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?

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Table of Contents

Face segmentation

Universal

Real Face, gendered

Eyes segmentation

Head+Hair segmentation

Breasts

Breasts Segmentation

Breast size detection/classification

Breasts segmentation

Drone detection

Support

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	0.791(box) 0.765(mask)	0.608(box) 0.445(mask)	1(face)	~500	640
Anzhc Face seg 768 v2 y8n.pt	Face: illustration, real	0.765(box) 0.748(mask)	0.572(box) 0.431(mask)	1(face)	~500	768
Anzhc Face seg 768MS v2 y8n.pt	Face: illustration, real	0.807(box) 0.770(mask)	0.601(box) 0.432(mask)	1(face)	~500	768
Anzhc Face seg 1024 v2 y8n.pt	Face: illustration, real	0.768(box) 0.740(mask)	0.557(box) 0.394(mask)	1(face)	~500	1024
Anzhc Face seg 640 v3 y11n.pt	Face: illustration	0.882(box) 0.871(mask)	0.689(box) 0.570(mask)	1(face)	~660	640

UPDATE: v3 model has a bit different face target compared to v2, so stats of v2 models suffer compared to v3 in newer benchmark, especially in mask, while box is +- same. Dataset for v3 and above is going to be targeting inclusion of eyebrows and full eyelashes, for better adetailer experience without large dillution parameter.

Also starting from v3, im moving to yolo11 models, as they seem to be direct upgrade over v8. v12 did not show significant improvement while requiring 50% more time to train, even with installed Flash Attention, so it's unlikely i will switch to it anytime soon.

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:

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

Breast size detection and classification:

Model for Detecting and classifying breast size. Can be used for tagging and moderating content. Utilizes custom scale, combining default Booru sizes with quite freeform upper range of scale from rule34, simplifying and standartizing it.

Size range is established relative to body proportion, instead of relative to scene, to not be confused in cases of gigantism and be disentangled from scene.
And of course it's subjective, since i was the only one annotating data.

Model	Target	Classes	Dataset size	Training Resolution
Anzhcs Breast Size det cls v8 y11m.pt	Breasts: illustration and real	15(size range)	~16100	640

mAPs are not displayed in table, because i think we need more complex stats for this model.

Accurate ratio - correct predictions, exactly matching val.
+1, -1, +-1 ratio - expanded range of acceptable predictions, by +,- and +-1 class. I suggest using this stat as main accuracy, because +-1 range is likely an acceptable margin of error.
At annotation, usual rate of error of original data according to this size scale was in range of +-2 to +-3 in some cases, so +-1 should be quite good.
Miscalss ratio - Correct detection, but classification goes beyond +-1 error.
Miss ratio - Not seen by model, completely missed.
False-Positive ratio - Detection of something that isn't there.
In case of this model i suspect that FPR is also including confusion rate. In some cases multiple detection will be made for single instance, and only 1 will be accepted.
That can be counted as false-positive, while it will be covered in +-1 acc. Actual FPR should be lower than reported, as tested manually.
GT Instances - amount of instances of data per class in dataset.

With that established,
v8 provides pretty decent quality detection and classification, except for extremes of class 11+, and class 0(flat chest), well, since it's not too simple to detect what's not there.
Class 2(medium) is one of the most confusing in this case, and has lowest accuracy. From charts, it's mostly mistaken with class 1.
Rest of classes with reasonable amount of data perform quite well, and achieve high 70s to mid 80s for normal sizes, and up to high 90s for bigger size range.
Misclassification is quite rare, and im happy with model performance in that regard. Average rate of misclassification is just ~3%.
Missing predictions is unfortunately over 10%, but data is highly skewed with classes 0-2, which are hard to detect.
FPR for v8 is very reasonable, assuming confused detections(of 2 classes at once) are counted as FPR. Size range is smooth, and lots of cases where both classes could be applied.

Last class(unmeasurable) is used for classifying outliers that are hard to measure in currently visible area(e.g. mostly out of frame), but model will try to reasonably predict obstructed and partially visible instances.

All ratios are calculated relative to their respective GT instance count.

I will continue to use this benchmark approach for future detection models.

Drone detection

Model for segmenting and detecting drones. What a wild swing after entry for breast model, huh. I don't really know, just had an idea, made it work, here we are.

I would highly advice against using it in anything serious.

Starting from v03. Consider it as v1, since v03 is my internal iteration.

HIGHLY SENSITIVE TO DRONE MODELS - will have hard time detecting certain types, especially close-up. Performs poorly on cluttered background.

Model	Target	mAP 50	mAP 50-95	Classes	Dataset size	Training Resolution
Anzhcs Drones v03 1024 y11n.pt	Drones	0.927(box) 0.888(mask)	0.753(box) 0.508(mask)	1(drone)	~3460	1024

Support

If you want to support me, feel free to donate on ko-fi:
https://ko-fi.com/anzhc

Or send me some BTC:
bc1qpc5kmxrpqp6x8ykdu6976s4rvsz0utk22h80j9

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Anzhc
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Anzhcs_YOLOs