<ul><li>The CPU-GPU transfer process in video processing for deep learning introduces a performance bottleneck, especially for high-resolution and high frame rate videos.</li><li>Using FFmpeg with NVIDIA GPU hardware acceleration can eliminate redundant CPU-GPU transfers and keep the entire video processing pipeline on the GPU for improved efficiency.</li><li>Benchmark tests demonstrate a significant reduction in processing time, with speed improvements of up to 18% for longer videos.</li><li>These optimizations are particularly beneficial for handling large video datasets and real-time video analysis tasks.</li></ul>

Breaking the Bottleneck: GPU-Optimised Video Processing for Deep Learning

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