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--- |
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license: other |
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license_name: umamusume-derivativework-guidelines |
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license_link: https://umamusume.jp/derivativework_guidelines/ |
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--- |
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This is the model repository for ULTIMA-YOLOv9, containing the following checkpoints: |
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- YOLO9-E |
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# About **ULTIMA-YOLO** models |
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This is a part of [ULTIMA](https://huggingface.co/datasets/UmaDiffusion/ULTIMA) project. |
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ULTIMA-YOLOv9 model is a facial detection model for Uma Musumes in illustrations and based on [yolov9-e](https://arxiv.org/abs/2402.13616) and [ULTIMA-YOLO dataset](https://huggingface.co/datasets/UmaDiffusion/ULTIMA-YOLO) |
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[ULTIMA Dataset](https://huggingface.co/datasets/UmaDiffusion/ULTIMA) is **U**ma Musume **L**abeled **T**ext-**I**mage **M**ultimodal **A**lignment Dataset. |
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### How to Use |
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Clone YOLOv9 repository. |
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``` |
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git clone https://github.com/WongKinYiu/yolov9.git |
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cd yolov9 |
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``` |
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Download the weights using `hf_hub_download` and use the loading function in helpers of YOLOv9. |
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```python |
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from huggingface_hub import hf_hub_download |
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hf_hub_download("UmaDiffusion/ULTIMA-YOLOv9", filename="ultima_yolov9-e.pt", local_dir="./") |
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``` |
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Load the model. |
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```python |
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# make sure you have the following dependencies |
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import torch |
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import numpy as np |
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from models.common import DetectMultiBackend |
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from utils.general import non_max_suppression, scale_boxes |
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from utils.torch_utils import select_device, smart_inference_mode |
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from utils.augmentations import letterbox |
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import PIL.Image |
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@smart_inference_mode() |
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def predict(image_path, weights='ultima_yolov9-e.pt', imgsz=640, conf_thres=0.1, iou_thres=0.45): |
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# Initialize |
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device = select_device('0') |
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model = DetectMultiBackend(weights='yolov9-e.pt', device="0", fp16=False, data='data/coco.yaml') |
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stride, names, pt = model.stride, model.names, model.pt |
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# Load image |
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image = np.array(PIL.Image.open(image_path)) |
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img = letterbox(img0, imgsz, stride=stride, auto=True)[0] |
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img = img[:, :, ::-1].transpose(2, 0, 1) |
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img = np.ascontiguousarray(img) |
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img = torch.from_numpy(img).to(device).float() |
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img /= 255.0 |
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if img.ndimension() == 3: |
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img = img.unsqueeze(0) |
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# Inference |
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pred = model(img, augment=False, visualize=False) |
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# Apply NMS |
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pred = non_max_suppression(pred[0][0], conf_thres, iou_thres, classes=None, max_det=1000) |
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``` |
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