YOLO11目标检测：从代码实现到Coovally平台无代码快速部署

<object-class> <x_center> <y_center> <width> <height>

import json
import os

# 定义标签映射，将类别名称映射为ID
label_map = {
    "car": 0,  # 汽车
    "bus": 1   # 公交车
}

def convert_labelme_to_yolo(json_path, output_dir):
    with open(json_path, 'r') as f:
        labelme_data = json.load(f)  # 读取JSON数据

    image_width = labelme_data['imageWidth']
    image_height = labelme_data['imageHeight']

    yolo_annotations = []

    for shape in labelme_data['shapes']:
        label = shape['label']
        if label not in label_map:
            continue  # 如果标签未定义，则忽略

        class_id = label_map[label]  # 获取类别ID
        points = shape['points']
        if shape['shape_type'] == 'rectangle':
            (x1, y1), (x2, y2) = points
        else:
            continue  # 如果是多边形或其他形状，暂时不处理

        # 计算YOLO11格式所需的中心点和宽高
        x_center = (x1 + x2) / 2.0 / image_width
        y_center = (y1 + y2) / 2.0 / image_height
        width = (x2 - x1) / image_width
        height = (y2 - y1) / image_height

        yolo_annotations.append(f"{class_id} {x_center} {y_center} {width} {height}")

    output_file = os.path.join(output_dir, os.path.splitext(os.path.basename(json_path))[0] + '.txt')
    with open(output_file, 'w') as f:
        f.write('\n'.join(yolo_annotations))

def process_folder(input_folder, output_folder):
    os.makedirs(output_folder, exist_ok=True)
    for filename in os.listdir(input_folder):
        if filename.endswith(".json"):
            json_path = os.path.join(input_folder, filename)
            convert_labelme_to_yolo(json_path, output_folder)

# 示例使用
input_folder = "./json_labels"  # 输入json_labels文件夹路径
output_folder = "./yolo_labels"  # 输出YOLO格式标签文件夹路径

process_folder(input_folder, output_folder)  # 执行转换

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
 
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
 
# YOLO11n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
  - [-1, 2, C3k2, [256, False, 0.25]]
  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]
  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  - [-1, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9
  - [-1, 2, C2PSA, [1024]] # 10
 
# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 13
 
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
 
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 13], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
 
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 10], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
 
  - [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)

# YOLO11 Dataset Configuration

path: ./datasets/det_auto_parts  # 数据集根目录
train: train/images              # 训练集图像路径
val: val/images                  # 验证集图像路径

names:
  0: car
  1: bus

from ultralytics import YOLO

# 加载YOLO11模型配置文件和预训练权重
model = YOLO("yolo11m.yaml").load("weights/yolo11m.pt")

# 训练参数设置
train_params = {
    'data': "auto-parts-det.yaml",  # 数据集配置文件路径
    'epochs': 100,                  # 训练轮次
    'imgsz': 640,                   # 输入图像大小
    'batch': 8,                     # 批次大小
    'save': True,                   # 是否保存模型
    'device': "cuda",               # 使用GPU训练
    'optimizer': 'Adam',            # 优化器选择
    'lr0': 0.01,                    # 初始学习率
}

# 开始训练
model.train(**train_params)

# 加载训练好的模型
model = YOLO("yolo11m.yaml").load("weights/yolo11m.pt")

# 对图像进行推理
results = model.predict("path_to_your_image.jpg")

# 输出检测结果
results.show()  # 显示结果