Skip to content

Latest commit

 

History

History
94 lines (76 loc) · 3.88 KB

Custom-builds.md

File metadata and controls

94 lines (76 loc) · 3.88 KB
Raw

Custom Builds

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

By default, the 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 well as the default build, and we encourage the 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 the community to share their builds; we will add them to our list with notice that this is a 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 are optimized for low-performance hardware and have acceptable accuracy. Of course, you have to make your own choice in this trade-off. But generally, you can follow these 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 using builds with models initially created for mobile
  • Do not take the most accurate model blindly. The accuracy does not vary significantly 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 open the corresponding folder and run docker-compose up -d.

Things to consider:

  • If you run CompreFace from the custom-build folder, it creates a new docker volume so that 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 the docker-compose file and change the post for the 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 these libraries. So all you need to do is

  1. Upload your model to Google Drive and add it to one of 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
  2. Take the docker-compose file from /dev folder as a template
  3. Specify new model names in build arguments. For more information, look at this documentation. E.g. here is a part of the docker-compose file for building with a 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