一、代码准备
可以去官网下载https://github.com/ultralytics
环境配置同Yolov5
二、DOTA1.0数据集转换
（1）原始数据集格式如下
937.0 913.0 921.0 912.0 923.0 874.0 940.0 875.0 small-vehicle 0（2）通过坐标在 0 和 1 之间归一化的四个角点来指定边界框，支持的 OBB 数据集格式如下
class_index, x1, y1, x2, y2, x3, y3, x4, y4（3）新建一个pre_data.py文件实现标签转换
from ultralytics.data.converter import convert_dota_to_yolo_obbconvert_dota_to_yolo_obb('./datasets/DOTAv1')注意，如果你的数据是jpg或者其他格式，记得注释以下几行
（4）跳转到convert_dota_to_yolo_obb.py函数，对class_mapping进行修改
class_mapping = {"plane": 0,"baseball-diamond": 1,"bridge": 2,"ground-track-field": 3,"small-vehicle": 4,"large-vehicle": 5,"ship": 6,"tennis-court": 7,"basketball-court": 8,"storage-tank": 9,"soccer-ball-field": 10,"roundabout": 11,"harbor": 12,"swimming-pool": 13,"helicopter": 14,
}(5)在ultralytics-main下新建一个数据集文件夹并设置如下结构，
其中，images/train和images/val分别放置DOTA数据集切割后的原始图片文件，labels/train_original和labels/val_original分别放置原始的标签文件，labels/train和labels/val为空，然后运行步骤(3)的代码，运行结束转换后的标签会保存在labels/train和labels/val中，转换后的格式如下。
4 0.915039 0.891602 0.899414 0.890625 0.901367 0.853516 0.917969 0.854492三、运行代码
（1）下载预训练权重(也可以不下载，后面运行train.py时候也会自己下载)
https://docs.ultralytics.com/tasks/obb/
（2）构建数据集，按照下面目录格式，其中test可为空，一定要对应。
（3）创建一个dota8-obb.yaml，然后将路径和类别改成自己的。
# Ultralytics YOLO 🚀, AGPL-3.0 license
# DOTA 1.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.yaml
# parent
# ├── ultralytics
# └── datasets
# └── dota1 ← downloads here (2GB)# 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: ../datasets/DOTAv1 # dataset root dir
train: images/train # train images (relative to 'path') 1411 images
val: images/val # val images (relative to 'path') 458 images
# test: images/test # test images (optional) 937 images# Classes for DOTA 1.0
names:0: plane1: ship2: storage tank3: baseball diamond4: tennis court5: basketball court6: ground track field7: harbor8: bridge9: large vehicle10: small vehicle11: helicopter12: roundabout13: soccer ball field14: swimming pool
（4）修改yolov8-obb.yaml，修改nc即可.
yolov8-obb.yaml的路径是在yolo/ultralytics/cfg/models/v8下，修改nc为自己的类别数
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 Oriented Bounding Boxes (OBB) model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 15 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPss: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPsm: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPsl: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# YOLOv8.0n 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, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 3, C2f, [512]] # 12- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2f, [256]] # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]] # cat head P4- [-1, 3, C2f, [512]] # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]] # cat head P5- [-1, 3, C2f, [1024]] # 21 (P5/32-large)- [[15, 18, 21], 1, OBB, [nc, 1]] # OBB(P3, P4, P5)（5）新建一个train.py，我使用的权重是“yolov8n-obb.pt”，设置相关参数如下，即可运行。值得注意的是：如果你使用的权重是“yolov8n-obb.pt”，只需要把下面代码中的配置文件yolov8-obbs.yaml改成yolov8n-obb.yaml，依次类推。
from ultralytics import YOLOdef main():# Load a modelmodel = YOLO("yolov8n-obb.yaml").load('yolov8n-obb.pt') # build a new model from YAML# Train the modelresults = model.train(data="datasets/DOTAv1.yaml", epochs=100, imgsz=640, task = 'obb', device=0, workers=4, batch=4)if __name__ == '__main__':main()四、验证
from ultralytics import YOLOdef main():model = YOLO(r'runs/obb/train/weights/best.pt')model.val(data='datasets/DOTAv1.yaml', imgsz=640, batch=4, workers=4)# 如果你有test就用下面的语句# model.val(data='datasets/DOTAv1.yaml',split='test', imgsz=640, batch=4, workers=4)if __name__ == '__main__':main()五、推理
from ultralytics import YOLO
from PIL import Image# Load a model
model = YOLO("runs/obb/train/weights/best.pt") # pretrained YOLO11n model# Run batched inference on a list of images
results = model(["datasets/0496.png", "datasets/0497.png"]) # return a list of Results objects# Process results list
for idx, result in enumerate(results):print(result)boxes = result.boxes # Boxes object for bounding box outputsmasks = result.masks # Masks object for segmentation masks outputskeypoints = result.keypoints # Keypoints object for pose outputsprobs = result.probs # Probs object for classification outputsobb = result.obb # Oriented boxes object for OBB outputs# result.show() # display to screenim_bgr = result.plot(labels=False) # BGR-order numpy arrayim_rgb = Image.fromarray(im_bgr[..., ::-1]) # RGB-order PIL imageim_rgb.save("result{}.jpg".format(idx))# result.save(filename="result{}.jpg".format(idx)) # save to disk