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license: cc-by-nc-4.0
language:
- en
base_model:
- facebook/metaclip-2-worldwide-s16
pipeline_tag: image-classification
library_name: transformers
tags:
- text-generation-inference
- Content-filtering
model-index:
- name: Nsfw_Image_Detection_OSS
results:
- task:
type: image-classification
dataset:
type: evaluation
name: NSFW Image Detection Benchmark
metrics:
- type: accuracy
value: 0.8918
name: Accuracy
- type: f1
value: 0.9071
name: F1 (NSFW)
- type: precision
value: 0.9047
name: Precision (NSFW)
- type: recall
value: 0.9094
name: Recall (NSFW)
- type: f1
value: 0.8705
name: F1 (SFW)
- type: precision
value: 0.8736
name: Precision (SFW)
- type: recall
value: 0.8673
name: Recall (SFW)
- type: f1_macro
value: 0.8888
name: Macro F1
- type: f1_weighted
value: 0.8917
name: Weighted F1
---
# **Nsfw_Image_Detection_OSS**
> **Nsfw_Image_Detection_OSS** is an image classification vision-language encoder model fine-tuned from **[facebook/metaclip-2-worldwide-s16](https://huggingface.co/facebook/metaclip-2-worldwide-s16)** for a **binary NSFW detection task**.
> It is designed to classify whether an image is **Safe For Work (SFW)** or **Not Safe For Work (NSFW)** using the **MetaClip2ForImageClassification** architecture.
> [!note]
> **MetaCLIP 2: A Worldwide Scaling Recipe**
> [https://huggingface.co/papers/2507.22062](https://huggingface.co/papers/2507.22062)
## Evaluation Report (Self-Reported)
```py
Classification report:
precision recall f1-score support
SFW 0.8736 0.8673 0.8705 11103
NSFW 0.9047 0.9094 0.9071 15380
accuracy 0.8918 26483
macro avg 0.8892 0.8884 0.8888 26483
weighted avg 0.8917 0.8918 0.8917 26483
```
# **Label Mapping**
The model categorizes images into two classes:
* **Class 0:** **SFW**
* **Class 1:** **NSFW**
```json
{
"id2label": {
"0": "SFW",
"1": "NSFW"
},
"label2id": {
"SFW": 0,
"NSFW": 1
}
}
```
# **Run with Transformers**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
# Model name from Hugging Face Hub
model_name = "prithivMLmods/Nsfw_Image_Detection_OSS"
# Load processor and model
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
model.eval()
# Define labels
LABELS = {
0: "SFW",
1: "NSFW"
}
def nsfw_detection(image):
"""Predict whether an image is SFW or NSFW."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
predictions = {LABELS[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Build Gradio interface
iface = gr.Interface(
fn=nsfw_detection,
inputs=gr.Image(type="numpy", label="Upload Image"),
outputs=gr.Label(label="NSFW Detection Probabilities"),
title="NSFW Image Detection (MetaCLIP-2)",
description="Upload an image to classify whether it is Safe For Work (SFW) or Not Safe For Work (NSFW)."
)
# Launch app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use**
The **Nsfw_Image_Detection_OSS** model is designed to classify images into **SFW or NSFW categories**.
Potential use cases include:
* **Content Moderation:** Automated filtering of unsafe or adult content.
* **Social Media Platforms:** Preventing the upload of explicit media.
* **Enterprise Safety:** Ensuring workplace-appropriate content in shared environments.
* **Dataset Filtering:** Cleaning large-scale image datasets before training.
* **Parental Control Systems:** Blocking inappropriate visual material. |