Why Scanner?

Standard library of DNN modules

Face Detection


Pose Detection


Object Detection


Simple Python API

from scannertools import face_detection, vis, sample_video
import scannerpy

def main():
    with sample_video() as video: # get an example video
        db = scannerpy.Database() # start up Scanner
        frames = list(range(50))  # select the first 50 frames
        bboxes = face_detection.detect_faces(
            db, videos=[video], frames=[frames])
            db, videos=[video], frames=[frames],
            bboxes=bboxes, paths=['sample_faces.mp4'])
        # output video is saved to 'sample_faces.mp4'

if __name__ == "__main__":
Check out this example and more

Designed for Performance

1. Never convert your videos into images again

Scanner provides native compressed video support, which enables directly accessing compressed videos to keep your storage and bandwidth costs low.

2. First-class support for GPUs

Scanner supports machines with CPUs and GPUs out of the box so that you can make the most out of your hardware.

3. Cloud & cluster ready

Scanner is integrated with Kubernetes, Google Cloud Platform, and AWS to make it easy to spin up machines to get your results back faster.

Scanner in Production

Analyzing decades worth of Hollywood Films

Scanner makes it much easier to analyze large video datasets to extract trends and statistics that rely on the content of the videos, instead of just the metadata. For example, we have been able to use Scanner to analyze over 600 feature-length films to determine how director's taste in cinematography and casting demographics has changed over the years. Check out scannertools for an easy to use toolkit that includes most of these tools to get started on your own analysis.

Facebook's Manifold VR Camera

Scanner is currently being used as the compute engine behind the Manifold 360 video camera from Facebook and RED. Scanner has also been integrated into the open-source version of the previous generation Surround 360 system on GitHub.