The dataset viewer is not available for this subset.
Exception: ConnectionError
Message: Couldn't reach 'NeFr25/TTA_Dataset' on the Hub (LocalEntryNotFoundError)
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 268, in get_dataset_config_info
builder = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1315, in load_dataset_builder
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1133, in dataset_module_factory
raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({e.__class__.__name__})") from e
ConnectionError: Couldn't reach 'NeFr25/TTA_Dataset' on the Hub (LocalEntryNotFoundError)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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