yolov11配置环境,实现OBB带方向目标检测

1. 安装GPU驱动，cuda

验证安装,nvidia-smi命令查看状态

nvcc -V查看cuda版本, 按安装后提示配置路径。

注意：

cuda选择版本，对pytorch支持有关系，目前pytorch最高支持到11.8 ，实测A5000装530驱动，安装cuda12.2会无法正确运行，可以降级安装cuda11.8，可以完美匹配。

2. 安装pytorch

用conda安装，确保版本兼容

conda install pytorch

验证安装正确，且cuda支持OK，

3. 安装python虚拟环境，方便测试

安装配置miniconda

miniconda安装-CSDN博客

4. yolo环境配置

仓库：

GitHub - ultralytics/ultralytics: Ultralytics YOLO11 🚀

官方文档，安装最好用conda方式：

快速入门 -Ultralytics YOLO 文档

安装过程遇到的错误及排查：

undefined symbol: __nvJitLinkComplete_12_4, version libnvJitLink.so.12
是torch版本匹配问题，降级到cuda11.8

官方有conda安装方式，但是外网限制问题，无法安装，会报错，连接不上conda

conda install -c conda-forge ultralytics

git安装

git目录后用pip安装可以正常使用

pip install -e .

测试python导入环境

命令行测试图片：

yolo predict model=yolo11s.pt source='https://ultralytics.com/images/bus.jpg' imgsz=320

命令行预测（predict）视频：

yolo predict model=yolo11n-seg.pt source=./1.mp4 imgsz=320

验证训练流程：

yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01

训练结果


(py39yolov11) hy@hy-C621-WD12-IPMI:~/yolopan/ultralytics$ yolo train data=coco8.yaml model=yolo                                          11n.pt epochs=10 lr0=0.01
Ultralytics 8.3.63 🚀 Python-3.9.21 torch-2.5.1+cu124 CUDA:0 (NVIDIA RTX A5000, 24248MiB)
engine/trainer: task=detect, mode=train, model=yolo11n.pt, data=coco8.yaml, epochs=10, time=Non                                          e, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, work                                          ers=8, project=None, name=train2, exist_ok=False, pretrained=True, optimizer=auto, verbose=True                                          , seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resu                                          me=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=T                                          rue, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=N                                          one, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream                                          _buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=F                                          alse, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=Fal                                          se, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, ker                                          as=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None,                                           nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_                                          momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h                                          =0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0                                          , flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip,                                           auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save                                          _dir=/home/hy/yolopan/ultralytics/runs/detect/train2from  n    params  module                                       arguments                                             0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2                                          ]1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3,                                           2]2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1,                                           False, 0.25]3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3,                                           2]4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1,                                           False, 0.25]5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3                                          , 2]6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1                                          , True]7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3                                          , 2]8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1                                          , True]9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5                                          ]10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1                                          ]11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'n                                          earest']12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                                                   13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1                                          , False]14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'n                                          earest']15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                                                   16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1,                                           False]17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3,                                           2]18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                                                   19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1                                          , False]20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3                                          , 2]21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                                                   22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1                                          , True]23        [16, 19, 22]  1    464912  ultralytics.nn.modules.head.Detect           [80, [64, 12                                          8, 256]]
YOLO11n summary: 319 layers, 2,624,080 parameters, 2,624,064 gradients, 6.6 GFLOPsTransferred 499/499 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Scanning /home/hy/yolopan/datasets/coco8/labels/train.cache... 4 images, 0 backgrounds,
val: Scanning /home/hy/yolopan/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 co
Plotting labels to /home/hy/yolopan/ultralytics/runs/detect/train2/labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'mom             entum' automatically...
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(dec             ay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to /home/hy/yolopan/ultralytics/runs/detect/train2
Starting training for 10 epochs...
Closing dataloader mosaicEpoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size1/10     0.692G      1.059      1.862      1.417         13        640: 100%|██████████| 1/1 [00:01<00:00,  1.03s/it]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,all          4         17      0.579       0.85      0.878      0.634Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size2/10     0.692G      1.219      2.578      1.548         13        640: 100%|██████████| 1/1 [00:00<00:00,  7.97it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,all          4         17      0.582       0.85      0.849      0.631Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size3/10     0.692G     0.9898      1.863      1.466         13        640: 100%|██████████| 1/1 [00:00<00:00,  9.93it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,all          4         17        0.6       0.85      0.849      0.648Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size4/10     0.692G       1.12      1.911      1.406         13        640: 100%|██████████| 1/1 [00:00<00:00, 10.29it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,all          4         17      0.613       0.85       0.85      0.649Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size5/10      0.69G      0.916      2.056      1.149         13        640: 100%|██████████| 1/1 [00:00<00:00,  6.19it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 25.48it/s]all          4         17      0.623       0.85      0.851      0.649Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size6/10     0.711G     0.9636      2.127      1.297         13        640: 100%|██████████| 1/1 [00:00<00:00,  9.93it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 23.77it/s]all          4         17      0.626       0.85      0.853      0.651Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size7/10     0.719G      1.204      2.029      1.541         13        640: 100%|██████████| 1/1 [00:00<00:00,  9.16it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 28.18it/s]all          4         17      0.624       0.85       0.89       0.64Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size8/10     0.711G      1.163       1.71      1.323         13        640: 100%|██████████| 1/1 [00:00<00:00,  9.21it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 28.27it/s]all          4         17      0.636       0.85      0.855      0.633Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size9/10     0.719G      1.048      1.896      1.427         13        640: 100%|██████████| 1/1 [00:00<00:00,  8.56it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 26.34it/s]all          4         17      0.636       0.85      0.855      0.633Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size10/10     0.711G     0.9268      1.613      1.245         13        640: 100%|██████████| 1/1 [00:00<00:00,  7.31it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 26.95it/s]all          4         17      0.635       0.85      0.858      0.63310 epochs completed in 0.002 hours.
Optimizer stripped from /home/hy/yolopan/ultralytics/runs/detect/train2/weights/last.pt, 5.5MB
Optimizer stripped from /home/hy/yolopan/ultralytics/runs/detect/train2/weights/best.pt, 5.5MBValidating /home/hy/yolopan/ultralytics/runs/detect/train2/weights/best.pt...
Ultralytics 8.3.63 🚀 Python-3.9.21 torch-2.5.1+cu124 CUDA:0 (NVIDIA RTX A5000, 24248MiB)
YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPsClass     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 33.59it/s]all          4         17      0.626       0.85      0.853      0.634person          3         10      0.614        0.6      0.604      0.281dog          1          1      0.552          1      0.995      0.796horse          1          2      0.736          1      0.995      0.675elephant          1          2      0.388        0.5      0.537      0.261umbrella          1          1      0.589          1      0.995      0.895potted plant          1          1      0.877          1      0.995      0.895
Speed: 0.2ms preprocess, 2.3ms inference, 0.0ms loss, 0.8ms postprocess per image
Results saved to /home/hy/yolopan/ultralytics/runs/detect/train2
💡 Learn more at https://docs.ultralytics.com/modes/train

验证OBB测试环境

yolo obb predict model=yolo11n-obb.pt source='https://ultralytics.com/images/boats.jpg'

识别效果：

验证OBB训练数据

支持的数据集包括：　

支持的数据集

目前支持以下带有定向边框的数据集：

DOTA-v1：DOTA 数据集的第一个版本，提供了一套全面的航空图像，并带有用于物体检测的定向边界框。
DOTA-v1.5：DOTA 数据集的中间版本，与 DOTA-v1 相比提供了更多注释和改进，用于增强物体检测任务。
DOTA-v2：DOTA（用于航空图像中物体检测的大规模数据集）第 2 版强调从航空角度进行检测，包含 170 万个实例和 11 268 幅图像的定向边界框。
DOTA8:完整 DOTA 数据集的 8 幅图像子集，适用于测试工作流程和 OBB 培训的持续集成 (CI) 检查。 ultralytics 存放处。

只是测试，可以用ｄｏｔａ８跑流程，把数据下载到本地，解压到／ｐａｔｈ／ｄａｔａｓｅｔｓ／

https://ultralytics.com/assets/dota8.zip

也可以先把ｏｂｂ模型下载，

https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt

用rolabelimg标注的数据集是

训练命令：　

yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=10 imgsz=1024


(py39yolov11) hy@hy-C621-WD12-IPMI:~/yolopan/ultralytics$ yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=10 imgsz=1024
Ultralytics 8.3.63 🚀 Python-3.9.21 torch-2.5.1+cu124 CUDA:0 (NVIDIA RTX A5000, 24248MiB)
engine/trainer: task=obb, mode=train, model=yolo11n-obb.pt, data=dota8.yaml, epochs=10, time=None, patience=100, batch=16, imgsz=1024, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=/home/hy/yolopan/ultralytics/runs/obb/train3
Overriding model.yaml nc=80 with nc=15from  n    params  module                                       arguments0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]23        [16, 19, 22]  1    505264  ultralytics.nn.modules.head.OBB              [15, 1, [64, 128, 256]]
YOLO11n-obb summary: 344 layers, 2,664,432 parameters, 2,664,416 gradients, 6.7 GFLOPsTransferred 541/541 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Scanning /home/hy/yolopan/datasets/dota8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4/4 [00:00<00:00, 52
train: New cache created: /home/hy/yolopan/datasets/dota8/labels/train.cache
val: Scanning /home/hy/yolopan/datasets/dota8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4/4 [00:00<00:00, 625.25
val: New cache created: /home/hy/yolopan/datasets/dota8/labels/val.cache
Plotting labels to /home/hy/yolopan/ultralytics/runs/obb/train3/labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: AdamW(lr=0.000526, momentum=0.9) with parameter groups 87 weight(decay=0.0), 97 weight(decay=0.0005), 96 bias(decay=0.0)
Image sizes 1024 train, 1024 val
Using 8 dataloader workers
Logging results to /home/hy/yolopan/ultralytics/runs/obb/train3
Starting training for 10 epochs...
Closing dataloader mosaicEpoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size1/10      1.62G     0.7865     0.4889      1.274        125       1024: 100%|██████████| 1/1 [00:01<00:00,  1.34s/it]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  4.45it/s]all          4          8      0.944          1      0.995      0.804Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size2/10      1.67G     0.8854     0.4443      1.459        112       1024: 100%|██████████| 1/1 [00:00<00:00,  4.53it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 14.57it/s]all          4          8      0.942          1      0.995      0.804Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size3/10      1.68G     0.7773     0.4294      1.505         80       1024: 100%|██████████| 1/1 [00:00<00:00,  7.89it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.67it/s]all          4          8       0.94          1      0.995      0.804Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size4/10      1.73G     0.7714     0.4289      1.781        106       1024: 100%|██████████| 1/1 [00:00<00:00,  7.32it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.88it/s]all          4          8      0.936          1      0.995      0.804Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size5/10      1.72G     0.8086     0.5075      1.195        122       1024: 100%|██████████| 1/1 [00:00<00:00,  8.05it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 20.90it/s]all          4          8      0.934          1      0.995      0.804Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size6/10      1.64G     0.7477     0.4211      1.381        112       1024: 100%|██████████| 1/1 [00:00<00:00,  8.29it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 21.25it/s]all          4          8      0.932          1      0.995      0.797Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size7/10       1.6G     0.7764     0.4645      1.679         90       1024: 100%|██████████| 1/1 [00:00<00:00,  8.51it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 19.87it/s]all          4          8      0.926          1      0.995      0.797Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size8/10      1.67G     0.8228     0.6261      1.396        125       1024: 100%|██████████| 1/1 [00:00<00:00,  8.60it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 19.98it/s]all          4          8      0.923          1      0.995      0.797Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size9/10      1.65G     0.7391      0.415      1.557        116       1024: 100%|██████████| 1/1 [00:00<00:00,  7.82it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 19.98it/s]all          4          8       0.92          1      0.995      0.797Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size10/10      1.67G     0.7673     0.5214      1.182        121       1024: 100%|██████████| 1/1 [00:00<00:00,  7.47it/s]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 19.88it/s]all          4          8      0.911          1      0.995      0.79710 epochs completed in 0.002 hours.
Optimizer stripped from /home/hy/yolopan/ultralytics/runs/obb/train3/weights/last.pt, 5.9MB
Optimizer stripped from /home/hy/yolopan/ultralytics/runs/obb/train3/weights/best.pt, 5.9MBValidating /home/hy/yolopan/ultralytics/runs/obb/train3/weights/best.pt...
Ultralytics 8.3.63 🚀 Python-3.9.21 torch-2.5.1+cu124 CUDA:0 (NVIDIA RTX A5000, 24248MiB)
YOLO11n-obb summary (fused): 257 layers, 2,656,648 parameters, 0 gradients, 6.6 GFLOPsClass     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 25.32it/s]all          4          8      0.933          1      0.995      0.797baseball diamond          3          4      0.879          1      0.995      0.828basketball court          1          3      0.958          1      0.995      0.866soccer ball field          1          1      0.962          1      0.995      0.697
Speed: 0.4ms preprocess, 2.4ms inference, 0.0ms loss, 1.7ms postprocess per image
Results saved to /home/hy/yolopan/ultralytics/runs/obb/train3
💡 Learn more at https://docs.ultralytics.com/modes/train

速度还行。　

整理自己的数据集，用来训练光伏的带方向框检测；

标注数据（带倾斜框，数据格式？）

用rolabelimg标注数据为xml格式，需要转换成txt格式，才能调用类似dota8的格式训练；

lableme标注的是json格式，都需要转换。

先转换xml为dota的txt格式，然后用yolo自带的脚本再转换成yolo-obb的txt格式：

import syssys.path.append('/home/code/wll/code/ultralytics-main-OBB/ultralytics')from ultralytics.data.converter import convert_dota_to_yolo_obb
convert_dota_to_yolo_obb('/home/code/wll/code/ultralytics-main-OBB/myData')

执行数据集：


>>> from ultralytics.data.converter import convert_dota_to_yolo_obb
>>> convert_dota_to_yolo_obb('../my_obb_datasets')
Processing train images: 100%|██████████| 16/16 [00:00<00:00, 102456.28it/s]
Processing val images: 100%|██████████| 6/6 [00:00<00:00, 70492.50it/s]

数据集格式必须参照：

           - DOTA├─ images│   ├─ train│   └─ val└─ labels├─ train_original└─ val_originalAfter execution, the function will organize the labels into:- DOTA└─ labels├─ train└─ val

注意converter代码必须修改两处：

一是classname的列表，另外就是文件类型；

class_mapping = {"module_front":0,"module_back":1# "plane": 0,# "ship": 1,# "storage-tank": 2,# "baseball-diamond": 3,# "tennis-court": 4,# "basketball-court": 5,# "ground-track-field": 6,# "harbor": 7,# "bridge": 8,# "large-vehicle": 9,# "small-vehicle": 10,# "helicopter": 11,# "roundabout": 12,# "soccer-ball-field": 13,# "swimming-pool": 14,# "container-crane": 15,# "airport": 16,# "helipad": 17,}def convert_label(image_name, image_width, image_height, orig_label_dir, save_dir):"""Converts a single image's DOTA annotation to YOLO OBB format and saves it to a specified directory."""orig_label_path = orig_label_dir / f"{image_name}.txt"save_path = save_dir / f"{image_name}.txt"print("debug",orig_label_dir,save_dir)with orig_label_path.open("r") as f, save_path.open("w") as g:lines = f.readlines()for line in lines:parts = line.strip().split()if len(parts) < 9:continueclass_name = parts[8]class_idx = class_mapping[class_name]coords = [float(p) for p in parts[:8]]normalized_coords = [coords[i] / image_width if i % 2 == 0 else coords[i] / image_height for i in range(8)]formatted_coords = [f"{coord:.6g}" for coord in normalized_coords]g.write(f"{class_idx} {' '.join(formatted_coords)}\n")for phase in ["train", "val"]:image_dir = dota_root_path / "images" / phaseorig_label_dir = dota_root_path / "labels" / f"{phase}_original"save_dir = dota_root_path / "labels" / phasesave_dir.mkdir(parents=True, exist_ok=True)print("debug........")image_paths = list(image_dir.iterdir())for image_path in TQDM(image_paths, desc=f"Processing {phase} images"):#  if image_path.suffix != ".jpg": # ".png":#      continue

然后就可以启动训练命令了：


(py39yolopannew) hy@hy-C621-WD12-IPMI:~/yolopan/ultralytics$ yolo obb train data=my_obb.yaml mo          del=yolo11n-obb.pt epochs=10
Ultralytics 8.3.59 🚀 Python-3.9.21 torch-2.5.1+cu124 CUDA:0 (NVIDIA RTX A5000, 24248MiB)
engine/trainer: task=obb, mode=train, model=yolo11n-obb.pt, data=my_obb.yaml, epochs=10, time=N          one, patience=100, batch=16, imgsz=1024, save=True, save_period=-1, cache=False, device=None, w          orkers=8, project=None, name=train6, exist_ok=False, pretrained=True, optimizer=auto, verbose=T          rue, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, r          esume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mas          k=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, con          f=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, str          eam_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_mask          s=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=          False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript,           keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=No          ne, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warm          up_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hs          v_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=          0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=fl          ip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, s          ave_dir=/home/hy/yolopan/ultralytics/runs/obb/train6
Overriding model.yaml nc=80 with nc=2from  n    params  module                                       arguments             0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2          ]1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3,           2]2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1,           False, 0.25]3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3,           2]4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1,           False, 0.25]5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3          , 2]6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1          , True]7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3          , 2]8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1          , True]9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5          ]10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1          ]11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'n          earest']12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                   13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1          , False]14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'n          earest']15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                   16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1,           False]17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3,           2]18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                   19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1          , False]20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3          , 2]21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                   22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1          , True]23        [16, 19, 22]  1    502729  ultralytics.nn.modules.head.OBB              [2, 1, [64,           128, 256]]
YOLO11n-obb summary: 344 layers, 2,661,897 parameters, 2,661,881 gradients, 6.7 GFLOPsTransferred 535/541 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Scanning /home/hy/yolopan/my_obb_datasets/labels/train... 16 images, 0 backgrounds, 0 co
train: New cache created: /home/hy/yolopan/my_obb_datasets/labels/train.cache
val: Scanning /home/hy/yolopan/my_obb_datasets/labels/val... 6 images, 0 backgrounds, 0 corrupt
val: New cache created: /home/hy/yolopan/my_obb_datasets/labels/val.cache
Plotting labels to /home/hy/yolopan/ultralytics/runs/obb/train6/labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining bes          t 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 87 weight(decay=0.0), 97 weig          ht(decay=0.0005), 96 bias(decay=0.0)
Image sizes 1024 train, 1024 val
Using 8 dataloader workers
Logging results to /home/hy/yolopan/ultralytics/runs/obb/train6
Starting training for 10 epochs...
Closing dataloader mosaicEpoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size1/10       5.8G      2.857      5.118      3.315        123       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00452       0.11    0.00731     0.0021Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size2/10      6.03G      2.661      4.933      3.164        120       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00454       0.11    0.00733     0.0021Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size3/10      6.24G      2.809      5.047      3.239        119       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00492      0.131    0.00655    0.00175Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size4/10      6.24G      2.799      5.143      3.208        123       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00533      0.153    0.00634    0.00129Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size5/10      6.04G      2.733      5.221      3.103        125       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00532      0.153    0.00671    0.00132Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size6/10      5.84G      2.894      5.016      3.415        125       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00577      0.175    0.00682    0.00158Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size7/10      5.84G      2.648      4.816      3.272        123       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00616      0.164    0.00687    0.00143Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size8/10      5.86G      2.949      5.435      3.323        125       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00673      0.186     0.0074    0.00151Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size9/10      5.87G      2.785      4.857      3.223        125       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00586      0.175    0.00542    0.00123Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size10/10      5.87G      2.668      4.629      3.351        125       1024: 100%|██████████|Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00584      0.175    0.00537     0.001210 epochs completed in 0.004 hours.
Optimizer stripped from /home/hy/yolopan/ultralytics/runs/obb/train6/weights/last.pt, 5.8MB
Optimizer stripped from /home/hy/yolopan/ultralytics/runs/obb/train6/weights/best.pt, 5.8MBValidating /home/hy/yolopan/ultralytics/runs/obb/train6/weights/best.pt...
Ultralytics 8.3.59 🚀 Python-3.9.21 torch-2.5.1+cu124 CUDA:0 (NVIDIA RTX A5000, 24248MiB)
YOLO11n-obb summary (fused): 257 layers, 2,654,113 parameters, 0 gradients, 6.6 GFLOPsClass     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|all          6         68    0.00455       0.11    0.00698    0.00199module_front          5         45    0.00659     0.0889     0.0125    0.00352module_back          2         23    0.00251       0.13    0.00148   0.000463
Speed: 0.2ms preprocess, 2.3ms inference, 0.0ms loss, 35.4ms postprocess per image
Results saved to /home/hy/yolopan/ultralytics/runs/obb/train6

用训练出来的模型验证测试数据集：

(py39yolopannew) hy@hy-C621-WD12-IPMI:~/yolopan/ultralytics$ yolo obb predict model=/home/hy/yolopan/ultralytics/runs/obb/train6/weights/best.pt source='../my_obb_datasets/test/cam2_flv_2.jpg'                  Ultralytics 8.3.59 🚀 Python-3.9.21 torch-2.5.1+cu124 CUDA:0 (NVIDIA RTX A5000, 24248MiB)
YOLO11n-obb summary (fused): 257 layers, 2,654,113 parameters, 0 gradients, 6.6 GFLOPsimage 1/1 /home/hy/yolopan/ultralytics/../my_obb_datasets/test/cam2_flv_2.jpg: 576x1024 (no detections), 97.4ms
Speed: 8.0ms preprocess, 97.4ms inference, 35.4ms postprocess per image at shape (1, 3, 576, 1024)
Results saved to /home/hy/yolopan/ultralytics/runs/obb/predict3
💡 Learn more at https://docs.ultralytics.com/modes/predict

检查下结果：训练10次的模型明显不能用；

增加训练次数到1000次；

中间检测到没有优化空间了就会自动停止：