AventIQ-AI/resnet18-sports-category-classification

🩺 ResNet-18 Sports Category Classification

This repository hosts a fine-tuned ResNet-18-based model optimized for sports category classification having 9 different labels of sports [cricket, archery, football, basketball, tennis, baseball, hockey, golf, boxing]. The model classifies images into these 9 categories.

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📌 Model Details

• Model Architecture: ResNet-18
• Task: Sports Category Classification
• Dataset: 100 Sports Image Classification (Kaggle)
• Framework: PyTorch
• Input Image Size: 224x224
• Number of Classes: 9

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🚀 Usage

Installation

pip install torch torchvision pillow

Loading the Model

import torch
import torchvision.models as models
from huggingface_hub import hf_hub_download
import json
from PIL import Image
import torchvision.transforms as transforms

device = "cuda" if torch.cuda.is_available() else "cpu"

weights_path = hf_hub_download(repo_id="AventIQ-AI/resnet18-sports-category-classification", filename="resnet18_sports_classification.pth")
labels_path = hf_hub_download(repo_id="AventIQ-AI/resnet18-sports-category-classification", filename="class_labels.json")

with open(labels_path, "r") as f:
    class_labels = json.load(f)

model = models.resnet18(pretrained=False)

num_classes = len(class_labels)
model.fc = torch.nn.Linear(in_features=512, out_features=num_classes)

model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))

model.eval()

print("Model loaded successfully!")

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🔍 Perform Classification

def predict_image(image_path, model, class_names):
    model.eval()
    
    # Load and transform the image
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    
    image = Image.open(image_path).convert("RGB")
    image_tensor = transform(image).unsqueeze(0).to(device)  # Add batch dimension
    
    # Predict
    with torch.no_grad():
        output = model(image_tensor)
        _, predicted = torch.max(output, 1)
    
    predicted_class = class_names[predicted.item()]
    return predicted_class

# Example usage:
image_path = "image_path.jpg"  # Change this to your image path
predicted_sport = predict_image(image_path, model, class_labels)
print(f"Predicted Sport Category: {predicted_sport}")

📊 Evaluation Results

After fine-tuning, the model was evaluated on the Chest X-ray Pneumonia Dataset, achieving the following performance:

Metric	Score
Accuracy	92.4%
Precision	88.2%
Recall	82.8%
F1-Score	88.5%

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🔧 Fine-Tuning Details

Dataset

The model was trained on 100 Sports Image Classification with labeled 9 classes of sports.

Training Configuration

• Number of epochs: 10
• Batch size: 32
• Optimizer: Adam
• Learning rate: 1e-4
• Loss Function: Cross-Entropy
• Evaluation Strategy: Validation at each epoch

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⚠️ Limitations

• Misclassification risk: The model may produce false positives or false negatives. Always verify results with a radiologist.
• Dataset bias: Performance may be affected by dataset distribution. It may not generalize well to different populations.
• Black-box nature: Like all deep learning models, it does not explain why a prediction was made.

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