Update README.md

README.md

@@ -2,7 +2,24 @@
 license: apache-2.0
 datasets:
 - qwertyforce/scenery_watermarks
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Watermark-Detection
+- SigLIP2
 ---
+
+![5.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/VXSOLkmcLM1t6XhTcYXUh.png)
+
+# **Watermark-Detection-SigLIP2**
+
+> **Watermark-Detection-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **binary image classification**. It is trained to detect whether an image **contains a watermark or not**, using the **SiglipForImageClassification** architecture.
+
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -16,3 +33,82 @@ weighted avg     0.9435    0.9427    0.9424     22762
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/_rKqtbSJbglsRiXmRF1ij.png)
+
+---
+
+## **Label Space: 2 Classes**
+
+The model classifies an image as either:
+
+```
+Class 0: "No Watermark"
+Class 1: "Watermark"
+```
+
+---
+
+## **Install dependencies**
+
+```bash
+pip install -q 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 = "prithivMLmods/Watermark-Detection-SigLIP2"  # Update this if using a different path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "No Watermark",
+    "1": "Watermark"
+}
+
+def classify_watermark(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_watermark,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2, label="Watermark Detection"),
+    title="Watermark-Detection-SigLIP2",
+    description="Upload an image to detect whether it contains a watermark."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use**
+
+**Watermark-Detection-SigLIP2** is useful in scenarios such as:
+
+- **Content Moderation** – Automatically detect watermarked content on image sharing platforms.
+- **Dataset Cleaning** – Filter out watermarked images from training datasets.
+- **Copyright Enforcement** – Monitor and flag usage of watermarked media.
+- **Digital Forensics** – Support analysis of tampered or protected media assets.