đ Anzhc's YOLOs
This repository contains a series of YOLO models trained on self - annotated datasets or with the help of friends. These models are designed for various object - detection tasks, such as face segmentation, eyes segmentation, and more.
đ Quick Start
The YOLO models in this repo are trained on specific datasets. If you want to request a custom model, you can contact the author on Discord (anzhc). All model names in the tables come with download links.
⨠Features
- Multiple Detection Tasks: Cover a wide range of detection and segmentation tasks, including face, eyes, head + hair, breasts, drones, and anime art scoring.
- Varied Model Versions: Different versions of models are available for each task, trained on different datasets and with different resolutions, providing options for various application scenarios.
- Performance Metrics: mAP 50 and mAP 50 - 95 metrics are provided for most models to evaluate their performance.
đĻ Installation
No installation steps are provided in the original README.
đģ Usage Examples
No code examples are provided in the original README.
đ Documentation
Available Models
Face segmentation
- Universal: A series of models aiming at accurate face detection and segmentation, trained on a closed self - annotated dataset.
| 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 (Multi - scale) |
| 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 |
The v3 model has a slightly different face target compared to v2. Starting from v3, the author is moving to YOLO11 models as they seem to be a direct upgrade over v8.
- Real Face, gendered: Trained mostly on real photos, performs poorly with illustrations but can be used for male/female detection.
| 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 |
Eyes segmentation
Trained for inpainting eyes with the Adetailer extension, specializing in detecting anime eyes.
| 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 for detecting head + hair, useful in likeness inpaint pipelines.
| 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: Trained on anime images, has weak realistic performance.
| 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: Can be used for tagging and moderating content.
| Model | Target | Classes | Dataset size | Training Resolution |
| --------------------------- | --------------------- |---------------|------------|-------------------|
| Anzhcs Breast Size det cls v8 y11m.pt | Breasts: illustration and real | 15(size range) | ~16100 | 640 |
The mAPs are not displayed in the table as more complex stats are needed for this model.
Drone detection
A model for segmenting and detecting drones. The author highly advises against using it in anything serious.
| 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 |
Anime Art Scoring
A classification model for assigning a percentile group based on human preference.
| Model |
Target |
Top - 1 acc/(w/ margin(1/2/3)) |
Top - 2 acc |
Top - 3 acc |
Classes |
Dataset size |
Training Resolution |
| Anzhcs Anime Score CLS v1.pt |
Anime illustration |
0.336(0.467/0.645/0.679) |
0.566 |
0.696 |
10(top10 to top100) |
~98000 |
224 |
Support
If you want to support the author, you can donate on ko - fi: [https://ko - fi.com/anzhc](https://ko - fi.com/anzhc) or send some BTC to: bc1qpc5kmxrpqp6x8ykdu6976s4rvsz0utk22h80j9
đ§ Technical Details
No technical details are provided in the original README.
đ License
The license for this project is AGPL - 3.0.
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Transformers