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adetailer
intellectusartificialis/adetailer
adetailer is a machine learning model from intellectusartificialis. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for ultralytics. The card lists the license as agpl-3.0.
- Anime Face CreateML - xml2txt - AN - wider face
All-time downloads
2.8K
Public
Repo size
225 MB
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1
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Repo makeup
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.pt225 MB · 100%
At a glance
- Library
- ultralytics
- License
- agpl-3.0
- Access
- Public
- Created
- Sep 11, 2023
- Updated
- Sep 11, 2023
- SHA
- 7aa0a616
Model card
From the Hugging Face model README
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 |
| hand_yolov8n.pt | 2D / realistic hand | 0.767 | 0.505 |
| hand_yolov8s.pt | 2D / realistic hand | 0.794 | 0.527 |
| person_yolov8n-seg.pt | 2D / realistic person | 0.782 (bbox)<br/>0.761 (mask) | 0.555 (bbox)<br/>0.460 (mask) |
| person_yolov8s-seg.pt | 2D / realistic person | 0.824 (bbox)<br/>0.809 (mask) | 0.605 (bbox)<br/>0.508 (mask) |
| person_yolov8m-seg.pt | 2D / realistic person | 0.849 (bbox)<br/>0.831 (mask) | 0.636 (bbox)<br/>0.533 (mask) |
| deepfashion2_yolov8s-seg.pt | realistic clothes | 0.849 (bbox)<br/>0.840 (mask) | 0.763 (bbox)<br/>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
- Library
- ultralytics
- License
- agpl-3.0
- Created
- Sep 11, 2023
- Updated
- Sep 11, 2023