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---
license: agpl-3.0
tags:
- pytorch
- YOLOv8
- art
- Ultralytics
base_model:
- Ultralytics/YOLOv8
- Ultralytics/YOLO11
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.
#### Want to request a model?
Im open to commissions, hit me up in Discord - **anzhc**
(Also if you want to support me - https://ko-fi.com/anzhc)
> ## **Table of Contents**
> - [**Face segmentation**](#face-segmentation)
> - [*Universal*](#universal)
> - [*Real Face, gendered*](#real-face-gendered)
> - [**Eyes segmentation**](#eyes-segmentation)
> - [**Head+Hair segmentation**](#headhair-segmentation)
> - [**Breasts segmentation**](#breasts-segmentation)
> - [**Drone detection**](#drone-detection)
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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Face%20-seg.pt) | Face: illustration, real | LOST DATA | LOST DATA |2(male, female)|LOST DATA| 640|
| [Anzhc Face seg 640 v2 y8n.pt](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Face%20seg%20640%20v2%20y8n.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Face%20seg%20768%20v2%20y8n.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Face%20seg%20768MS%20v2%20y8n.pt) | Face: illustration, real | 0.807(box) 0.770(mask) | 0.601(box) 0.432(mask) |1(face) |~500| 768|(Multi-scale)|
| [Anzhc Face seg 1024 v2 y8n.pt](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Face%20seg%201024%20v2%20y8n.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Face%20seg%20640%20v3%20y11n.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhcs%20ManFace%20v02%201024%20y8n.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhcs%20WomanFace%20v05%201024%20y8n.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Eyes%20-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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20HeadHair%20seg%20y8n.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://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20HeadHair%20seg%20y8m.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Breasts%20Seg%20v1%201024n.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Breasts%20Seg%20v1%201024s.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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhc%20Breasts%20Seg%20v1%201024m.pt) | Breasts: illustration | 0.782(box),0.775(mask) | 0.644(box), 0.614(mask) |1(breasts) |~2000 |1024|
### 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](https://huggingface.co/Anzhc/Anzhcs_YOLOs/blob/main/Anzhcs%20Drones%20v03%201024%20y11n.pt) | Drones | 0.927(box) 0.888(mask) | 0.753(box) 0.508(mask) |1(drone) |~3460 |1024|
/--UNDER CONSTRUCTION--/ |