使用YOLOv8训练自己的目标检测数据集（VOC格式/COCO格式）

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1. 下载项目

git clone https://github.com/ultralytics/ultralytics.git

2. 搭建环境

conda create --name ultralytics python==3.8
conda activate ultralytics
# 电脑是CUDA11.1的
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.htmlpip install ultralytics

3. 数据集格式转换

3.1 VOC格式转YOLO格式

• VOC格式

── VOCdevkit
└── VOC2007├── Annotations	# 存放图片对应的xml文件，与JPEGImages图片一一对应├── ImageSets│   └── Main	# 存放train.txt、val.txt└── JPEGImages	# 存放所有图片文件

• YOLO格式

── VOCdevkit
├── images
│   ├── train	# 存放训练集图片
│   └── val	# 存放验证集图片
└── labels├── train	# 存放训练集标注文件└── val	# 存放验证集标注文件

• 转换脚本

from tqdm import tqdm
import shutil
from pathlib import Path
import xml.etree.ElementTree as ETdef convert_label(path, lb_path, year, image_id, names):def convert_box(size, box):dw, dh = 1. / size[0], 1. / size[1]x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]return x * dw, y * dh, w * dw, h * dhin_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')out_file = open(lb_path, 'w')tree = ET.parse(in_file)root = tree.getroot()size = root.find('size')w = int(size.find('width').text)h = int(size.find('height').text)for obj in root.iter('object'):cls = obj.find('name').textif cls in names:xmlbox = obj.find('bndbox')bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])cls_id = names.index(cls)  # class idout_file.write(" ".join(str(a) for a in (cls_id, *bb)) + '\n')else:print("category error: ", cls)year = "2007"
image_sets = ["train", "val"]
path = Path("F:/vsCode/ultralytics/datasets/VOCdevkit/")
class_names = ["apple"]for image_set in image_sets:imgs_path = path / 'images' / f'{image_set}'lbs_path = path / 'labels' / f'{image_set}'imgs_path.mkdir(exist_ok=True, parents=True)lbs_path.mkdir(exist_ok=True, parents=True)with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:image_ids = f.read().strip().split()for id in tqdm(image_ids, desc=f'{image_set}'):f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img pathlb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path# f.rename(imgs_path / f.name)  # move imageshutil.copyfile(f, imgs_path / f.name) # copy imageconvert_label(path, lb_path, year, id, class_names)  # convert labels to YOLO format

数据集文件夹目录如下

3.2 COCO格式转YOLO格式

• COCO格式

── Apple
├── train
│   ├── _annotations.coco.json	# 训练集标注文件
│   ├── 00001.jpg
│   ├── 00003.jpg
│   └── ...
└── valid├── _annotations.coco.json	# 验证集标注文件├── 00002.jpg├── 00004.jpg└── ...

• 转换脚本

import json
import os
import shutil
from tqdm import tqdmcoco_path = "F:/datasets/Apple_Detection_Swift-YOLO_192"
output_path = "F:/vsCode/ultralytics/datasets/Apple"os.makedirs(os.path.join(output_path, "images", "train"), exist_ok=True)
os.makedirs(os.path.join(output_path, "images", "val"), exist_ok=True)
os.makedirs(os.path.join(output_path, "labels", "train"), exist_ok=True)
os.makedirs(os.path.join(output_path, "labels", "val"), exist_ok=True)with open(os.path.join(coco_path, "train", "_annotations.coco.json"), "r") as f:train_annotations = json.load(f)with open(os.path.join(coco_path, "valid", "_annotations.coco.json"), "r") as f:val_annotations = json.load(f)# Iterate over the training images
for image in tqdm(train_annotations["images"]):width, height = image["width"], image["height"]scale_x = 1.0 / widthscale_y = 1.0 / heightlabel = ""for annotation in train_annotations["annotations"]:if annotation["image_id"] == image["id"]:# Convert the annotation to YOLO formatx, y, w, h = annotation["bbox"]x_center = x + w / 2.0y_center = y + h / 2.0x_center *= scale_xy_center *= scale_yw *= scale_xh *= scale_yclass_id = annotation["category_id"]label += "{} {} {} {} {}\n".format(class_id, x_center, y_center, w, h)# Save the image and labelshutil.copy(os.path.join(coco_path, "train", image["file_name"]), os.path.join(output_path, "images", "train", image["file_name"]))with open(os.path.join(output_path, "labels", "train", image["file_name"].replace(".jpg", ".txt")), "w") as f:f.write(label)# Iterate over the validation images
for image in tqdm(val_annotations["images"]):width, height = image["width"], image["height"]scale_x = 1.0 / widthscale_y = 1.0 / heightlabel = ""for annotation in val_annotations["annotations"]:if annotation["image_id"] == image["id"]:# Convert the annotation to YOLO formatx, y, w, h = annotation["bbox"]x_center = x + w / 2.0y_center = y + h / 2.0x_center *= scale_xy_center *= scale_yw *= scale_xh *= scale_yclass_id = annotation["category_id"]label += "{} {} {} {} {}\n".format(class_id, x_center, y_center, w, h)# Save the image and labelshutil.copy(os.path.join(coco_path, "valid", image["file_name"]), os.path.join(output_path, "images", "val", image["file_name"]))with open(os.path.join(output_path, "labels", "val", image["file_name"].replace(".jpg", ".txt")), "w") as f:f.write(label)

4. 训练数据

找到ultralytics/cfg/datasets/VOC.yaml，复制一份命名为VOC_self.yaml。

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: F:/vsCode/ultralytics/datasets/VOCdevkit
train: # train images (relative to 'path')  16551 images- images/train
val: # val images (relative to 'path')  4952 images- images/val
test: # test images (optional)- images/val# Classes
names:0: apple

根据README文件选择预训练模型，最好先手动下载放置在项目主目录下。

训练命令：

yolo task=detect mode=train model=yolov8x.pt data=f:/ultralytics/ultralytics/cfg/datasets/VOC_self.yaml epochs=100 batch=4 device=0

• 如果想从头开始构建新模型，则model参数设置为yolov8x.yaml
• 使用自己的数据集，则data参数最好使用绝对路径
• 如果数据集进行了修改，比如标注文件调整了、图片增多了等等，那么在训练前一定要先把labels文件夹下面的train.cache和val.cache删掉再运行训练命令

训练得到的模型保存到runs/detect/train文件夹下

5. 推理预测

yolo task=detect mode=predict model=runs\detect\train\weights\best.pt source=datasets\VOCdevkit\images\val device=0

6. 模型导出

将训练好的pt模型文件导出为onnx格式的

yolo task=detect mode=export model=runs\detect\train\weights\best.pt format=onnx

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• 遇到的问题

由于没提前安装onnx，运行后会自动下载最新版本的onnx，接着就会报错max() arg is an empty sequence

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• 解决方法

1）按照输出可以知道Ultralytics要求的onnx>=1.12.0，最好就是直接安装1.12.0版本的，所以pip install onnx==1.12.0；

2）直接使用上方的CLI命令导出onnx还是会报max() arg is an empty sequence，需要改用python脚本来导出，并指定onnx的opset设置为13；

from ultralytics import YOLOmodel = YOLO('F:/vsCode/ultralytics/runs/detect/train2/weights/best.pt')
model.export(format='onnx', opset=13)

3）运行该导出脚本即可导出成功

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