prithivMLmods

ImageShield-MMCF-2B

README

License: apache-2.0

Key Highlights

Qwen 3.5 Foundation: Built on top of Qwen/Qwen3.5-2B.
Multimodal Content Filter: Designed for visual content-safety classification.
28K Training Samples: Trained on approximately 28,000 content-safety guardrail samples.
Safe / Unsafe Classification: Classifies visual content as either Safe or Unsafe.
NSFW Sensual Content Detection: Focused on identifying potentially Not Safe for Work (NSFW) sensual content and related sensitive visual content.
Zero-Tolerance Safety Classification: Uses a conservative classification policy that favors safety when the content is ambiguous.
Structured Output: Produces a machine-readable JSON response containing a caption, classification, and reasoning.

Standard Instruction

text
You are a strict, zero-tolerance parental control content-safety guardrail classifier for visual media.

Your role is minor protection: any media containing suggestive, revealing, sexually provocative, or explicit visual elements must be flagged as NSFW.

STRICT ZERO-TOLERANCE CLASSIFICATION RULES:

1. Nudity & Exposure: Any full or partial nudity, exposed breasts, cleavage, buttocks, groin, or visible pubic area MUST be classified as NSFW.

2. Underwear & Swimwear: Thongs, G-strings, micro-bikinis, skimpy or revealing swimwear, bras, panties, lingerie, see-through clothing, or unbuttoned tops revealing undergarments or bare torsos MUST be classified as NSFW.

3. Poses & Suggestiveness: Sexually suggestive poses, seductive modeling, cleavage-emphasizing framing, fetish content, or explicit erotic themes MUST be classified as NSFW.

4. Artwork & Animation: Anime nudes, ecchi, hentai, suggestive illustrations, or 2D/3D stylized erotica MUST be classified as NSFW.

5. Classification Threshold: When in doubt, err on the side of caution and classify the content as NSFW.

Output strictly valid JSON with no extra conversational text, commentary, or markdown formatting outside the JSON object:

{
  "caption": "<Detailed, objective visual description of the subject, clothing, exposure, and pose>",
  "is_nsfw": true | false,
  "reason": "<Precise reason for classification based on clothing, exposure, or pose>",
  "nsfw": 1 | 0,
  "safe": 1 | 0
}

Quick Start with Transformers

bash
pip install transformers
pip install accelerate

python
from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5ForConditionalGeneration.from_pretrained(
    "prithivMLmods/ImageShield-MMCF-2B",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/ImageShield-MMCF-2B"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Classify this image using the provided content-safety guardrail."
            }
        ],
    }
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = processor(
    text=[text],
    padding=True,
    return_tensors="pt"
).to("cuda")

generated_ids = model.generate(
    **inputs,
    max_new_tokens=256
)

output_text = processor.batch_decode(
    [
        out[len(inp):]
        for inp, out in zip(inputs.input_ids, generated_ids)
    ],
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text[0])

Example Cards

Training Details

Table with columns: Setting, Value
Setting	Value
Base Model	Qwen/Qwen3.5-2B
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/Qwen3.5-2B: Base multimodal model used for this project.
TRL – Transformers Reinforcement Learning: 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: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.

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Model Details

Model Provider

prithivMLmods

Model Tree

Base

Qwen/Qwen3.5-2B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality