Anzhc/Anzhcs_YOLOs

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-hd.pt	Face: illustration, real	LOST DATA	LOST DATA	2(male, female)	LOST DATA	1024
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 segmnetation:

PLACEHOLDER

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

Head+Hair segmentation:

PLACEHOLDER

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

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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