Anzhcs YOLOs

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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
• Drone detection
• Anime Art Scoring
• 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.

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.

UPDATE: V4 - Addition of high complexity data (over 100 instances per image), more simple realistic and AI-generated faces.

Benchmark was performed in 640px. V2 was condensed to single model, as their differences are marginal.
Benchmark results were recomputed on new val based on V4 dataset(images previously in val remained in val, so no contamination for previous versions), stats updated accordingly.

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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.

Benchmark was performed in 640px. image/png

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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).

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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.

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Breasts:

Breasts segmentation:

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

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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.

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

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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...

Anzhc Breasts Seg v1 1024s.pt

pt · 22.9 MB · SHA-256 413a9b948a40…b395 · Hugging Face