Face recognition on nextcloud with TPU
a project by jordimassaguerpla
Updated 11 months ago. 5 hacker ♥️. 4 followers.

Project Description

I have all my photos on a private NAS running nextcloud.

This NAS has an ARM CPU and 1GB of RAM, which means I cannot run the face recognition plugin because it requires a GPU, 2 GB of RAM, and PDLib is not available for this arch (I know I could build it and package it ... but doesn't sound fun ;) )

However, I have a Coral TPU connected to a USB port (Thanks to my super friend Marc!):

https://coral.ai/products/accelerator

Where I could run Tensorflow Lite... you see where this is going, don't you?

Goal for this Hackweek

The goal is to run face recognition on the Coral TPU using tensorflow lite and then using the nextcloud API to tag the images.

Resources

Join this project

Looking for hackers with the skills:

ml ai nextcloud

This project is part of:

Hack Week 20

Activity

  • 11 months ago: xcxienpai started this project.
  • over 4 years ago: stefannica liked this project.
  • over 4 years ago: vliaskovitis liked this project.
  • over 4 years ago: jordimassaguerpla left this project.
  • over 4 years ago: XGWang0 liked this project.
  • over 4 years ago: ories liked this project.
  • over 4 years ago: jordimassaguerpla started this project.
  • over 4 years ago: mbrugger liked this project.
  • over 4 years ago: jordimassaguerpla added keyword "ml" to this project.
  • over 4 years ago: jordimassaguerpla added keyword "ai" to this project.
  • over 4 years ago: jordimassaguerpla added keyword "nextcloud" to this project.
  • over 4 years ago: jordimassaguerpla originated this project.


    • Comments

    Be the first to comment!

    Similar Projects

    Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

    Description

    Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

    This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

    The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

    Goals

    By the end of Hack Week, we aim to have a single, working Python script that:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources