Update README.md

README.md

@@ -7,3 +7,131 @@ base_model:
 # Vit-Mature-Content-Detection (ONNX)
 
 This is an ONNX version of [prithivMLmods/Vit-Mature-Content-Detection](https://huggingface.co/prithivMLmods/Vit-Mature-Content-Detection). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
+
+---
+
+
+![uijytyyt.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/IDbH_a4KQpydEVQ4VtKiA.png)
+ 
+# **Vit-Mature-Content-Detection**
+
+> **Vit-Mature-Content-Detection** is an image classification vision-language model fine-tuned from **vit-base-patch16-224-in21k** for a single-label classification task. It classifies images into various mature or neutral content categories using the **ViTForImageClassification** architecture.
+
+> [!Note]
+> Use this model to support positive, safe, and respectful digital spaces. Misuse is strongly discouraged and may violate platform or regional policies. This model doesn't generate any unsafe content, as it is a classification model and does not fall under the category of models not suitable for all audiences.
+
+> [!Important]
+> Neutral = Safe / Normal
+
+```py
+Classification Report:
+                     precision    recall  f1-score   support
+
+      Anime Picture     0.9311    0.9455    0.9382      5600
+             Hentai     0.9520    0.9244    0.9380      4180
+            Neutral     0.9681    0.9529    0.9604      5503
+        Pornography     0.9896    0.9832    0.9864      5600
+Enticing or Sensual     0.9602    0.9870    0.9734      5600
+
+           accuracy                         0.9605     26483
+          macro avg     0.9602    0.9586    0.9593     26483
+       weighted avg     0.9606    0.9605    0.9604     26483
+```
+
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/FvFTPm_JKwFIffb_LF4ft.png)
+
+```py
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("YOUR-DATASET-HERE")
+
+# Extract unique labels
+labels = dataset["train"].features["label"].names
+
+# Create id2label mapping
+id2label = {str(i): label for i, label in enumerate(labels)}
+
+# Print the mapping
+print(id2label)
+```
+
+---
+
+The model categorizes images into five classes:
+
+- **Class 0:** Anime Picture  
+- **Class 1:** Hentai  
+- **Class 2:** Neutral  
+- **Class 3:** Pornography  
+- **Class 4:** Enticing or Sensual 
+
+# **Run with Transformers 🤗**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Vit-Mature-Content-Detection"  # Replace with your actual model path
+model = ViTForImageClassification.from_pretrained(model_name)
+processor = ViTImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+labels = {
+    "0": "Anime Picture",
+    "1": "Hentai",
+    "2": "Neutral",
+    "3": "Pornography",
+    "4": "Enticing or Sensual"
+}
+
+def mature_content_detection(image):
+    """Predicts the type of content in the 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()
+
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=mature_content_detection,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Vit-Mature-Content-Detection",
+    description="Upload an image to classify whether it contains anime, hentai, neutral, pornographic, or enticing/sensual content."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Recommended Use Cases**
+- Content moderation systems
+- Parental control filters
+- Dataset preprocessing and filtering
+- Digital well-being and user safety tools
+- Search engine safe filter enhancements
+
+# **Discouraged / Prohibited Use**
+- Harassment or shaming
+- Unethical surveillance
+- Illegal or deceptive applications
+- Sole-dependency without human oversight
+- Misuse to mislead moderation decisions