataset Preview

Duplicate

Split (1)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ValueError
Message:      Invalid string class label Cropped_Face_Dataset_128x128@d2d9ea18fcc33edc26e3a815ead8d1a737d9c027
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1537, in _prepare_split_single
                  example = self.info.features.encode_example(record) if self.info.features is not None else record
                            ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label Cropped_Face_Dataset_128x128@d2d9ea18fcc33edc26e3a815ead8d1a737d9c027
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1382, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1560, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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.

image image	label class label
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main
	0Output_main

End of preview.

FaceCrop-12k Dataset

FaceCrop-12k is a curated dataset consisting of 12,000 high-quality human face images, each resized to a resolution of 128×128 pixels. The dataset is suitable for training and evaluating models in face generation, facial recognition, identity embedding extraction, and other computer vision tasks involving facial data.


output.zip/
├── 1.jpg
├── 2.jpg
├── ...
└── 12000.jpg

Usage Example

from PIL import Image
import os

folder = "path_to_extracted_zip"
for filename in sorted(os.listdir(folder)):
    if filename.endswith(".jpg"):
        img = Image.open(os.path.join(folder, filename))
        # Further processing here

Applications

Training generative models (e.g., GANs, VAEs, diffusion models)
Facial embedding learning and identity clustering
Super-resolution, denoising, and image restoration tasks
Research on facial similarity and reconstruction

Downloads last month: 13