Update README.md

README.md

@@ -4,18 +4,69 @@ license_name: umamusume-derivativework-guidelines
 license_link: https://umamusume.jp/derivativework_guidelines/
 ---
 
-# About **ULTIMA-YOLO models**
+This is the model repository for ULTIMA-YOLOv9, containing the following checkpoints:
+- YOLO9-E
 
-This is a part of [](ULTIMA) project.
+# About **ULTIMA-YOLO** models
 
-ULTIMA-YOLOv9 model is a facial detection model for Uma Musumes in illustrations and based on [](yolov9-e) and [](ULTIMA-YOLO dataset)
+This is a part of [ULTIMA](https://huggingface.co/datasets/UmaDiffusion/ULTIMA) project.
 
-[](ULTIMA Dataset) is **U**ma Musume **L**abeled **T**ext-**I**mage **M**ultimodal **A**lignment Dataset.   
+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)
 
+[ULTIMA Dataset](https://huggingface.co/datasets/UmaDiffusion/ULTIMA) is **U**ma Musume **L**abeled **T**ext-**I**mage **M**ultimodal **A**lignment Dataset.   
 
 
+### 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='yolov9-e.pt', device="0", fp16=False, data='data/coco.yaml')
+    stride, names, pt = model.stride, model.names, model.pt
+
+    # Load image
+    image = np.array(PIL.Image.open(image_path))
+    img = letterbox(img0, imgsz, stride=stride, auto=True)[0]
+    img = img[:, :, ::-1].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)
+```