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The dataset generation failed
Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1898, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
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 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1919, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
5 0.3531269712453481 0.3555878084179971 0.07071269387113269 0.11320754716981128 |
0 0.061546233554504434 0.681422351233672 0.04626879969345723 0.12917271407837455 |
22 0.35487296368661053 0.6821480406386067 0.06896670142987019 0.0899854862119014 |
22 0.9022415940224161 0.7075471698113207 0.060236739223557406 0.10595065312046444 |
22 0.9048605826843098 0.8809869375907112 0.06896670142987034 0.1044992743105952 |
21 0.9052970807946255 0.4818577648766328 0.08206164473933925 0.11030478955007254 |
27 0.48538589867098525 0.5703918722786647 0.5272897172612862 0.8592162554426704 |
16 0.058927244892610614 0.13715529753265604 0.07071269387113274 0.09724238026124818 |
11 0.902241594022416 0.27721335268505076 0.06547471654734509 0.11030478955007254 |
8 0.6289937769648289 0.3490566037735849 0.0742046787536577 0.10595065312046441 |
End of preview.
STRIDE Architecture Threat Modeling Dataset (AWS & Azure)
📌 Overview
This dataset was created to enable automatic STRIDE threat modeling from cloud architecture diagrams (AWS and Azure).
The goal is to detect architectural components in diagrams and support automated threat identification based on data flows and trust boundaries.
Annotations were created using Label Studio in YOLO format.
Total images: 4190
Total classes: 32
🎯 Purpose
- Detect cloud architecture components
- Identify trust boundaries
- Enable automated STRIDE threat analysis
- Support security research in diagram-based threat modeling
🖼 Image Source
Images were collected from publicly available architecture diagrams on the internet, including:
- AWS reference architectures
- Azure reference architectures
- Cloud solution blog posts
- Technical documentation examples
🔄 Data Augmentation Strategy
To improve robustness and simulate real-world variations, the following augmentations were applied:
_BW(Black and White conversion)_sharp(Sharpen filter)_contrast(Contrast adjustment)_gamma_hi(High gamma correction)_gamma_lo(Low gamma correction)_jpeg50(JPEG compression 50%)_blur1(Gaussian blur level 1)_noise6(Noise injection level 6)_degrade80(Quality degradation 80%)
🏷 Classes (32 total)
| ID | Class |
|---|---|
| 0 | actor_user |
| 1 | actor_admin |
| 2 | edge_ddos_protection |
| 3 | edge_cdn |
| 4 | edge_waf |
| 5 | edge_gateway |
| 6 | edge_portal |
| 7 | external_entry_point |
| 8 | integration_orchestrator |
| 9 | integration_messaging |
| 10 | compute_load_balancer |
| 11 | compute_service |
| 12 | compute_worker |
| 13 | data_database |
| 14 | data_cache |
| 15 | data_storage |
| 16 | security_identity_provider |
| 17 | security_key_management |
| 18 | obs_monitoring |
| 19 | obs_audit |
| 20 | external_backend_service |
| 21 | external_saas_service |
| 22 | external_web_service |
| 23 | communication_service |
| 24 | backup_service |
| 25 | boundary_cloud |
| 26 | boundary_region |
| 27 | boundary_resource_group |
| 28 | boundary_vpc_or_vnet |
| 29 | boundary_subnet_public |
| 30 | boundary_subnet_private |
| 31 | boundary_autoscaling_group |
📦 Dataset Format
YOLO format:
Values are normalized between 0 and 1.
🏗 Structure
train/images, val/images, test/images
train/labels, val/labels, test/labels
data.yaml
👤 Author
Guilherme Santos
Vision Architecture Analyzer – 2026
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