facenet VS insightface

As for me, openface is already outdated - the latest release was in 2016. If you look for a library, the easiest to use is ageitgey/face_recognition. The more accurate libraries are davidsandberg/facenet and deepinsight/insightface.

ML training is practically impossible on micro-controllers. Inferencing on the other hand is quite doable, especially if aided by a [TPU coprocessor](https://coral.ai/products/accelerator/). Supposedly with the TPU you can do some quantization-aware training, but I haven't tried this. I am working on a security system that does facial recognition to recognize me and some friends and considers anyone else as an intruder. How I am doing this is by retraining [Facenet](https://github.com/davidsandberg/facenet) with my facial embeddings. Use something like Haar Cascade in OpenCV to get the bounding box for a face and put it through the model to extract face embeddings. You can then save these embeddings as a sort of databases for the faces you want it to recognize during the inferencing phase. After that you can impose something like a SVM classifier to say who in your face database it is. One thing I will note is that the problem is even easier if you are only concerned with one face - in which case it is technically face identification - not recognition. If that is the case, you only need to do a difference calculation between the embeddings you saved during training and the result output from inferencing. If you do end up using the TPU, you can connect to it over USB from inside a container (I only know how to do this in Docker though) too. Hope this was helpful. I am actually looking to use a k8s cluster eventually too as a sort of smart hub for my security system and other devices so I can handle much more traffic (not sure if this is overkill or not on the pi 4s).

He might just be a solid techie because the scripts are freely available on github. https://github.com/davidsandberg/facenet

I haven't used it for search, but I believe Insightface's embeddings can be used for this purpose. https://insightface.ai/

This is Picsi.ai/InsightFace Discord server: https://discord.gg/Ym3X8U59ZN The COO of the company, enforcing these strikes is Discord user unmoved.mover This is the github of insightface: https://github.com/deepinsight/insightface

Insightface offering improved model but closed source via discord bot.

https://github.com/deepinsight/insightface/issues/2294

https://github.com/deepinsight/insightface/issues/2315

The easiest I've found so far is to use InsightFace. When you call it on an image, it will return both a 2D and 3D mesh showing the orientation of the face.

Let's say I want to make a flask api using insightface on a linode server. How much ram do I really need?

I have tried Insightface ( https://insightface.ai/ ). But it struggles with face edges. i have used GFPGAN too, but that still leaves blurs around the face. Notice below https://www.youtube.com/watch?v=jWIAVjVeG1I

Mainly as the base model. https://insightface.ai/ There was some post processing done to further improve quality. https://github.com/TencentARC/GFPGAN

in this page says that you need the onxruntime to run it https://github.com/deepinsight/insightface/tree/master/python-package

According to this, they're working on a paper for the 256 model right now.

This is a tongue-in-cheek variation of a WIP for Mat Cauthon. RJ has a list of casting choices and a young James Garner was his selection for Mat Cauthon. The illustration itself is from my own WIP, and no, Mat doesn't look like James Garner in my version. I used Insight Faceswap, https://insightface.ai/ along with a pic of a young James Garner. Made me laugh...