Update README.md

README.md

@@ -2,8 +2,25 @@
 license: apache-2.0
 datasets:
 - prithivMLmods/High_Res-vs-Low_Res
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- image
+- quality
 ---
 
+# **High_Res-vs-Low_Res**
+
+> **High_Res-vs-Low_Res** 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 assess the resolution quality of images using the **SiglipForImageClassification** architecture.  
+
+The model categorizes images into two classes:
+- **Class 0:** "High Resolution Image" – indicating that the image has a high resolution and appears sharp and detailed.
+- **Class 1:** "Low Resolution Image" – indicating that the image has a low resolution and may appear pixelated or blurry.
+
     Classification Report:
                            precision    recall  f1-score   support
     
@@ -14,4 +31,64 @@ datasets:
                 macro avg     0.7096    0.7023    0.7057      5016
              weighted avg     0.7795    0.7831    0.7812      5016
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-DY0vd8e2QrSuoievpfvH.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-DY0vd8e2QrSuoievpfvH.png)
+
+
+
+
+# **Run with Transformers🤗**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/High_Res-vs-Low_Res"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def resolution_classification(image):
+    """Predicts image resolution classification."""
+    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": "High Resolution Image", "1": "Low Resolution Image"}
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=resolution_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Image Resolution Classification",
+    description="Upload an image to classify its resolution quality."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **High_Res-vs-Low_Res** model is designed to evaluate the resolution quality of images. It helps distinguish between high-resolution and low-resolution images. Potential use cases include:
+
+- **Image Quality Assessment:** Identifying whether an image meets high-resolution standards or suffers from low-quality artifacts.
+- **Content Moderation:** Assisting platforms in filtering low-resolution images for better user experience.
+- **Forensic Analysis:** Supporting researchers and analysts in determining the clarity of images used in various applications.
+- **Image Processing Pipelines:** Helping developers optimize image enhancement algorithms by assessing resolution quality.