doen the changes

README.md

@@ -12,23 +12,25 @@ tags:
 - Image-Classification
 - Watermark-Detection
 - SigLIP2
-
 ---
 
+# **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 detects whether an image **contains a watermark or not**, using the `SiglipForImageClassification` architecture.
+
+> ⚠️ Note: Watermark detection works best with high-quality, crisp images. Avoid noisy inputs.
 
-> **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.
+> 📄 Paper: *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*  
+> https://arxiv.org/pdf/2502.14786
 
-> [!note]
-> Watermark detection works best with crisp and high-quality images. Noisy images are not recommended for validation.
+---
 
-> [!note]
-*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786
+## 📊 Classification Report
 
-```py
-Classification Report:
+```text
               precision    recall  f1-score   support
 
 No Watermark     0.9290    0.9722    0.9501     12779
@@ -37,134 +39,3 @@ No Watermark     0.9290    0.9722    0.9501     12779
     accuracy                         0.9427     22762
    macro avg     0.9456    0.9385    0.9414     22762
 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()
-```
-
----
-
-## **Demo Inference**
-
-> [!Warning]
-> Watermark
-
-<table>
-  <tr>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/sm062kFE7QJiLisTTjNwv.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/UFymm_tzVRmov6vn_cElE.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/bPzPAK-Mib8nFhHCkjD2B.png" width="300"/></td>
-  </tr>
-  <tr>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4fP8SBIYofKEeDBU0klQ2.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/wD5M4YgyQGk9-QLFjMcn9.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/yg0q88-0S4k4FUS4-qGNw.png" width="300"/></td>
-  </tr>
-  <tr>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WhRkeYw8-wIgldpaz0E4m.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Uhb1zBxQV_5CWLoyTAMmD.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/7hnLD2b0f7B7edwgx_eOR.png" width="300"/></td>
-  </tr>
-</table>
-
-> [!Warning]
-> No Watermark
-
-<table>
-  <tr>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/edyFBIETs3Dosn1edpGZ8.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/3bRMcr2r0k00mMkthbYDW.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/eeMLQEg4r89f9owe8jSij.png" width="300"/></td>
-  </tr>
-  <tr>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/45jk4dvZk1wT3L7cprqql.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mrkm0JXXgSQVXi0_d7EKH.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/f_5R7Inb8I-32hWJchkgj.png" width="300"/></td>
-  </tr>
-  <tr>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/qIUTSy8SuJEsRkYGd0L5d.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/DnlNo9lM4mBNUjlexKLVa.png" width="300"/></td>
-    <td><img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/bs4oyaapW8mi0lizOqWSf.png" width="300"/></td>
-  </tr>
-</table>
-
----
-
-## **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.
-
-
-