Update README.md

README.md

@@ -17,6 +17,10 @@ tags:
 
 ![ChatGPT Image Apr 24, 2025, 09_44_31 AM.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/l-C5O9g4CNLdVyWnRn-UG.png)
 
+# **BnW-vs-Colored-Detection**
+
+> **BnW-vs-Colored-Detection** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to distinguish between black & white and colored images using the **SiglipForImageClassification** architecture.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -29,4 +33,77 @@ Classification Report:
 weighted avg     0.9989    0.9989    0.9989     10000
 ```
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/1ylOG64XFJgD1uvlTaehx.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/1ylOG64XFJgD1uvlTaehx.png)
+
+---
+
+The model categorizes images into 2 classes:
+
+```
+    Class 0: "B & W"
+    Class 1: "Colored"
+```
+
+---
+
+## **Install dependencies**
+
+```python
+!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/BnW-vs-Colored-Detection"  # Updated model name
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def classify_bw_colored(image):
+    """Predicts if an image is Black & White or Colored."""
+    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()
+    
+    labels = {
+        "0": "B & W", "1": "Colored"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_bw_colored,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="BnW vs Colored Detection",
+    description="Upload an image to detect if it is Black & White or Colored."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use:**
+
+The **BnW-vs-Colored-Detection** model is designed to classify images by color mode. Potential use cases include:
+
+- **Archive Organization:** Separate historical B&W images from modern colored ones.
+- **Data Filtering:** Preprocess image datasets by removing or labeling specific types.
+- **Digital Restoration:** Assist in determining candidates for colorization.
+- **Search & Categorization:** Enable efficient tagging and filtering in image libraries.