---
license: other
license_name: umamusume-derivativework-guidelines
license_link: https://umamusume.jp/derivativework_guidelines/
datasets:
- UmaDiffusion/ULTIMA-YOLO
pipeline_tag: object-detection
tags:
- YOLO
- YOLOv9
---
# About **ULTIMA-YOLO** models
This is a part of [ULTIMA](https://huggingface.co./datasets/UmaDiffusion/ULTIMA) project.
ULTIMA is **U**ma Musume **L**abeled **T**ext-**I**mage **M**ultimodal **A**lignment.
ULTIMA-YOLOv9 model is a facial detection model for Uma Musumes in illustrations and based on [yolov9](https://arxiv.org/abs/2402.13616)-e and [ULTIMA-YOLO dataset](https://huggingface.co./datasets/UmaDiffusion/ULTIMA-YOLO)
This is the model repository for ULTIMA-YOLOv9, containing the following checkpoints:
- YOLO9-E
### How to Use
Clone YOLOv9 repository.
```
git clone https://github.com/WongKinYiu/yolov9.git
cd yolov9
```
Download the weights using `hf_hub_download` and use the loading function in helpers of YOLOv9.
```python
from huggingface_hub import hf_hub_download
hf_hub_download("UmaDiffusion/ULTIMA-YOLOv9", filename="ultima_yolov9-e.pt", local_dir="./")
```
Load the model.
```python
# make sure you have the following dependencies
import torch
import numpy as np
from models.common import DetectMultiBackend
from utils.general import non_max_suppression, scale_boxes
from utils.torch_utils import select_device, smart_inference_mode
from utils.augmentations import letterbox
import PIL.Image
@smart_inference_mode()
def predict(image_path, weights='ultima_yolov9-e.pt', imgsz=640, conf_thres=0.1, iou_thres=0.45):
# Initialize
device = select_device('0')
model = DetectMultiBackend(weights=weights, device=device, fp16=False)
stride, names, pt = model.stride, model.names, model.pt
# Load image
image = np.array(PIL.Image.open(image_path).convert("RGB"))
img = letterbox(image, imgsz, stride=stride, auto=True)[0]
img = img.transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(device).float()
img /= 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
pred = model(img, augment=False, visualize=False)
# Apply NMS
pred = non_max_suppression(pred[0][0], conf_thres, iou_thres, classes=None, max_det=1000)
```
or use `detect.py` in yolov9 repo.
```bash
python ./detect.py --source [image_path] --device 0 --img 1280 --weights './ultima_yolov9-e.pt' --name ultima_yolov9_1280_detect
```
# Training Infomation
- Batch Size: 32
- Resolution: 640
- Epochs: 300, chosen best mAP
- GPU: 1x A6000 48GB
- Dataset: [ULTIMA-YOLO](https://huggingface.co./datasets/UmaDiffusion/ULTIMA-YOLO)
# Statistics
- Train: 3,991 images
- Val: 399 images
| Character Name | # in Train | # in Val | Precision | Recall | mAP50 | mAP50-95 |
|:------------------:|:---:|:---:|:---:|:---:|:---:|:---:|
| Agnes Tachyon | 187 | 35 | 0.957 | 0.886 | 0.961 | 0.765 |
| Air Groove | 87 | 12 | 1 | 0.835 | 0.933 | 0.713 |
| Air Shakur | 75 | 12 | 0.986 | 1 | 0.995 | 0.909 |
| Akikawa Yayoi | 25 | 3 | 1 | 0.693 | 0.995 | 0.648 |
| Admire Vega | 74 | 16 | 1 | 0.754 | 0.894 | 0.707 |
| Agnes Digital | 50 | 6 | 0.992 | 0.833 | 0.972 | 0.803 |
| Anshinzawa Sasami | 25 | 1 | 0.956 | 1 | 0.995 | 0.796 |
| Aston Machan | 55 | 3 | 1 | 0.726 | 0.995 | 0.912 |
| Bamboo Memory | 41 | 3 | 0.97 | 1 | 0.995 | 0.895 |
| Biko Pegasus | 34 | 3 | 0.972 | 1 | 0.995 | 0.84 |
| Byerley Turk | 43 | 2 | 0.951 | 1 | 0.995 | 0.855 |
| Bitter Glace | 24 | 0 | 0.888 | 0.875 | 0.944 | 0.776 |
| Biwa Hayahide | 52 | 8 | 0.821 | 1 | 0.995 | 0.846 |
| Copano Rickey | 51 | 5 | 0.969 | 0.667 | 0.864 | 0.69 |
| Curren Chan | 54 | 9 | 0.996 | 1 | 0.995 | 0.801 |
| Cheval Grand | 43 | 13 | 0.998 | 1 | 0.995 | 0.783 |
| Twin Turbo | 120 | 13 | 0.982 | 1 | 0.995 | 0.842 |
| Daiichi Ruby | 57 | 5 | 0.963 | 1 | 0.995 | 0.949 |
| Darley Arabian | 48 | 2 | 1 | 0.837 | 0.995 | 0.819 |
| Daring Tact | 62 | 5 | 0.997 | 1 | 0.995 | 0.841 |
| Daitaku Helios | 100 | 11 | 1 | 0.903 | 0.961 | 0.787 |
| Daiwa Scarlet | 114 | 19 | 0.987 | 1 | 0.995 | 0.707 |
| El Condor Pasa | 65 | 6 | 0.951 | 1 | 0.995 | 0.808 |
| Eishin Flash | 39 | 5 | 0.853 | 1 | 0.995 | 0.927 |
| Fuji Kiseki | 48 | 6 | 1 | 0.875 | 0.995 | 0.88 |
| Fine Motion | 55 | 7 | 0.989 | 0.875 | 0.906 | 0.71 |
| Gold City | 49 | 8 | 0.942 | 0.938 | 0.991 | 0.81 |
| Gold Ship | 146 | 16 | 0.858 | 1 | 0.995 | 0.895 |
| Godolphin Barb | 44 | 2 | 0.84 | 0.833 | 0.851 | 0.659 |
| Grass Wonder | 74 | 6 | 1 | 0.797 | 0.995 | 0.792 |
| Hishi Akebono | 39 | 4 | 0.989 | 1 | 0.995 | 0.766 |
| Hishi Amazon | 46 | 6 | 0.993 | 1 | 0.995 | 0.835 |
| Hayakawa Tazuna | 34 | 5 | 1 | 0.659 | 0.922 | 0.638 |
| Hishi Miracle | 52 | 6 | 0.971 | 0.75 | 0.945 | 0.769 |
| Happy Meek | 51 | 4 | 1 | 0.787 | 0.938 | 0.808 |
| Hokko Tarumae | 50 | 9 | 1 | 0.678 | 0.995 | 0.76 |
| Haru Urara | 69 | 9 | 0.986 | 0.917 | 0.989 | 0.747 |
| Ikuno Dictus | 96 | 12 | 0.873 | 1 | 0.995 | 0.858 |
| Ines Fujin | 41 | 7 | 0.947 | 1 | 0.995 | 0.898 |
| Inari One | 46 | 2 | 0.856 | 1 | 0.995 | 0.656 |
| Jungle Pocket | 53 | 6 | 1 | 0.85 | 0.995 | 0.747 |
| King Halo | 77 | 6 | 0.975 | 1 | 0.995 | 0.773 |
| Kashimoto Riko | 34 | 3 | 1 | 0.778 | 0.995 | 0.823 |
| Kiryuin Aoi | 44 | 4 | 0.997 | 0.895 | 0.929 | 0.712 |
| Kitasan Black | 116 | 19 | 0.974 | 1 | 0.995 | 0.909 |
| K.S.Miracle | 48 | 3 | 0.982 | 1 | 0.995 | 0.852 |
| Katsuragi Ace | 43 | 4 | 0.989 | 1 | 0.995 | 0.881 |
| Kawakami Princess | 50 | 7 | 0.975 | 1 | 0.995 | 0.841 |
| Little Cocon | 51 | 3 | 1 | 0.567 | 0.995 | 0.796 |
| Light Hello | 25 | 2 | 0.993 | 1 | 0.995 | 0.788 |
| Mr. C.B. | 91 | 13 | 1 | 0.659 | 0.995 | 0.703 |
| Meisho Doto | 59 | 7 | 0.988 | 1 | 0.995 | 0.782 |
| Mihono Bourbon | 84 | 13 | 1 | 0.955 | 0.994 | 0.779 |
| Manhattan Cafe | 144 | 32 | 0.876 | 0.884 | 0.967 | 0.797 |
| Mejiro Ardan | 58 | 8 | 0.985 | 0.833 | 0.869 | 0.723 |
| Mejiro Bright | 55 | 6 | 0.987 | 1 | 0.995 | 0.813 |
| Mejiro Dober | 56 | 5 | 0.981 | 0.933 | 0.972 | 0.785 |
| Mejiro McQueen | 272 | 30 | 0.98 | 1 | 0.995 | 0.873 |
| Mejiro Ryan | 43 | 7 | 0.998 | 1 | 0.995 | 0.849 |
| Matikanefukukitaru | 52 | 7 | 1 | 0.952 | 0.995 | 0.719 |
| Matikanetannhauser | 87 | 13 | 0.996 | 1 | 0.995 | 0.81 |
| Mejiro Palmer | 95 | 11 | 0.893 | 1 | 0.929 | 0.822 |
| Mejiro Ramonu | 52 | 9 | 0.993 | 1 | 0.995 | 0.748 |
| Maruzensky | 43 | 7 | 0.984 | 1 | 0.995 | 0.684 |
| Marvelous Sunday | 40 | 6 | 1 | 0.702 | 0.995 | 0.668 |
| Nakayama Festa | 47 | 7 | 0.992 | 1 | 0.995 | 0.829 |
| Nice Nature | 96 | 8 | 0.993 | 1 | 0.995 | 0.723 |
| Narita Brian | 86 | 13 | 0.827 | 1 | 0.962 | 0.778 |
| Narita Taishin | 55 | 5 | 0.899 | 0.857 | 0.978 | 0.938 |
| Nishino Flower | 48 | 7 | 0.97 | 1 | 0.995 | 0.72 |
| Narita Top Road | 50 | 9 | 0.988 | 1 | 0.995 | 0.834 |
| Oguri Cap | 94 | 10 | 0.997 | 0.92 | 0.945 | 0.744 |
| Rice Shower | 165 | 25 | 0.992 | 1 | 0.995 | 0.89 |
| Sakura Bakushin O | 55 | 7 | 1 | 0.949 | 0.995 | 0.795 |
| Symboli Rudolf | 157 | 17 | 0.987 | 0.889 | 0.975 | 0.748 |
| Sakura Chiyono O | 48 | 9 | 0.946 | 0.8 | 0.941 | 0.835 |
| Seiun Sky | 72 | 10 | 0.98 | 1 | 0.995 | 0.842 |
| Sakura Laurel | 44 | 6 | 0.944 | 1 | 0.995 | 0.895 |
| Shinko Windy | 46 | 1 | 0.96 | 1 | 0.995 | 0.949 |
| Seeking the Pearl | 34 | 2 | 0.985 | 1 | 0.995 | 0.844 |
| Symboli Kris S | 68 | 6 | 0.87 | 0.958 | 0.943 | 0.728 |
| Smart Falcon | 53 | 7 | 0.976 | 1 | 0.995 | 0.876 |
| Super Creek | 48 | 4 | 1 | 0.959 | 0.995 | 0.736 |
| Special Week | 147 | 14 | 1 | 0.975 | 0.995 | 0.777 |
| Silence Suzuka | 129 | 18 | 0.993 | 1 | 0.995 | 0.84 |
| Sirius Symboli | 60 | 9 | 0.962 | 1 | 0.995 | 0.849 |
| Satono Crown | 47 | 2 | 0.993 | 0.75 | 0.925 | 0.746 |
| Satono Diamond | 79 | 12 | 0.98 | 0.75 | 0.775 | 0.649 |
| Sweep Tosho | 42 | 4 | 0.951 | 1 | 0.995 | 0.895 |
| Tap Dance City | 49 | 4 | 0.995 | 1 | 0.995 | 0.832 |
| Taiki Shuttle | 50 | 7 | 0.883 | 1 | 0.939 | 0.756 |
| Tokai Teio | 239 | 23 | 0.994 | 1 | 0.995 | 0.56 |
| Tamamo Cross | 59 | 6 | 1 | 0.86 | 0.99 | 0.748 |
| T.M. Opera O | 85 | 13 | 0.986 | 1 | 0.995 | 0.838 |
| Tanino Gimlet | 52 | 6 | 0.986 | 1 | 0.995 | 0.771 |
| Mayano Top Gun | 70 | 5 | 1 | 0.824 | 0.995 | 0.787 |
| Tosen Jordan | 68 | 9 | 0.959 | 1 | 0.995 | 0.801 |
| Tsurumaru Tsuyoshi | 38 | 2 | 0.984 | 1 | 0.995 | 0.736 |
| Neo Universe | 47 | 5 | 1 | 0.806 | 0.945 | 0.753 |
| Vodka | 110 | 15 | 0.954 | 1 | 0.995 | 0.895 |
| Wonder Acute | 53 | 1 | 0.976 | 0.8 | 0.962 | 0.877 |
| Winning Ticket | 47 | 5 | 0.997 | 1 | 0.995 | 0.889 |
| Yukino Bijin | 44 | 7 | 1 | 0.965 | 0.995 | 0.904 |
| Yaeno Muteki | 39 | 5 | 0.975 | 1 | 0.995 | 0.932 |
| Yamanin Zephyr | 42 | 3 | 0.976 | 0.714 | 0.96 | 0.747 |
| Zenno Rob Roy | 51 | 7 | 0.958 | 1 | 0.995 | 0.895 |
| Furioso | 15 | 0 | 0.938 | 1 | 0.995 | 0.995 |
| Transcend | 40 | 2 | 0.964 | 1 | 0.995 | 0.796 |
| Espoir City | 30 | 1 | 0.939 | 1 | 0.995 | 0.895 |
| North Flight | 40 | 2 | 0.946 | 1 | 0.995 | 0.597 |
| Dantsu Flame | 30 | 1 | 0.878 | 1 | 0.995 | 0.895 |
| No Reason | 26 | 0 | 0.961 | 0.667 | 0.699 | 0.53 |
| Still in Love | 28 | 1 | 0.961 | 1 | 0.995 | 0.895 |
| Samson Big | 25 | 1 | 0.891 | 1 | 0.995 | 0.697 |
| Sounds of Earth | 53 | 3 | 0.972 | 1 | 0.995 | 0.857 |
| Royce and Royce | 30 | 2 | 0.942 | 1 | 0.995 | 0.398 |
| Duramente | 43 | 1 | 0.939 | 1 | 0.995 | 0.895 |
| Rhein Kraft | 31 | 3 | 0.975 | 1 | 0.995 | 0.799 |
| Cesario | 37 | 1 | 0.947 | 1 | 0.995 | 0.796 |
| Air Messiah | 23 | 1 | 0.964 | 1 | 0.995 | 0.927 |
| Daring Heart | 28 | 0 | 0.961 | 1 | 0.995 | 0.858 |
| Orfevre | 25 | 3 | 0.947 | 1 | 0.995 | 0.995 |
| Gentildonna | 40 | 1 | 0.944 | 1 | 0.995 | 0.597 |
| Win Variation | 21 | 2 | 0.94 | 1 | 0.995 | 0.895 |
| Venus Paques | 37 | 2 | 0.935 | 1 | 0.995 | 0.796 |
| Rigantona | 28 | 1 | 0.995 | 1 | 0.995 | 0.91 |
| Sonon Elfie | 29 | 1 | 0.994 | 1 | 0.995 | 0.815 |
