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IOC1 Dataset v2
A standardized object-centric computer vision dataset designed for AI Safety, Computer Vision, and Machine Learning research.
Overview
IOC1 Dataset v2 is a curated object-centric dataset containing synchronized image assets and rich metadata for computer vision research.
Each sample includes:
- Original Image
- Segmentation Mask
- Object-only Image
- Background-only Image
- Metadata
The dataset is released in two formats:
- Original Dataset – Human-readable, inspection and debugging
- WebDataset – High-performance streaming for large-scale training
Designed For
- AI Safety Research
- Computer Vision
- Machine Learning
- Object Classification
- Segmentation
- Context Understanding
- Vision Foundation Models
Features
- Object-centric samples
- Binary segmentation masks
- Object/background separation
- Rich metadata
- Train / Validation / Test splits
- WebDataset support
- Streaming-ready
- Reproducible dataset structure
- Dataset validation metadata
Repository Structure
IOC1_Dataset_v2/
│
├── IOC1_Dataset_v2_Original/
│ ├── Data/
│ └── Metadata/
│
├── IOC1_Dataset_v2_WebDataset/
│ ├── Data/
│ ├── Metadata/
│ └── Work/
│
├── README.md
├── LICENSE
└── CITATION.cff
Quick Start
Kaggle
from kaggle.api.kaggle_api_extended import KaggleApi
api = KaggleApi()
api.authenticate()
api.dataset_download_files(
"YOUR_USERNAME/IOC1_Dataset_v2",
path="./dataset",
unzip=True
)
Hugging Face
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="YOUR_ORG/IOC1_Dataset_v2",
repo_type="dataset"
)
Dataset Components
| Component | Description |
|---|---|
| Image | Original RGB image |
| Mask | Binary segmentation mask |
| Object | Foreground-only image |
| Background | Background-only image |
| Metadata | JSON/JSONL sample information |
Intended Usage
Recommended for:
- Model Training
- Benchmarking
- Segmentation
- Representation Learning
- AI Safety Research
Dataset Formats
| Format | Purpose |
|---|---|
| Original | Inspection & Analysis |
| WebDataset | Training & Streaming |
Documentation
The remainder of this README is the complete technical reference manual covering:
- Dataset structure
- Metadata specification
- File formats
- Loading examples
- Validation
- Best practices
- Licensing
- Citation
Citation
If you use this dataset, please cite and credit PNC-TechLabs.
See:
CITATION.cff- License section
License
Released under the PNC-TechLabs Attribution License (PTAL-1.0).
Commercial use, research, redistribution, and derivative works are permitted, provided attribution to PNC-TechLabs is maintained.
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