The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: UnidentifiedImageError
Message: cannot identify image file <_io.BytesIO object at 0x7fefd6c2da80>
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 2159, in _apply_feature_types_on_example
decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2204, in decode_example
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1508, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 190, in decode_example
image = PIL.Image.open(bytes_)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3498, in open
raise UnidentifiedImageError(msg)
PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7fefd6c2da80>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.
TTA Dataset: Tidal Turbine Assembly Dataset
This folder contains a sample version of the TTA (Tidal Turbine Assembly) dataset , introduced in our paper "Computer Vision as a Data Source for Digital Twins in Manufacturing: a Sim2Real Pipeline". The dataset is designed to support object detection in industrial assembly environments, combining controlled captures, synthetic renderings, and real-world footage desired for test . This version includes a representative subset with annotations for reproducibility and testing purposes. TTA is a mixed-data object detection dataset designed for sim-to-real research in industrial assembly environments. It includes spontaneous real-world footage , controlled real data captured via cobot-mounted camera , and domain-randomized synthetic images generated using Unity, targeting seven classes related to tidal turbine components at various stages of assembly. The dataset supports reproducibility and benchmarking for vision-based digital twins in manufacturing.
Dataset Card Abstract
TTA contains over 120,000 annotated images across three data types:
-Spontaneous Real Data : Captured from live assembly and disassembly operations, including operator presence with face blurring for privacy, dedicated for test and fine-tuning. -Controlled Real Data : 15, 000 Structured scenes recorded under uniform lighting and positioning using a cobot-mounted high-resolution camera. -Synthetic Data : 105,000 of auto-labeled images generated using Unity 2022 with domain randomization techniques.
The dataset targets seven object classes representing key turbine components:
-Tidal-turbine -Body-assembled -Body-not-assembled -Hub-assembled -Hub-not-assembled -Rear-cap-assembled -Rear-cap-not-assembled
Folder Structure Overview
dataset/
The full dataset, including video recordings, will be made publicly available upon publication. To ensure reproducibility, the annotations are provided for evaluation purposes. ├ data_annotation/ # Annotation files and documentation
│ ├── spontaneous_real_data.zip/ # Bounding box labels in YOLO format
│ ├── controlled_real_data.zip/ # Bounding box labels in YOLO format
│ ├── synthetic_data.zip/ # Auto-generated JSON and mask labels
└ README.md # This file
Dataset Description
data_annotation/
Contains annotation files for training and evaluation:
🔹 spontaneous_real_data.zip/
Semi-automatic annotations where available.
Format:YOLO-compatible .txt files.
🔹 controlled_real_data.zip/
Annotated with YOLO-style bounding boxes.
High-quality labels created semi-automatically using CVAT with AI-assisted tools.
🔹 synthetic_data.zip/
Auto-labeled by Unity with accurate bounding boxes and semantic masks.
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