{
"caption": "A woman in a white tank top stands outdoors, gesturing with her right hand near her face while speaking. She wears a silver necklace and a bracelet. To her right, a man in a dark shirt is partially visible, looking towards her. The background shows blurred pedestrians and urban buildings.",
"is_nsfw": false,
"reason": "The image depicts a casual outdoor scene featuring two adults in everyday attire. There is no evidence of nudity, revealing undergarments, suggestive poses, or any sexually explicit content. The focus is on the interaction between the subjects.",
"nsfw": 0,
"safe": 1
}
{
"caption": "A blonde woman is shown from behind, wearing red thong-style underwear. She has long hair and a visible tattoo on her lower back. Her hands are positioned near her hips, holding the straps of the thong. The setting appears to be a modern living room with a window and a patterned sofa.",
"is_nsfw": true,
"reason": "The image is classified as NSFW due to revealing thong-style underwear, substantial skin exposure, and a suggestive presentation.",
"nsfw": 1,
"safe": 0
}
{
"caption": "A medium-sized lioness with golden-brown fur walks across a grassy enclosure in a zoo setting. She is walking towards the camera, looking directly at the viewer. The background features large rocks, trees, and a thatched-roof structure, likely a viewing platform or shelter. The environment appears clean and well-maintained.",
"is_nsfw": false,
"reason": "The image depicts a standard outdoor scene featuring a lioness in a zoo environment. There is no evidence of nudity, revealing clothing, suggestive poses, or any sexually explicit content. The focus is entirely on the animal and its natural habitat.",
"nsfw": 0,
"safe": 1
}
---
## **Training Details**
| Setting | Value |
| :------------------------- | :------------------------------------------- |
| **Base Model** | **Qwen/Qwen3.5-0.8B** |
| **Model Type** | **Multimodal Content-Safety Classifier** |
| **Training Samples** | Approximately **28,000** |
| **Training Objective** | Content-safety guardrail classification |
| **Primary Classification** | **Safe / Unsafe** |
| **Safety Focus** | **Not Safe for Work (NSFW) Sensual Content** |
| **Training Framework** | **TRL** |
## **Intended Use**
* **Content Safety Classification**: Classifying visual media as safe or unsafe.
* **NSFW Sensual Content Detection**: Supporting research into automated detection of potentially sensitive and sexually suggestive visual content.
* **Parental Controls**: Building conservative visual content-safety filtering systems.
* **Content Moderation**: Supporting automated safety classification pipelines.
* **Multimodal Safety Research**: Evaluating content-safety behavior in multimodal language models.
* **Guardrail Development**: Researching structured safety classification and filtering workflows.
## **Limitations**
* **Experimental Model**: The model may produce incorrect or inconsistent classifications.
* **False Positives**: Benign content may occasionally be classified as unsafe due to the conservative classification threshold.
* **False Negatives**: Unsafe content may occasionally be missed.
* **Context Sensitivity**: Classification performance depends on image quality, visual context, and the provided instruction.
* **Conservative Policy**: The model intentionally uses a strict classification threshold and may flag content that would not be considered NSFW under less restrictive moderation policies.
* **Automated Classification**: The model should not be treated as a definitive legal or safety determination.
## **Acknowledgements**
* **[Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B)**: Base multimodal model used for this project.
* **TRL – [Transformers Reinforcement Learning](https://huggingface.co/docs/trl/en/index)**: TRL is a full-stack library providing tools to train transformer language models with methods including Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more.
* **[Transformers](https://huggingface.co/docs/transformers/en/index)**: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.
