Update README.md

README.md

@@ -2,7 +2,30 @@
 license: apache-2.0
 datasets:
 - garythung/trashnet
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Trash
+- Classification
+- Net
+- biology
+- SigLIP2
 ---
+# **Trash-Net**  
+
+> **Trash-Net** 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 classify images of waste materials into different categories using the **SiglipForImageClassification** architecture.  
+
+The model categorizes images into six classes:  
+- **Class 0:** "cardboard"  
+- **Class 1:** "glass"  
+- **Class 2:** "metal"  
+- **Class 3:** "paper"  
+- **Class 4:** "plastic"  
+- **Class 5:** "trash"
 
 ```py
 Classification Report:
@@ -20,4 +43,68 @@ Classification Report:
 weighted avg     0.9631    0.9626    0.9626      5054
 ```
 
-![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/gl4jGVduxcQQi2FrqzL1D.png)
+![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/gl4jGVduxcQQi2FrqzL1D.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/Trash-Net"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def trash_classification(image):
+    """Predicts the category of waste material in the 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()
+    
+    labels = {
+        "0": "cardboard", 
+        "1": "glass", 
+        "2": "metal", 
+        "3": "paper", 
+        "4": "plastic", 
+        "5": "trash"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=trash_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Trash Classification",
+    description="Upload an image to classify the type of waste material."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```  
+
+# **Intended Use:**  
+
+The **Trash-Net** model is designed to classify waste materials into different categories. Potential use cases include:  
+
+- **Waste Management:** Assisting in automated waste sorting and recycling.  
+- **Environmental Monitoring:** Identifying and categorizing waste in public spaces.  
+- **Educational Purposes:** Teaching waste classification and sustainability.  
+- **Smart Cities:** Enhancing waste disposal systems through AI-driven classification.