Ibonon/imina_na_v2_lora
Updated • 1
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"role": "assistant",
"content": [
{
"type": "text"... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000075_drain_star.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text"... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000065_mixing_chain.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "tex... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000067_drain_star.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text"... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000073_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000072_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000066_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000068_drain_star.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text"... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000070_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000071_mixing_chain.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "tex... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000069_mixing_chain.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "tex... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000087_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000078_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000079_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000081_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000074_mixing_chain.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "tex... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000083_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000077_drain_star.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text"... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000080_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000094_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000082_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000092_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000084_mixing_chain.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "tex... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000090_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000088_mixing_chain.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "tex... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000085_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000086_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000089_drain_star.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text"... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000096_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000095_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000091_drain_star.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text"... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000093_mixing_chain.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "tex... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000098_normal.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000112_drain_star.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text"... |
[
{
"role": "user",
"content": [
{
"type": "image",
"image": "dataset_v2_real_100k/images/graph_000097_drain_star.png"
},
{
"type": "text",
"text": "Analyze graph."
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text"... |
End of preview. Expand in Data Studio
Sigui DePIN 1M — Multichain Transaction Graph Dataset
The largest open dataset of annotated blockchain transaction graph visualizations for AI security research.
📋 Dataset Description
sigui-depin-1m contains 1,000,000 visual transaction graph images generated from 1.87 million real on-chain transactions across Ethereum, Arbitrum, and Polygon. Each image is annotated with an attack topology label designed to train Vision-Language Models to detect DeFi threats. This dataset is the training foundation for Imina-Na V2, the vision brain of the Sigui Protocol — a DePIN security oracle for the agentic economy.
📊 Dataset Statistics
| Property | Value |
|---|---|
| Total images | 1,000,000 |
| Source transactions | 1,870,000+ real on-chain txs |
| Chains covered | Ethereum, Arbitrum, Polygon |
| Label classes | 3 (DRAIN_STAR, MIXING_CHAIN, NORMAL) |
| Image format | PNG (dark background) |
| Total size | ~10.8 GB (images.tar) |
| Annotation file | qwen2_vl_real_data.jsonl (260 MB) |
| License | MIT |
🏷️ Label Classes
DRAIN_STAR
Multiple wallets converging simultaneously into a single target address. This topology is the signature of coordinated rug pulls, flash loan attacks, and fund aggregation schemes. ### MIXING_CHAIN Funds routed through a sequential chain of intermediate wallets to obscure the origin. This is the on-chain fingerprint of mixer evasion, layering attacks, and money laundering. ### NORMAL Organic transaction topologies with no anomalous structure. Standard user-to-user and contract interactions.
📁 Dataset Structure
sigui-depin-1m/ ├── images.tar # 1,000,000 PNG graph images └── qwen2_vl_real_data.jsonl # Annotations in ShareGPT format
Each line in the JSONL file follows this format:
{
"messages": [
{"role": "user", "content": "<image>Analyze this DePIN transaction graph."},
{"role": "assistant", "content": "DRAIN_STAR detected."}
],
"images": ["/path/to/graph_000001_drain_star.png"]
}
⚙️ Generation Process
Generated using a custom multiprocessing pipeline on AMD MI300X (ROCm 7.0):
Source: 1.87M real transactions from Ethereum, Arbitrum, and Polygon via public APIs
Graph construction: NetworkX DiGraph built from from/to transaction pairs
Attack injection: Deterministic injection of DRAIN_STAR and MIXING_CHAIN patterns
Rendering: Matplotlib (Agg backend), dark background, DPI=50
Speed: ~225 images/second using 20 parallel CPU cores
Total generation time: ~1 hour 15 minutes
🔧 How to Use
python
from datasets import load_dataset
dataset = load_dataset(
"json",
data_files="qwen2_vl_real_data.jsonl",
split="train"
)
print(dataset[0])
🔗 Related Resources
Sigui Protocol (GitHub): https://github.com/ibonon/ERCs
ERC-8259 Standard: https://ethereum-magicians.org/t/erc-8259-ai-agent-identity-threat-registry/28473
Imina-Na V1 Model: https://huggingface.co/Ibonon/imina_na_lora
Imina-Na V2 Model: https://huggingface.co/Ibonon/imina_na_v2_lora
✍️ Citation
bibtex
@dataset{sigui_depin_1m_2026,
author = {Ibonon},
title = {Sigui DePIN 1M — Multichain Transaction Graph Dataset},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/Ibonon/sigui-depin-1m}
}
⚖️ License
MIT — Open for research and commercial use.
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