Instructions to use Runware/adetailer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Runware/adetailer with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("Runware/adetailer") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
library_name: ultralytics
datasets:
- wider_face
- skytnt/anime-segmentation
tags:
- pytorch
YOLOv8 Detection Model
Datasets
Face
Hand
Person
- coco2017 (only person)
- AniSeg
- skytnt/anime-segmentation
deepfashion2
| id | label |
|---|---|
| 0 | short_sleeved_shirt |
| 1 | long_sleeved_shirt |
| 2 | short_sleeved_outwear |
| 3 | long_sleeved_outwear |
| 4 | vest |
| 5 | sling |
| 6 | shorts |
| 7 | trousers |
| 8 | skirt |
| 9 | short_sleeved_dress |
| 10 | long_sleeved_dress |
| 11 | vest_dress |
| 12 | sling_dress |
Info
| Model | Target | mAP 50 | mAP 50-95 |
|---|---|---|---|
| face_yolov8n.pt | 2D / realistic face | 0.660 | 0.366 |
| face_yolov8n_v2.pt | 2D / realistic face | 0.669 | 0.372 |
| face_yolov8s.pt | 2D / realistic face | 0.713 | 0.404 |
| face_yolov8m.pt | 2D / realistic face | 0.737 | 0.424 |
| face_yolov9c.pt | 2D / realistic face | 0.748 | 0.433 |
| hand_yolov8n.pt | 2D / realistic hand | 0.767 | 0.505 |
| hand_yolov8s.pt | 2D / realistic hand | 0.794 | 0.527 |
| hand_yolov9c.pt | 2D / realistic hand | 0.810 | 0.550 |
| person_yolov8n-seg.pt | 2D / realistic person | 0.782 (bbox) 0.761 (mask) |
0.555 (bbox) 0.460 (mask) |
| person_yolov8s-seg.pt | 2D / realistic person | 0.824 (bbox) 0.809 (mask) |
0.605 (bbox) 0.508 (mask) |
| person_yolov8m-seg.pt | 2D / realistic person | 0.849 (bbox) 0.831 (mask) |
0.636 (bbox) 0.533 (mask) |
| deepfashion2_yolov8s-seg.pt | realistic clothes | 0.849 (bbox) 0.840 (mask) |
0.763 (bbox) 0.675 (mask) |
Usage
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
path = hf_hub_download("Bingsu/adetailer", "face_yolov8n.pt")
model = YOLO(path)
import cv2
from PIL import Image
img = "https://farm5.staticflickr.com/4139/4887614566_6b57ec4422_z.jpg"
output = model(img)
pred = output[0].plot()
pred = cv2.cvtColor(pred, cv2.COLOR_BGR2RGB)
pred = Image.fromarray(pred)
pred
Unsafe files
Since getattr is classified as a dangerous pickle function, any segmentation model that uses it is classified as unsafe.
All models were created and saved using the official ultralytics library, so it's okay to use files downloaded from a trusted source.

