Datasets:

prithivMLmods
/

ImageShield-Guardrail-Traces

---
dataset_info:
  features:
  - name: Image
    dtype: image
  - name: Output
    dtype: string
  splits:
  - name: train
    num_bytes: 479766546
    num_examples: 5000
  download_size: 461366760
  dataset_size: 479766546
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: apache-2.0
task_categories:
- image-to-text
language:
- en
tags:
- ImageShield
- Guardrail
- Traces
- Classification
- Image
- Text
pretty_name: ImageShield
size_categories:
- 1K<n<10K
---
> [!IMPORTANT]
> This dataset contains sensitive visual content intended for training Image Guard, Image Filter, and Image Shield models. Due to the sensitive nature of the data, access is gated and requires manual review. Access may be approved for users who provide a valid research, safety, or development purpose.

## **ImageShield-Guardrail-Traces**

A multimodal image safety dataset containing visual inputs and structured guardrail classification traces. The dataset is designed for image understanding, safety classification, and explanation-oriented multimodal model training. `ImageShield-Guardrail-Traces` contains 5,000 image-text samples. Each sample pairs an image with a corresponding text output that describes the visual content, explains the relevant safety classification, and provides structured Boolean labels across six guardrail categories.

The dataset follows a consistent three-part output format:

- **DESCRIPTION:** A concise, objective description of the image.
- **EXPLANATION:** An explanation of the safety classification based on the visible content.
- **GUARDRAIL:** A JSON object containing Boolean labels for categories C1 through C6.

## Dataset Details

| Property | Value |
|---|---|
| Dataset | `ImageShield-Guardrail-Traces` |
| Author | `prithivMLmods` |
| Samples | 5,000 |
| Task | Image-to-Text |
| Modalities | Image, Text |
| Language | English |
| Features | `Image`, `Output` |
| Format | Image-text pairs |
| License | Apache-2.0 |

## Dataset Structure

The dataset contains two columns.

| Column | Type | Description |
|---|---|---|
| `Image` | Image | Input image to be analyzed |
| `Output` | String | Structured description, explanation, and guardrail classification |

Each output follows a consistent structure to support supervised fine-tuning and multimodal safety classification workflows.

## Guardrail Categories

The output uses six Boolean classification labels.

| Label | Category | Description |
|---|---|---|
| `C1` | Sexual Content | Sexual content, nudity, or explicit sexual activity |
| `C2` | Harassment & Bullying | Targeted bullying, degradation, humiliation, or harassment |
| `C3` | Threats, Violence & Harm | Physical violence, threats, or depictions of harm |
| `C4` | False & Deceptive Information | False or misleading information and deceptive content |
| `C5` | Illegal/Regulated Activities | Content involving illegal or regulated activities |
| `C6` | Hateful Content & Extremism | Hate-related content, extremist material, or associated symbols |

A category is marked `true` when the content meets the applicable classification criteria. Otherwise, it is marked `false`.

These labels are not mutually exclusive. Multiple categories may be marked `true` when the content clearly meets more than one category's criteria.

## Sample Trace

**Input:** An image depicting two individuals physically grappling in what appears to be a school hallway.

**Output:**

```text
DESCRIPTION: Two individuals are physically grappling or fighting in what appears to be a school hallway.
EXPLANATION: The image displays a physical confrontation where two individuals are grappling in a public indoor space. This content is classified under Violence & Harm as it features direct physical struggle and physical force.
GUARDRAIL: {"C1": false, "C2": false, "C3": true, "C4": false, "C5": false, "C6": false}
```

The example illustrates how an image is converted into a concise visual description, a category-based explanation, and a machine-readable guardrail classification.

## Intended Use

The dataset may be useful for:

- **Multimodal safety classification:** Training models to identify safety-relevant visual content.
- **Supervised fine-tuning (SFT):** Teaching vision-language models to produce structured classification outputs.
- **Visual content moderation:** Exploring image-based safety detection and content filtering.
- **Explanation generation:** Training models to provide concise, evidence-based explanations for their classifications.
- **Structured output generation:** Improving consistency in generating predefined fields and Boolean JSON labels.
- **Guardrail evaluation:** Evaluating model predictions against annotated category labels.

## Loading the Dataset

Load the dataset directly from Hugging Face using the `datasets` library.

```python
from datasets import load_dataset
dataset = load_dataset(
    "prithivMLmods/ImageShield-Guardrail-Traces"
)
print(dataset)
print(dataset["train"][0])
```

Access an individual image and its corresponding output:

```python
sample = dataset["train"][0]
image = sample["Image"]
output = sample["Output"]
print(output)
```

The `Image` column contains the visual input, while `Output` contains the associated classification trace.

## Training Considerations

For supervised fine-tuning, the `Image` column can be used as the visual input and `Output` as the target text.

Depending on the training objective, the output can be used as a complete target sequence or parsed into separate components:

1. `DESCRIPTION` for visual description.
2. `EXPLANATION` for classification justification.
3. `GUARDRAIL` for structured category prediction.

For classification-only training, the Boolean labels can be extracted from the JSON object. For explanation-oriented training, the complete output can be retained.

The output format should be validated before training to ensure that all six category keys are present, their values are Boolean, and the JSON is valid.

## Limitations

- The dataset contains model-generated classification traces, which may include incorrect interpretations or inconsistent category assignments.
- Visual context may be insufficient to determine intent, the circumstances surrounding an event, or whether an incident constitutes targeted harassment.
- Some categories may overlap, and classification quality depends on the definitions and annotation criteria used.
- The presence of a depicted activity does not necessarily establish that the activity is illegal, harmful in context, or directed at a particular individual.
- The dataset should not be treated as a definitive source of safety judgments without independent validation.

For research and deployment, evaluate the dataset against a manually reviewed validation set and measure category-wise precision, recall, and false-positive rates.

## License

This dataset is published under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

Users should independently verify the licensing and usage rights of the source images before redistribution or commercial use.

## Citation

If you use this dataset in your research or projects, please reference the dataset repository:

```bibtex
@misc{imageshield_guardrail_traces,
  author       = {prithivMLmods},
  title        = {ImageShield-Guardrail-Traces},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/prithivMLmods/ImageShield-Guardrail-Traces}}
}
```

## Acknowledgements

Developed by [`prithivMLmods`](https://huggingface.co/prithivMLmods) as part of the ImageShield collection of multimodal safety and guardrail datasets.

	---
	dataset_info:
	features:
	- name: Image
	dtype: image
	- name: Output
	dtype: string
	splits:
	- name: train
	num_bytes: 479766546
	num_examples: 5000
	download_size: 461366760
	dataset_size: 479766546
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	license: apache-2.0
	task_categories:
	- image-to-text
	language:
	- en
	tags:
	- ImageShield
	- Guardrail
	- Traces
	- Classification
	- Image
	- Text
	pretty_name: ImageShield
	size_categories:
	- 1K<n<10K
	---

	> [!IMPORTANT]
	> This dataset contains sensitive visual content intended for training Image Guard, Image Filter, and Image Shield models. Due to the sensitive nature of the data, access is gated and requires manual review. Access may be approved for users who provide a valid research, safety, or development purpose.

	## ImageShield-Guardrail-Traces

	A multimodal image safety dataset containing visual inputs and structured guardrail classification traces. The dataset is designed for image understanding, safety classification, and explanation-oriented multimodal model training. `ImageShield-Guardrail-Traces` contains 5,000 image-text samples. Each sample pairs an image with a corresponding text output that describes the visual content, explains the relevant safety classification, and provides structured Boolean labels across six guardrail categories.

	The dataset follows a consistent three-part output format:

	- DESCRIPTION: A concise, objective description of the image.
	- EXPLANATION: An explanation of the safety classification based on the visible content.
	- GUARDRAIL: A JSON object containing Boolean labels for categories C1 through C6.

	## Dataset Details

	\| Property \| Value \|
	\|---\|---\|
	\| Dataset \| `ImageShield-Guardrail-Traces` \|
	\| Author \| `prithivMLmods` \|
	\| Samples \| 5,000 \|
	\| Task \| Image-to-Text \|
	\| Modalities \| Image, Text \|
	\| Language \| English \|
	\| Features \| `Image`, `Output` \|
	\| Format \| Image-text pairs \|
	\| License \| Apache-2.0 \|

	## Dataset Structure

	The dataset contains two columns.

	\| Column \| Type \| Description \|
	\|---\|---\|---\|
	\| `Image` \| Image \| Input image to be analyzed \|
	\| `Output` \| String \| Structured description, explanation, and guardrail classification \|

	Each output follows a consistent structure to support supervised fine-tuning and multimodal safety classification workflows.

	## Guardrail Categories

	The output uses six Boolean classification labels.

	\| Label \| Category \| Description \|
	\|---\|---\|---\|
	\| `C1` \| Sexual Content \| Sexual content, nudity, or explicit sexual activity \|
	\| `C2` \| Harassment & Bullying \| Targeted bullying, degradation, humiliation, or harassment \|
	\| `C3` \| Threats, Violence & Harm \| Physical violence, threats, or depictions of harm \|
	\| `C4` \| False & Deceptive Information \| False or misleading information and deceptive content \|
	\| `C5` \| Illegal/Regulated Activities \| Content involving illegal or regulated activities \|
	\| `C6` \| Hateful Content & Extremism \| Hate-related content, extremist material, or associated symbols \|

	A category is marked `true` when the content meets the applicable classification criteria. Otherwise, it is marked `false`.

	These labels are not mutually exclusive. Multiple categories may be marked `true` when the content clearly meets more than one category's criteria.

	## Sample Trace

	Input: An image depicting two individuals physically grappling in what appears to be a school hallway.

	Output:

	```text
	DESCRIPTION: Two individuals are physically grappling or fighting in what appears to be a school hallway.

	EXPLANATION: The image displays a physical confrontation where two individuals are grappling in a public indoor space. This content is classified under Violence & Harm as it features direct physical struggle and physical force.

	GUARDRAIL: {"C1": false, "C2": false, "C3": true, "C4": false, "C5": false, "C6": false}
	```

	The example illustrates how an image is converted into a concise visual description, a category-based explanation, and a machine-readable guardrail classification.

	## Intended Use

	The dataset may be useful for:

	- Multimodal safety classification: Training models to identify safety-relevant visual content.
	- Supervised fine-tuning (SFT): Teaching vision-language models to produce structured classification outputs.
	- Visual content moderation: Exploring image-based safety detection and content filtering.
	- Explanation generation: Training models to provide concise, evidence-based explanations for their classifications.
	- Structured output generation: Improving consistency in generating predefined fields and Boolean JSON labels.
	- Guardrail evaluation: Evaluating model predictions against annotated category labels.

	## Loading the Dataset

	Load the dataset directly from Hugging Face using the `datasets` library.

	```python
	from datasets import load_dataset

	dataset = load_dataset(
	"prithivMLmods/ImageShield-Guardrail-Traces"
	)

	print(dataset)
	print(dataset["train"][0])
	```

	Access an individual image and its corresponding output:

	```python
	sample = dataset["train"][0]

	image = sample["Image"]
	output = sample["Output"]

	print(output)
	```

	The `Image` column contains the visual input, while `Output` contains the associated classification trace.

	## Training Considerations

	For supervised fine-tuning, the `Image` column can be used as the visual input and `Output` as the target text.

	Depending on the training objective, the output can be used as a complete target sequence or parsed into separate components:

	1. `DESCRIPTION` for visual description.
	2. `EXPLANATION` for classification justification.
	3. `GUARDRAIL` for structured category prediction.

	For classification-only training, the Boolean labels can be extracted from the JSON object. For explanation-oriented training, the complete output can be retained.

	The output format should be validated before training to ensure that all six category keys are present, their values are Boolean, and the JSON is valid.

	## Limitations

	- The dataset contains model-generated classification traces, which may include incorrect interpretations or inconsistent category assignments.
	- Visual context may be insufficient to determine intent, the circumstances surrounding an event, or whether an incident constitutes targeted harassment.
	- Some categories may overlap, and classification quality depends on the definitions and annotation criteria used.
	- The presence of a depicted activity does not necessarily establish that the activity is illegal, harmful in context, or directed at a particular individual.
	- The dataset should not be treated as a definitive source of safety judgments without independent validation.

	For research and deployment, evaluate the dataset against a manually reviewed validation set and measure category-wise precision, recall, and false-positive rates.

	## License

	This dataset is published under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

	Users should independently verify the licensing and usage rights of the source images before redistribution or commercial use.

	## Citation

	If you use this dataset in your research or projects, please reference the dataset repository:

	```bibtex
	@misc{imageshield_guardrail_traces,
	author = {prithivMLmods},
	title = {ImageShield-Guardrail-Traces},
	year = {2026},
	howpublished = {\url{https://huggingface.co/datasets/prithivMLmods/ImageShield-Guardrail-Traces}}
	}
	```

	## Acknowledgements

	Developed by [`prithivMLmods`](https://huggingface.co/prithivMLmods) as part of the ImageShield collection of multimodal safety and guardrail datasets.