Update README.md

README.md

@@ -2,15 +2,21 @@
 license: apache-2.0
 datasets:
 - prithivMLmods/BnW-vs-Colored-10K
+language:
+- en
+base_model:
+- google/siglip-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- B&W
+- Colored
+- art
+- SigLIP2
 ---
 
 ![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
@@ -23,78 +29,4 @@ Classification Report:
 weighted avg     0.9989    0.9989    0.9989     10000
 ```
 
-![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.
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/1ylOG64XFJgD1uvlTaehx.png)