How do you go from raw video, to training machine learning models
Traditionally, video data in machine learning is treated as separate images, sampled from the video (sometimes at very low frame rates, one or two images per second), and then processed as images. This can seem counter-intuitive, since you’d want as much training data as possible. For example, a minute of video can have 1800 meaningful images with a wealth of data, why would you use only 60 images and discard of 96.6% of your potential training data? Why would you ignore the temporal data in the video?
Read below to learn about a straightforward treatment of video to get training data, using all the frames in the video. The training data is immediately available and can be directly used in your machine learning code, eg through a Jupyter notebook. You can also watch a webinar we recently hosted demonstrating the same approach. Continue reading “Watch: From Raw Video to Deep Learning in 68 seconds”
