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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.

Classification Report:
 precision recall f1-score support

 badminton 0.9385 0.9760 0.9569 1125
 cricket 0.9583 0.9739 0.9660 1226
 football 0.9821 0.9144 0.9470 958
 karate 0.9513 0.9611 0.9562 488
 swimming 0.9960 0.9650 0.9802 514
 tennis 0.9425 0.9530 0.9477 1169
 wrestling 0.9761 0.9753 0.9757 1175

 accuracy 0.9606 6655
 macro avg 0.9635 0.9598 0.9614 6655
weighted avg 0.9611 0.9606 0.9606 6655

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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 

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Installation

pip install transformers torch pillow gradio

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Example Inference Code

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()

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Use Cases

• Sports video tagging
• Real-time sport event classification
• Dataset enrichment for sports analytics
• Educational or training datasets for sports AI

Base model

google/siglip2-base-patch16-224

(119)

Moderation, Balance, Classifiers • 7 items • Updated 1 day ago • 1