Update README.md

README.md

@@ -2,8 +2,25 @@
 license: apache-2.0
 datasets:
 - vieanh/sports_img_classification
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Sports
+- Cricket
+- art
+- Basketball
 ---
 
+![FGI.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4x5S3wqAgJiNuFtoqZzq9.png)
+
+# **SportsNet-7**
+
+> **SportsNet-7** is a SigLIP2-based image classification model fine-tuned to identify seven popular sports categories. Built upon the powerful `google/siglip2-base-patch16-224` backbone, this model enables fast and accurate sport-type recognition from images or video frames.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -23,3 +40,86 @@ weighted avg     0.9611    0.9606    0.9606      6655
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/rrUjAtRXEZWIOySA_7n1r.png)
 
+---
+
+## **Label Classes**
+
+The model classifies an input image into one of the following 7 sports:
+
+```
+0: badminton  
+1: cricket  
+2: football  
+3: karate  
+4: swimming  
+5: tennis  
+6: wrestling  
+```
+
+---
+
+## **Installation**
+
+```bash
+pip install transformers torch pillow gradio
+```
+
+---
+
+## **Example 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/SportsNet-7"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "badminton",
+    "1": "cricket",
+    "2": "football",
+    "3": "karate",
+    "4": "swimming",
+    "5": "tennis",
+    "6": "wrestling"
+}
+
+def predict_sport(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()
+
+    prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    return prediction
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_sport,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=3, label="Predicted Sport"),
+    title="SportsNet-7",
+    description="Upload a sports image to classify it as Badminton, Cricket, Football, Karate, Swimming, Tennis, or Wrestling."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Use Cases**
+
+* Sports video tagging
+* Real-time sport event classification
+* Dataset enrichment for sports analytics
+* Educational or training datasets for sports AI