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--- |
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license: apache-2.0 |
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datasets: |
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- vieanh/sports_img_classification |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Sports |
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- Cricket |
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- art |
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- Basketball |
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--- |
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# **SportsNet-7** |
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> **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. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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badminton 0.9385 0.9760 0.9569 1125 |
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cricket 0.9583 0.9739 0.9660 1226 |
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football 0.9821 0.9144 0.9470 958 |
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karate 0.9513 0.9611 0.9562 488 |
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swimming 0.9960 0.9650 0.9802 514 |
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tennis 0.9425 0.9530 0.9477 1169 |
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wrestling 0.9761 0.9753 0.9757 1175 |
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accuracy 0.9606 6655 |
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macro avg 0.9635 0.9598 0.9614 6655 |
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weighted avg 0.9611 0.9606 0.9606 6655 |
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``` |
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--- |
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## **Label Classes** |
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The model classifies an input image into one of the following 7 sports: |
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``` |
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0: badminton |
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1: cricket |
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2: football |
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3: karate |
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4: swimming |
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5: tennis |
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6: wrestling |
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``` |
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--- |
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## **Installation** |
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```bash |
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pip install transformers torch pillow gradio |
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``` |
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--- |
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## **Example Inference Code** |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/SportsNet-7" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Label mapping |
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id2label = { |
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"0": "badminton", |
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"1": "cricket", |
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"2": "football", |
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"3": "karate", |
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"4": "swimming", |
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"5": "tennis", |
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"6": "wrestling" |
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} |
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def predict_sport(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return prediction |
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# Gradio interface |
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iface = gr.Interface( |
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fn=predict_sport, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=3, label="Predicted Sport"), |
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title="SportsNet-7", |
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description="Upload a sports image to classify it as Badminton, Cricket, Football, Karate, Swimming, Tennis, or Wrestling." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## **Use Cases** |
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* Sports video tagging |
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* Real-time sport event classification |
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* Dataset enrichment for sports analytics |
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* Educational or training datasets for sports AI |
