安装

项目地址

https://github.com/THU-MIG/yolov10

安装依赖

pip install -r requirements.txt pip install -e . pip install supervision git+https://github.com/THU-MIG/yolov10.git

下载模型权重

https://modelscope.cn/models/THU-MIG/Yolov10/summary

放到https://github.com/THU-MIG/yolov10.git根目录

测试

#在https://github.com/THU-MIG/yolov10根目录下执行

from ultralytics import YOLOv10
import supervision as sv
import cv2

MODEL_PATH = 'yolov10n.pt'
IMAGE_PATH = 'dog.jpeg'

model = YOLOv10(MODEL_PATH)
image  = cv2.imread(IMAGE_PATH)
results = model(source=image, conf=0.25, verbose=False)[0]
detections = sv.Detections.from_ultralytics(results)
box_annotator = sv.BoxAnnotator()

category_dict = {
    0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
    6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
    11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
    16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
    22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
    27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
    32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
    36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
    40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
    46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
    51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
    56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
    61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
    67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
    72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
    77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
}

labels = [
    f"{category_dict[class_id]} {confidence:.2f}"
    for class_id, confidence in zip(detections.class_id, detections.confidence)
]
annotated_image = box_annotator.annotate(
    image.copy(), detections=detections, labels=labels
)

cv2.imwrite('annotated_dog.jpeg', annotated_image)

数据

目录结构

子目录

images

labels

labels详情

0代表类别 后面为坐标

坐标详解

data.yaml

names: # class names
  - 0:pen # 类别号: 类别名称 （需要改成自己的）
  #-1:cat #如果还有其他类别，以此往下加就行了，类别号请认真和自己当时目标框labels文件中的一一对应
  #-2:dog
nc: 1 # number of classes 数据集中一共有几个类别，参考上面说的
path: D:\app\image\test # 数据集路径（需要改成自己的，也就是train、test和valid目录的上级目录）
train: train/images # 训练集路径(相对于数据集路径)
val: valid/images # 验证集路径(相对于数据集路径)
test: test/images # 测试集路径(相对于数据集路径)

示例数据

http://minio.top/com-data/Road_Sign_YOLO_datasets.zip 其中minio.top 替换为本地minio储存的地址

训练

训练

yolo detect train data=D://app//image//test//data.yaml model=D:\code\money\opencv\yolov10-main\yolov10s.pt epochs=1 batch=2 imgsz=640 device=cpu

yolo val model=D:\code\money\opencv\yolov10-main\yolov10s.pt data=coco.yaml batch=256

导出

# End-to-End ONNX
yolo export model=yolov10n/s/m/b/l/x.pt format=onnx opset=13 simplify
# Predict with ONNX
yolo predict model=yolov10n/s/m/b/l/x.onnx

# End-to-End TensorRT
yolo export model=yolov10n/s/m/b/l/x.pt format=engine half=True simplify opset=13 workspace=16
# Or
trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16
# Predict with TensorRT
yolo predict model=yolov10n/s/m/b/l/x.engine