It's rough for now, but the goal of this point is to find a face of any type of generation, including realistic, anime, and anthro users.
It is only personally tested, please provide any feedback you may have.
Right now it is not trained on realistic animals or common mistakes, but I have seen my desired success rate already.
This is a bbox detection YOLOv8 model. This does not improve your image on its own and must be used as a part of the 'adetailer' extension for WebUI, found at https://github.com/Bing-su/adetailer or 'uddetailer' found at https://github.com/wkpark/uddetailer. It is to be used to find any type of face in a image so that you can infill that part of the image. Right now it is better suited for finding faces that the other pre-installed databases seem to miss. (ex. face_yolov8n.pt, face_yolov8s.pt, mediapipe_face_full)
This is my first model and first item I have made I feel confident in that others may find it useful. I found out that these adetailer databases are actually part of something that I was already working on.
I realized to late that I focused to hard on if I could, that I didn't stop to think if I should. Now I am making a new data set and I have to re-tag all my 'facial' data... The next one will have classes. There are some rumblings that adetailer may be able to pick and choose which classes of detection to remake.
Later if I can work this out I'll probably move on to segmentation.
Please feel free to provide your feedback on which faces it seems to miss or what false positive you find, as I want to continue to improve this model to find Only faces :)
V1.2 has a significantly reduced data set. This comes with the benefit of significantly less false positives, and it can find a variety of faces among humans, anime, and animal. However, the data set did not include data of 'bad faces', so if the face is to weird or to small, it likely will not find it as I found in my testing.
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