Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
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ROI-1555: Rebar Detection and Instance Segmentation Dataset
- ROI-1555 for rebar object detection and instance segmentation contains 1555 rebar images and their fine-labeled bounding boxes and pixel-wise masks.
- Diverse rebar specifications, layouts, application scenarios, and environmental conditions.
Usage
- Here is an example to convert the annotations to MSCOCO 2017 format
python
cp -r 1260/img_label tools/data_annotated/train2017
cd tools
python labelme2coco_instance.py
#Annotation instances_train2017.json(MSCOCO 2017 format) will be generated in tools/annotations
Paper
Deep Learning-based Rebar Detection and Instance Segmentation in Images
Tao Sun,
Qipei Fan,
Yi Shao*
Advanced Engineering Informatics
If you use the dataset in your work, please cite our paper:
@article{sun2025deep,
title={Deep learning-based rebar detection and instance segmentation in images},
author={Sun, Tao and Fan, Qipei and Shao, Yi},
journal={Advanced Engineering Informatics},
volume={65},
pages={103224},
year={2025},
publisher={Elsevier}
}
Highlights
Mask2Former trained on our dataset shows good generalization ability in unseen data
Benchmark test shows the performance and limitations of the popular networks
Six data augmentation strategies were introduced and tested to improve the SOTA method, which can guide the selection of suitable data augmentation strategies for rebar perception.
| Methods | Epochs | mAP | mAP50 | mAP75 | mAPstraight | mAPhoop |
|---|---|---|---|---|---|---|
| Without additional data augmentation | 178 | 68.6 | 93.6 | 79.9 | 76.0 | 61.1 |
| With Random Vertical-Flip | 178 | 60.1 | 91.7 | 65.8 | 70.1 | 50.1 |
| With Random Rotation | 178 | 66.9 | 94.4 | 77.6 | 75.1 | 58.8 |
| With Mosaic | 178 | 54.1 | 85.6 | 58.7 | 61.4 | 46.8 |
| With MixUp | 356 | 64.0 | 94.1 | 71.8 | 72.6 | 55.4 |
| With Cutout | 356 | 70.2 | 94.2 | 81.9 | 77.5 | 62.8 |
| With Simple Copy-Paste | 718 | 71.4 | 93.9 | 84.7 | 78.5 | 64.3 |
Contact
If you have any questions on the dataset, please email tao.sun@mail.mcgill.ca.
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