Anzhcs YOLOs

---
license: agpl-3.0
tags:

• pytorch

• YOLOv8

• art

• Ultralytics

base_model:

• Ultralytics/YOLO11

• Ultralytics/YOLOv8

library_name: ultralytics
pipeline_tag: object-detection
metrics:

• mAP50

• mAP50-95

---

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.

Known Adetailer Issues

~~Ultralytics 8.3.217 updates mask handling, which breaks function in main Adetailer repo. Install Ultralytics==8.3.216 or lower. Alternatively - use forks that fix this.~~ - Fixed in main repo.

My Adetailer fork

I've added some features to make Adetailer more usable and less manual - https://github.com/Anzhc/aadetailer-reforge

#### 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
> - *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.
| 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 |0.464(box) 0.453(mask) | 0.309(box) 0.207(mask)|1(face) |~500| 640|
| Anzhc Face seg 768 v2 y8n.pt | Face: illustration, real | ^ | ^ |1(face) |~500| 768|
| Anzhc Face seg 768MS v2 y8n.pt | Face: illustration, real | ^ | ^ |1(face) |~500| 768|(Multi-scale)|
| Anzhc Face seg 1024 v2 y8n.pt | Face: illustration, real | ^ | ^|1(face) |~500| 1024|
| Anzhc Face seg 640 v3 y11n.pt | Face: illustration | 0.524(box) 0.516(mask) | 0.387(box) 0.297(mask)|1(face) |~660| 640|
| [Anzhc Face seg 640 v4 y11n.pt]() | Face: illustration, real | 0.835(box) 0.800(mask) | 0.537(box) 0.467(mask)|1(face) |~1030| 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.

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.

!image

!hjkhgjkghj

#### 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.
!image/png

!image/png

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|

!image/png

!image/png

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](https://huggingf