Image Classification
Transformers
Safetensors
English
siglip
siglip2
Not-For-All-Audiences
art
Synthetic
nsfw
sfw
Anime Picture
Hentai
Normal
Pornography
Enticing
Sensual
File size: 4,458 Bytes
6008e6d 811ed03 cae2148 3422b05 2377870 3422b05 6008e6d 6ceb38b dea4268 6008e6d 72e2662 4f7d5a0 6008e6d b1deab2 dea4268 6008e6d d77968e 6008e6d ed31c70 6008e6d 7e5a6bf 6008e6d 0b4471d 6008e6d 2c183ee d5123c7 2c183ee dea4268 6008e6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
---
license: apache-2.0
language:
- en
pipeline_tag: image-classification
library_name: transformers
tags:
- siglip2
- not-for-all-audiences
- art
- synthetic
- nsfw
- sfw
- Anime Picture
- Hentai
- Normal
- Pornography
- Enticing
- Sensual
datasets:
- strangerguardhf/NSFW-MultiDomain-Classification
base_model:
- google/siglip2-base-patch16-224
---
# **nsfw-image-detection**
nsfw-image-detection is a vision-language encoder model fine-tuned from siglip2-base-patch16-256 for multi-class image classification. Built on the SiglipForImageClassification architecture, the model is trained to identify and categorize content types in images, especially for explicit, suggestive, or safe media filtering.
Original Model : https://huggingface.co/prithivMLmods/siglip2-x256-explicit-content
---
## **Evals**
```py
Classification Report:
precision recall f1-score support
Anime Picture 0.8940 0.8718 0.8827 5600
Hentai 0.8961 0.8935 0.8948 4180
Normal 0.9100 0.8895 0.8997 5503
Pornography 0.9496 0.9654 0.9574 5600
Enticing or Sensual 0.9132 0.9429 0.9278 5600
accuracy 0.9137 26483
macro avg 0.9126 0.9126 0.9125 26483
weighted avg 0.9135 0.9137 0.9135 26483
```
---
# **Quick Start with Transformers🤗**
## **Install Dependencies**
```bash
!pip install transformers torch pillow gradio
```
---
## **Inference Code**
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "strangerguardhf/nsfw-image-detection" # Replace with your model path if needed
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# ID to Label mapping
id2label = {
"0": "Anime Picture",
"1": "Hentai",
"2": "Normal",
"3": "Pornography",
"4": "Enticing or Sensual"
}
def classify_explicit_content(image):
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()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_explicit_content,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=5, label="Predicted Content Type"),
title="nsfw-image-detection",
description="Classifies images into explicit, suggestive, or safe categories (e.g., Hentai, Pornography, Normal)."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Demo Inference**
---
The model classifies each image into one of the following content categories:
```
Class 0: "Anime Picture"
Class 1: "Hentai"
Class 2: "Normal"
Class 3: "Pornography"
Class 4: "Enticing or Sensual"
```
--- |