Anzhc/Anzhcs_YOLOs

Description

YOLOs in this repo are trained with datasets that i have annotated myself, or with the help of my friends(They will be appropriately mentioned in those cases). YOLOs on open datasets will have their own pages.

Want to request a model?

Im open to commissions, hit me up in Discord - anzhc

Table of Contents

• Face segmentation
  • Universal
  • Real Face, gendered
• Eyes segmentation
• Head+Hair segmentation
• Breasts segmentation

P.S. All model names in tables have download links attached :3

Available Models

Face segmentation:

Universal:

Series of models aiming at detecting and segmenting face accurately. Trained on closed dataset i annotated myself.

Model	Target	mAP 50	mAP 50-95	Classes	Dataset size	Training Resolution
Anzhc Face -seg.pt	Face: illustration, real	LOST DATA	LOST DATA	2(male, female)	LOST DATA	640
Anzhc Face seg 640 v2 y8n.pt	Face: illustration, real	0.872(box),0.872(mask)	0.835(box),0.752(mask)	1(face)	~500	640
Anzhc Face seg 768 v2 y8n.pt	Face: illustration, real	0.86(box),0.86(mask)	0.81(box),0.726(mask)	1(face)	~500	768
Anzhc Face seg 768MS v2 y8n.pt	Face: illustration, real	0.866(box),0.866(mask)	0.816(box),0.72(mask)	1(face)	~500	768
Anzhc Face seg 1024 v2 y8n.pt	Face: illustration, real	0.872(box),0.872(mask)	0.804(box),0.726(mask)	1(face)	~500	1024

Take those stats with a grain of salt, since im pretty sure i re-scrambled dataset partition after training those models ages ago. Benchmark was performed in 640px. Difference in v2 models are only in their target resolution, so their performance spread is marginal.

Real Face, gendered:

Trained only on real photos for the most part, so will perform poorly with illustrations, but is gendered, and can be used for male/female detection stack.

Model	Target	mAP 50	mAP 50-95	Classes	Dataset size	Training Resolution
Anzhcs ManFace v02 1024 y8n.pt	Face: real	0.883(box),0.883(mask)	0.778(box), 0.704(mask)	1(face)	~340	1024
Anzhcs WomanFace v05 1024 y8n.pt	Face: real	0.82(box),0.82(mask)	0.713(box), 0.659(mask)	1(face)	~600	1024

Benchmark was performed in 640px.

Eyes segmentation:

Was trained for the purpose of inpainting eyes with Adetailer extension, and specializes on detecting anime eyes, particularly - sclera area, without adding eyelashes and outer eye area to detection. Current benchmark is likely inaccurate (but it is all i have), due to data being re-scrambled multi times (dataset expansion for future versions).

Model	Target	mAP 50	mAP 50-95	Classes	Dataset size	Training Resolution
Anzhc Eyes -seg-hd.pt	Eyes: illustration	0.925(box),0.868(mask)	0.721(box), 0.511(mask)	1(eye)	~500(?)	1024

Head+Hair segmentation:

An old model (one of my first). Detects head + hair. Can be useful in likeness inpaint pipelines that need to be automated.

Model	Target	mAP 50	mAP 50-95	Classes	Dataset size	Training Resolution
Anzhc HeadHair seg y8n.pt	Head: illustration, real	0.775(box),0.777(mask)	0.576(box), 0.552(mask)	1(head)	~3180	640
Anzhc HeadHair seg y8m.pt	Head: illustration, real	0.867(box),0.862(mask)	0.674(box), 0.626(mask)	1(head)	~3180	640

Breasts segmentation:

Model for segmenting breasts. Was trained on anime images only, therefore has very weak realistic performance, but still is possible.

Model	Target	mAP 50	mAP 50-95	Classes	Dataset size	Training Resolution
Anzhc Breasts Seg v1 1024n.pt	Breasts: illustration	0.742(box),0.73(mask)	0.563(box), 0.535(mask)	1(breasts)	~2000	1024
Anzhc Breasts Seg v1 1024s.pt	Breasts: illustration	0.768(box),0.763(mask)	0.596(box), 0.575(mask)	1(breasts)	~2000	1024
Anzhc Breasts Seg v1 1024m.pt	Breasts: illustration	0.782(box),0.775(mask)	0.644(box), 0.614(mask)	1(breasts)	~2000	1024

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