RFDETR Soccernet

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

pip install rfdetr pandas opencv-python pillow tqdm numpy torch torchvision

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')

from inference import RFDETRSoccerNet

# Initialize model (auto-detects CUDA/CPU)
model = RFDETRSoccerNet()

# Process video and get DataFrame
df = model.process_video('soccer_match.mp4', confidence_threshold=0.5)

# Display first 5 detections
print(df.head())

# Save results
model.save_results(df, 'match_analysis.csv')