Update README.md

README.md

@@ -11,6 +11,8 @@ tags:
 - mnist
 - siglip2
 ---
+# **Fashion-Mnist-SigLIP2**
+> **Fashion-Mnist-SigLIP2** 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 into **Fashion-MNIST** categories using the **SiglipForImageClassification** architecture.  
 
 ![sfsfsdf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/c0hTpHVZmTQySkYq9aCls.png)
 
@@ -34,4 +36,76 @@ T-shirt / top     0.8142    0.9147    0.8615      6000
  weighted avg     0.9179    0.9180    0.9172     60000
 ```
 
-![Untitled.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4RcQ0vyPssALOOCIhpNqu.png)
+![Untitled.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4RcQ0vyPssALOOCIhpNqu.png)
+
+The model categorizes images into the following 10 classes:  
+- **Class 0:** "T-shirt / top"  
+- **Class 1:** "Trouser"  
+- **Class 2:** "Pullover"  
+- **Class 3:** "Dress"  
+- **Class 4:** "Coat"  
+- **Class 5:** "Sandal"  
+- **Class 6:** "Shirt"  
+- **Class 7:** "Sneaker"  
+- **Class 8:** "Bag"  
+- **Class 9:** "Ankle boot"  
+
+# **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/Fashion-Mnist-SigLIP2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def fashion_mnist_classification(image):
+    """Predicts fashion category for an 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": "T-shirt / top", "1": "Trouser", "2": "Pullover", "3": "Dress", "4": "Coat",
+        "5": "Sandal", "6": "Shirt", "7": "Sneaker", "8": "Bag", "9": "Ankle boot"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=fashion_mnist_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Fashion MNIST Classification",
+    description="Upload an image to classify it into one of the 10 Fashion-MNIST categories."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **Fashion-Mnist-SigLIP2** model is designed for fashion image classification. It helps categorize clothing and footwear items into predefined Fashion-MNIST classes. Potential use cases include:  
+
+- **Fashion Recognition:** Classifying fashion images into common categories like shirts, sneakers, and dresses.  
+- **E-commerce Applications:** Assisting online retailers in organizing and tagging clothing items for better search and recommendations.  
+- **Automated Fashion Sorting:** Helping automated inventory management systems classify fashion items.  
+- **Educational Purposes:** Supporting AI and ML research in vision-based fashion classification models.