Update README.md

README.md

@@ -2,6 +2,14 @@
 license: cc-by-nc-4.0
 ---
 
+# **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)
 
@@ -18,4 +26,89 @@ Classification report:
 weighted avg     0.8917    0.8918    0.8917     26483
 ```
 
-![download](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/T1Hp_Yuhew7gUJJH-Prmy.png)
+![download](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/T1Hp_Yuhew7gUJJH-Prmy.png)
+
+# **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.