Preparing Video Data for Deep Learning: Introducing Vid Prepper

Preparing video data for machine learning and deep learning is a critical step for ensuring computational efficiency. Key preprocessing tasks include metadata analysis to filter corrupted files and standardization of resolution, codecs, and frame rates. An open-source Python package called `vid-prepper` is introduced to help automate these processes, building on the FFmpeg library. Proper standardization, such as choosing less compressed codecs like H264, is vital for preventing decoding bottlenecks on GPUs during model training. The content also details how to mathematically determine an optimal minimum frame rate using optical flow analysis to reduce computational load without compromising model performance.