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---
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
---
# **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
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
```
---
## **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 |