phpIPAM VS model

Compare phpIPAM vs model and see what are their differences.

model

The model for filtering NSFW images backing the Wingman Jr. plugin: https://github.com/wingman-jr-addon/wingman_jr (by wingman-jr-addon)
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phpIPAM model
40 7
2,131 5
- -
8.3 0.0
7 days ago about 3 years ago
PHP
- Creative Commons Zero v1.0 Universal
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

phpIPAM

Posts with mentions or reviews of phpIPAM. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-23.

model

Posts with mentions or reviews of model. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-18.
  • Show HN: Firefox Addon to Filter NSFW Content
    10 projects | news.ycombinator.com | 18 Nov 2022
    https://github.com/wingman-jr-addon/model#dataset

    Your response is interesting because it tells me you maybe expected it to be in a different spot - was there a specific spot you were looking at? Might help me improve the descriptions.

  • Show HN: An AI program to check videos for NSFW content
    7 projects | news.ycombinator.com | 8 Feb 2022
    Thanks for the response dynamite-ready. There's a lot in here, but I'll try to comment on a couple items. Some of your suggestions I've actually thought about extensively, so perhaps you'll find the reasoning interesting?

    Regarding the current state of tech: I agree the tech still has quite a ways to go. I think one of the most interesting aspects here is how e.g. NSFW.js can get extremely high accuracy - but not necessarily perform better in the real world. I think it speaks in part to the nature of how CNN's work, the nature of the data, and the difficulty of the problem. Still, having seen how incredibly good "AI" has gotten in the last decade, I have quite a bit of hope here.

    Regarding putting it on a server: that is indeed a fair question, but my desire is to keep the scanning on the client side for the user. In fact, it was actually the confluence of Firefox's webRequest response filtering (which is why I didn't make a Chrome version) and Tensorflow.js that allowed me to move from dream to reality as I had been waiting prior to that time. I can't afford server infrastructure if the user base grows, and people don't want to route all their pictures to me. So I guess I see the current way it works as a bonus, not a flaw - but it DOES impact performance, certainly.

    Regarding data collection with respect to server - yes, this is something I've contemplated (there's a GitHub issue if you're curious). There are, however, two things that I've long mulled over: privacy and dark psychological patterns. Let me explain a bit. On the privacy front - it is not likely legal for a user to share the image data directly due to copyright, so they need to share by URL. This can have many issues when considering e.g. authenticated services, but one big one also is that the URL may have relatively sensitive user-identifying information buried in its path. I can try to be careful here but this absolutely precludes sharing this type of URL data as an open dataset. On the psychological dark patterns front - while I'm fine with folks wanting to submit false positives, I think there's a very real chance some will want to go flag all the images they can find that are false negatives (e.g. porn). I don't think that type of submission is particularly good for their mental health or mine. So, in general, I think user image feedback is something that would be quite powerful but needs a lot of care in how it would be approached.

    Regarding the UX - thanks! And you're welcome to try the model as well - I've tried to include enough detail and data to allow others to integrate as they wish: https://github.com/wingman-jr-addon/model/tree/master/sqrxr_... Also, let us know how things go if you try out Darknet.

    Good luck!

  • What kind of evil genius research do you do in your Lab? Or not so evil - I won’t judge.
    5 projects | /r/homelab | 10 Jan 2022
    Y'all were kind enough to help me get up and running with a Supermicro GPU server. I use it to cook up the machine learning model for a Firefox addon that blocks NSFW images client-side, Wingman Jr. Filter. Your help made a big difference in me being able to get the right box at the right price - so thanks!

What are some alternatives?

When comparing phpIPAM and model you can also consider the following projects:

Installation - The premier source of truth powering network automation. Open source under Apache 2. Public demo: https://demo.netbox.dev

WebODM - User-friendly, commercial-grade software for processing aerial imagery. 🛩

Oxidized - Oxidized is a network device configuration backup tool. It's a RANCID replacement!

pytorch_nsfw_model - Pytorch model for NSFW classification with usage example

traefik - The Cloud Native Application Proxy

darknet - Convolutional Neural Networks

NOC Project

wingman_jr - This is the official repository (https://github.com/wingman-jr-addon/wingman_jr) for the Wingman Jr. Firefox addon, which filters NSFW images in the browser fully client-side: https://addons.mozilla.org/en-US/firefox/addon/wingman-jr-filter/ Optional DNS-blocking using Cloudflare's 1.1.1.1 for families! Also, check out the blog!

Vault - A tool for secrets management, encryption as a service, and privileged access management

movie-parser - NWJS wrapper for a wider project.

RackTables - RackTables current development repository

movie-parser-cli