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Custom-builds.md

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Custom Builds

There is always a trade off between the face recognition accuracy, max throughput of the system and even hardware support.

By default, CompreFace release contains configuration that could be run on the widest variety of hardware.

The downside of this build is that it's not optimized for the latest generations of CPU and doesn't support GPU.

With custom-builds we aim to cover as many cases as we can. They are not tested as good as the default build, and we encourage community to report any bugs related to these builds.

List of custom-builds

You can find the list of custom-builds here

Contribution

We also encourage community to share their own builds, we will add them to our list with notice that this is community build.

How to choose a build

Different builds are fit for different purposes - some of them have higher accuracy, but the performance on CPU is low, others optimized for low-performance hardware and have acceptable accuracy. You have to make your own choice in this trade off. But generally you can follow this rules:

  • If you want to run real-time face recognition, we recommend choosing builds with GPU support.
  • If you need to run face recognition on old or low performance systems, we recommend to use builds with models originally created for mobile
  • Do not take blindly the most accurate model. The accuracy is not so different between models, but the required hardware resources could differ dramatically

How to run custom-builds

Running custom-build is very similar to running the default build - all you need to do is to open the corresponding folder and run docker-compose up -d.

Things to consider:

  • If you run CompreFace from the custom-build folder, it will create a new docker volume, so you won't see your saved information. To run custom-build with your previously saved information, you need to copy files from custom-build to folder with the original build (and replace the original files)
  • In most cases, face recognition models are not interchangeable, this means that all you saved examples from the old build won't work on new builds. See migrations documentation to know what is the options.
  • Do not run two instances of CompreFace simultaneously without changing the port. To change the port go to docker-compose file and change the post for compreface-fe container.

How to build your own custom-build

Custom models

CompreFace supports two face recognition libraries - FaceNet and InsightFace. It means CompreFace can run any model that can run this libraries. All you need to do is

  1. Upload your model to Google Drive and add it to one the following files into the Calculator class:
  • /embedding-calculator/src/services/facescan/plugins/facenet/facenet.py
  • /embedding-calculator/src/services/facescan/plugins/insightface/insightface.py
  1. Take the docker-compose file from /dev folder as a template
  2. Specify new model name in build arguments. For more information look at [this documentation](https://github. com/exadel-inc/CompreFace/tree/master/embedding-calculator#run-service). E.g. here is a part of docker-compose file for building with custom model with GPU support:
compreface-core:
  image: ${registry}compreface-core:${CORE_VERSION}
  container_name: "compreface-core"
  ports:
    - "3300:3000"
  runtime: nvidia
  build:
    context: ../embedding-calculator
    args:
      - FACE_DETECTION_PLUGIN=insightface.FaceDetector@retinaface_r50_v1
      - CALCULATION_PLUGIN=insightface.Calculator@arcface_r100_v1
      - EXTRA_PLUGINS=insightface.LandmarksDetector,insightface.GenderDetector,insightface.AgeDetector,insightface.facemask.MaskDetector
      - BASE_IMAGE=compreface-core-base:base-cuda100-py37
      - GPU_IDX=0
  environment:
    - ML_PORT=3000