A fine-tune of a ViT for binary classification of images of women who are of my type vs. not of my type. Mainly for fun and educational purposes.
Caution
This repo contains inherent bias! Please keep in mind that its contents align with me, but will most likely not with anyone else.
Facial landmark detection model ref:
C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic.
300 faces In-the-wild challenge: Database and results.
Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
attraction-classifier/train.py
Lines 19 to 22 in ebaced4
TODO comment in ebaced4. It's been assigned to @simonSlamka because they committed the code.Then, put in a known positive face, get the embedding, then get the embedding of a new face, and compare the resulting embeddings using similarity metrics, like dot or euclidean. I'm curious if this is gonna yield any usable results
as part of https://github.com/simonSlamka/NUTT
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