Automatic video understanding is becoming more important for
applications where real-time performance is crucial and compute is
limited. Yet, accurate solutions so far have been computationally
intensive. We propose efficient models for videos - Tiny Video Networks
- which are video architectures, automatically designed to comply with
fast runtimes and, at the same time are effective at video recognition
tasks. The Tiny Video Networks run at faster-than-real-time speeds and
demonstrate strong performance across several video benchmarks. These
models not only provide new tools for real-time video applications, but
also enable fast research and development in video understanding. Code
and models are available.