Spline Video Labeling is a Jupyter Notebook application designed to facilitate the preparation and annotation of video data for machine learning tasks. It enables users to efficiently split input videos into frames, generate input manifests, and create video templates compatible with Amazon SageMaker Ground Truth workflows. Key Features and Functionality: - Data Preparation: Automatically splits input videos into individual frames, simplifying the process of preparing video data for analysis. - Manifest Generation: Creates input manifests required for Amazon SageMaker Ground Truth, streamlining the integration of video data into machine learning pipelines. - Video Template Creation: Develops video templates tailored for SageMaker Ground Truth workflows, facilitating efficient labeling and annotation processes. - Ease of Installation: Designed for straightforward deployment, allowing users to set up and begin using the tool with minimal effort. Primary Value and Problem Solved: Spline Video Labeling addresses the challenges associated with preparing and annotating video data for machine learning applications. By automating the extraction of frames and the creation of necessary input files, it significantly reduces the time and effort required for data preparation. This efficiency enables data scientists and machine learning practitioners to focus more on model development and less on the labor-intensive tasks of data preprocessing and annotation.