misn-1.0

MISN-1.0

MISN (Moderate Images SWIN Network) is an image moderation model for binary NSFW image classification.

The model is fine-tuned from microsoft/swin-base-patch4-window7-224 and predicts whether an image should be classified as clean or nsfw.

Model Details

• Model: MISN-1.0
• Architecture: Swin Transformer Base
• Base model: microsoft/swin-base-patch4-window7-224
• Task: Binary image classification
• Pipeline: image-classification
• Classes: clean, nsfw
• License: OpenMDW 1.1
• Repository: aksern/misn-1.0

Intended Use

MISN-1.0 is intended for automated image moderation and NSFW content classification.

Example use cases include:

• preprocessing images before publication
• content moderation pipelines
• dataset filtering
• automated image classification
• moderation assistance for user-generated content

MISN-1.0 is intended to be used as a moderation aid, rather than as the sole decision-making system for high-impact applications.

Training Data

The original training dataset contains approximately 2,500 images divided between two classes:

• clean
• nsfw

Data augmentation was used to increase the training set to approximately 5,000 samples per class.

The augmented dataset should not be interpreted as 10,000 independent source images. Augmentation creates additional variations of the original images and is primarily intended to improve robustness and reduce overfitting.

Training

MISN-1.0 was fine-tuned from the pretrained Swin Transformer Base model:

microsoft/swin-base-patch4-window7-224

The model uses the Swin Transformer architecture for visual feature extraction followed by a classification head for the two target classes.

Classification Labels

0: clean
1: nsfw

The exact label mapping should be read from the model configuration when loading the model.

Usage

The model can be used directly with the Hugging Face Transformers pipeline.

Python

import torch
from transformers import pipeline

model_repo = "aksern/misn-1.0"

device = 0 if torch.cuda.is_available() else -1

classifier = pipeline(
    "image-classification",
    model=model_repo,
    device=device
)

image_path = "image.jpg"

results = classifier(image_path)

for result in results:
    print(
        f"Class: {result['label']} | "
        f"Probability: {result['score']:.4f}"
    )

The pipeline also supports image URLs:

results = classifier(
    "https://example.com/image.jpg"
)

for result in results:
    print(result)

Example output:

Class: clean | Probability: 0.9999
Class: nsfw   | Probability: 0.0001

Input

MISN-1.0 is based on:

Swin Base
Patch size: 4
Window size: 7
Input resolution: 224x224

Images are automatically processed using the image processor included with the model.

Output

The model returns probabilities for both classes.

Example:

[
    {"label": "clean", "score": 0.9998},
    {"label": "nsfw", "score": 0.0002}
]

The highest-probability class is the model's predicted class.

For production moderation systems, it may be preferable to define a custom probability threshold instead of simply selecting the highest-probability class.

Limitations

MISN-1.0 has several important limitations.

Dataset size

The model was trained from approximately 2,500 original images. Although augmentation increased the effective training set size, the number of unique source images remains limited.

As a result, the model may not generalize equally well to all image distributions.

Domain shift

Performance can vary depending on the type of images being classified. Images that differ substantially from the training data may produce unreliable predictions.

Examples include:

• unusual artistic styles
• heavily edited images
• low-resolution images
• screenshots
• extreme crops
• unusual lighting
• synthetic or generated images

Binary classification

MISN-1.0 only distinguishes between:

clean
nsfw

It does not provide detailed content categories or explain why an image was classified as NSFW.

False positives and false negatives

No binary moderation model is perfectly accurate.

MISN-1.0 may incorrectly classify:

• clean images as NSFW
• NSFW images as clean

The model's confidence score should therefore not be interpreted as a guarantee of correctness.

Bias and Fairness

Image moderation datasets can contain biases caused by their source data, labeling process, class distribution, and augmentation strategy.

Performance may vary across different visual domains and types of imagery.

Users deploying MISN-1.0 should evaluate it on data representative of their own application before using it in production.

Safety Considerations

MISN-1.0 is designed for automated moderation assistance.

Predictions should be treated as model outputs rather than definitive judgments about an image. For applications where incorrect moderation could have significant consequences, predictions should be combined with appropriate review or additional moderation mechanisms.

License

MISN-1.0 is released under the OpenMDW 1.1 license.

See the license information provided with the repository for the applicable terms and conditions.

Acknowledgements

MISN-1.0 is based on the pretrained Swin Transformer model:

microsoft/swin-base-patch4-window7-224

Thanks to the authors and contributors of the Swin Transformer and Hugging Face Transformers ecosystem.

Citation

If you use MISN-1.0 in a project, you can cite the model as:

@misc{misn-1.0,
  title        = {MISN-1.0: Moderate Images SWIN Network},
  author       = {Aksern},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {https://huggingface.co/aksern/misn-1.0}
}