NSFW Image Detection – A Top Performer

This model is fine-tuned for NSFW image classification. It classifies content into three safety-critical categories, making it useful for moderation, safety filtering, and compliant content handling systems.

🚀 Usage Example

import torch
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch.nn.functional as F

model_path = "Ateeqq/nsfw-image-detection"
processor = AutoImageProcessor.from_pretrained(model_path)
model = SiglipForImageClassification.from_pretrained(model_path)

image_path = r"/content/download.jpg"
image = Image.open(image_path).convert("RGB")
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits
probabilities = F.softmax(logits, dim=1)

predicted_class_id = logits.argmax().item()
predicted_class_label = model.config.id2label[predicted_class_id]
confidence_scores = probabilities[0].tolist()

print(f"Predicted class ID: {predicted_class_id}")
print(f"Predicted class label: {predicted_class_label}\n")
for i, score in enumerate(confidence_scores):
    label = model.config.id2label[i]
    print(f"Confidence for '{label}': {score:.6f}")

Output

Predicted class ID: 2
Predicted class label: safe_normal

Confidence for 'gore_bloodshed_violent': 0.000002
Confidence for 'nudity_pornography': 0.000005
Confidence for 'safe_normal': 0.999993

🧠 Model Details

• Base model: google/siglip2-base-patch16-224
• Task: Image Classification (NSFW/Safe detection)
• Framework: PyTorch / Hugging Face Transformers
• Fine-tuned on: Custom dataset with 3 content categories
• Selected checkpoint: Epoch 5
• Batch size: 64
• Epochs trained: 5

📌 Confusion Matrix

🏷️ Categories

🧾 Label Mapping

label2id = {'gore_bloodshed_violent': 0, 'nudity_pornography': 1, 'safe_normal': 2}
id2label = {0: 'gore_bloodshed_violent', 1: 'nudity_pornography', 2: 'safe_normal'}

📊 Training Metrics (Epoch 5 Selected ✅)

📌 Epoch Training Results

• Training runtime: 1h 21m 40s
• Final Training Loss: 0.0727
• Steps/sec: 0.11 | Samples/sec: 6.99