Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/packaged_modules/csv/csv.py", line 188, in _generate_tables
                  csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/streaming.py", line 73, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1199, in xpandas_read_csv
                  return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
                  return _read(filepath_or_buffer, kwds)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 620, in _read
                  parser = TextFileReader(filepath_or_buffer, **kwds)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
                  self._engine = self._make_engine(f, self.engine)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
                  return mapping[engine](f, **self.options)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
                  self._reader = parsers.TextReader(src, **kwds)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pandas/_libs/parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__
                File "pandas/_libs/parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2053, in pandas._libs.parsers.raise_parser_error
                File "<frozen codecs>", line 322, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte

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πŸ“ΈSCIMD-17 (Source Camera Identification β€” Mobile Devices 17) is an extended version of SCIMD-6, which originally contained images from 6 mobile devices. This version expands the dataset to 17 smartphone models and ~17,000 real-world images, enabling more comprehensive research in camera model identification, image forensics, and cross-device generalization. Images were collected under varied indoor and outdoor scenes using multiple mobile phone brands to ensure cross-device diversity. The dataset aims to support studies in source camera attribution, cross-device generalization, and mobile imaging behavior.

🧠 Dataset Summary SCIMD-17 (Smartphone Camera Image Metadata Dataset) is a large-scale dataset containing 17,000 mobile images captured using 17 different smartphone models. This dataset supports research in camera identification, mobile image forensics, image quality analysis, and vision-language model training.

πŸ“Š Data Composition Attribute Description Total images β‰ˆ17,000 Devices 17 smartphone types Image type Real photos captured in natural scenes Format JPEG Metadata Make, filename, White Balance, Focal Length, Flash, Model, Date Time Original, Exposure time, ISO Speed Ratings, Exif Offset, Date Time

Use cases Camera model recognition, forensic analysis, cross-device generalization πŸ†š Compared to SCIMD-6 β€’ Devices: ↑ from 6 β†’ 17 β€’ Images: ↑ from ~6 k β†’ ~17 k β€’ Added new brands and lighting conditions β€’ More balanced per-device sampling

πŸ”¬ Research Applications β€’ Smartphone camera model identification β€’ Scene-independent mobile image analysis β€’ Vision-language pre-training (e.g., image captioning or VQA based on EXIF/device clues) β€’ Benchmarking model robustness across devices

πŸ§ͺ Dataset Collection Methodology The SCIMD-17 (Source Camera Identification β€” Mobile Devices 17) dataset was collected to represent real-world smartphone imaging conditions without any laboratory or controlled setup. Images were captured casually in both indoor and outdoor environments, using natural lighting and varied scenes to simulate authentic usage patterns. Data collection was conducted primarily within the Bapatla Engineering College campus, including classrooms, corridors, and open areas, as well as in residential locations such as houses and surrounding environments. No artificial constraints were imposed on lighting, focus, or scene content, ensuring natural variation typical of everyday photography. All images were captured manually by volunteers using 17 different smartphone models, listed below: πŸ“± Smartphone Models Used πŸ“± Xiaomi_M2101K6P πŸ“± Infinix_Note40_Pro πŸ“± IQZ9X_5G_224 πŸ“± MotoG45_5G πŸ“± MotoG64_5G πŸ“± MotoG85_5G πŸ“± Nothing_A001 πŸ“± Nothing_Phone1 πŸ“± Oneplus_Nord_C3lite πŸ“± Realme_6i πŸ“± Realme8_Pro πŸ“± redmi_9_prime224 πŸ“± Redmi14C_5G πŸ“± SamsungM13_5G πŸ“± Vivo_V50_5G πŸ“± VivoT1_5G πŸ“± VivoY56_5G Captured images were subsequently pre processed to a uniform resolution of 224 Γ— 224 pixels to ensure compatibility with deep learning and vision-language model architectures.

No additional filtering or enhancement was applied beyond resizing, preserving the natural characteristics of each device’s imaging pipeline. This casual, real-world collection strategy ensures that SCIMD-17 reflects genuine variations in smartphone imaging behavior, making it highly suitable for camera model identification, source attribution, and cross-device generalization studies.

πŸ“‚ Dataset File Description SCIMD-17.zip file contains 17 folders and each folder contains images captured by the corresponding mobile phone (folder name). 🧾 merged_common17.csv This file contains the EXIF metadata and device information corresponding to all ~17,000 images in the SCIMD-17 (Source Camera Identification β€” Mobile Devices 17) dataset. Each row represents one image entry, describing its camera parameters and capture details. File name: merged_common17.csv Format: Comma-Separated Values (CSV) Number of entries: β‰ˆ17,000 🧩 Column Descriptions Column Name Description FNumber: Camera aperture value used during image capture. Make Smartphone brand or manufacturer (e.g., vivo, realme, samsung). filename Image file name corresponding to this record. WhiteBalance White balance mode used during capture (0 = Auto, 1 = Manual). FocalLength Camera focal length in millimeters. Flash Flash mode or status during capture. Model Full smartphone model name (e.g., vivo V50, Realme8_Pro). DateTimeOriginal Original date and time when the photo was taken. DateTimeDigitized Date and time when the digital image file was created. ExposureTime Exposure duration in seconds (e.g., 0.001349). ISOSpeedRatings ISO sensitivity value used by the device. ExifOffset Offset pointer to EXIF metadata block in the image file. DateTime General timestamp combining date and time metadata.

🧠 Purpose This CSV file serves as the metadata index for SCIMD-17, enabling: β€’ Source camera identification and device classification tasks, β€’ Forensic analysis of imaging parameters, β€’ Cross-device feature correlation studies, and β€’ Vision-language model training using image metadata as auxiliary information.

πŸ‘₯Dataset Contributors 🧠 Data Curators Faculty Members, Dept of ECE, Bapatla Engineering College, Bapatla-522102. 🧩 Dr. Chandra Mohan Bhuma 🧩 Dr.CH.V.M.S.N. Pavan Kumar (0000-0002-4354-4185) 🧩 Dr.Ch.Nagaraju (0000-0001-9909-3849) 🧩 P.Madhu Kumar (0000-0003-2889-6059) 🧩 V S Sri harsha kasukurthy (0009-0005-5000-8224)

πŸ‘₯ Dataset Collectors UG (B.Tech) Students, Dept of ECE, Bapatla Engineering College, Bapatla-522102. πŸ‘¨β€πŸ’» Daggumalli Vamsi β€” [email protected] πŸ‘¨β€πŸ’» Degala Rupesh Karthikeya β€” [email protected] πŸ‘¨β€πŸ’» Burri Guru Prasad β€” [email protected] πŸ‘¨β€πŸ’» V. Venkata Shiva Kumar β€” [email protected] πŸ‘¨β€πŸ’» Thulava Vamsi β€” [email protected] πŸ‘¨β€πŸ’» Pilli Harsha Vardhan β€” [email protected]

πŸ“œ Citation Bhuma, C. M., CH.V.M.S.N., P. K., Challa, N., Madhu kumar, . patnala ., & V S Sri harsha Kasukurthy. (2025). SCIMD-17 (Source Camera Identification β€” Mobile Devices 17) (Version 1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17317613

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