华为云开发者空间☁️昇腾NPU实现AI工业质检【玩转华为云】
【摘要】 本案例借助华为云开发者空间💡昇腾910B NPU完成YOLO11模型训练与PCB缺陷检测，并且结合SAHI实现高效切片推理🚀。
华为云开发者空间☁️昇腾NPU实现AI工业质检
本案例将在华为云开发者空间工作台⚙️AI Notebook 中使用免费的🎉 昇腾 NPU 910B ✨完成YOLO11模型训练，利用SAHI切片辅助超推理框架实现PCB缺陷检测。
1. 下载模型和数据集📦
首先在Notebook的代码块中粘贴并运行下面的代码，下载解压本案例所需的训练数据和模型文件：
import os
import zipfile
if not os.path.exists('yolo11_train_ascend.zip'):
os.system('wget -q https://orangepi-ascend.obs.cn-north-4.myhuaweicloud.com/yolo11_train_ascend.zip')
if not os.path.exists('yolo11_train_ascend'):
zip_file = zipfile.ZipFile('yolo11_train_ascend.zip')
zip_file.extractall()
zip_file.close()
2. 安装依赖包🛠️
安装YOLO11所需的依赖包以及SAHI库，构建项目的运行环境：
!pip install ultralytics==8.3.160 ultralytics-thop==2.0.14 sahi==0.11.26 numpy==1.26.4
Defaulting to user installation because normal site-packages is not writeable
Looking in indexes: https://mirrors.huaweicloud.com/repository/pypi/simple
Collecting ultralytics==8.3.160
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/7b/8d/924524ff26c0ed0ba43b90cc598887e2b06f3bf00dd51a505a754ecb138d/ultralytics-8.3.160-py3-none-any.whl (1.0 MB)
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Collecting ultralytics-thop==2.0.14
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/a6/10/251f036b4c5d77249f9a119cc89dafe8745dc1ad1f1a5f06b6a3988ca454/ultralytics_thop-2.0.14-py3-none-any.whl (26 kB)
Collecting sahi==0.11.26
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/5e/8a/9782c8088af52e6f41fee59c77b5117783c0d6eafde45c96ca3912ec197f/sahi-0.11.26-py3-none-any.whl (115 kB)
Collecting numpy==1.26.4
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/fc/a5/4beee6488160798683eed5bdb7eead455892c3b4e1f78d79d8d3f3b084ac/numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (14.2 MB)
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Requirement already satisfied: click in /usr/local/python3.10/lib/python3.10/site-packages (from sahi==0.11.26) (8.1.8)
Collecting fire (from sahi==0.11.26)
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/6b/b6/82c7e601d6d3c3278c40b7bd35e17e82aa227f050aa9f66cb7b7fce29471/fire-0.7.0.tar.gz (87 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Collecting pybboxes==0.1.6 (from sahi==0.11.26)
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/3c/3f/46f6613b41a3c2b4f7af3b526035771ca5bb12d6fdf3b23145899f785e36/pybboxes-0.1.6-py3-none-any.whl (24 kB)
Collecting shapely>=2.0.0 (from sahi==0.11.26)
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/29/51/0b158a261df94e33505eadfe737db9531f346dfa60850945ad25fd4162f1/shapely-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.9 MB)
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Collecting terminaltables (from sahi==0.11.26)
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/c4/fb/ea621e0a19733e01fe4005d46087d383693c0f4a8f824b47d8d4122c87e0/terminaltables-3.1.10-py2.py3-none-any.whl (15 kB)
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Collecting termcolor (from fire->sahi==0.11.26)
Downloading https://mirrors.huaweicloud.com/repository/pypi/packages/4f/bd/de8d508070629b6d84a30d01d57e4a65c69aa7f5abe7560b8fad3b50ea59/termcolor-3.1.0-py3-none-any.whl (7.7 kB)
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Building wheels for collected packages: fire
Building wheel for fire (pyproject.toml) ... done
Created wheel for fire: filename=fire-0.7.0-py3-none-any.whl size=114330 sha256=a1f27d511635da524f8f51fa2d35ae22862e400cec55285acbc05ced6ef91371
Stored in directory: /home/service/.cache/pip/wheels/9b/dc/c7/06491fe82713723ab64494dbcfd521bdbe80cf26b5fcb5f564
Successfully built fire
Installing collected packages: terminaltables, termcolor, numpy, shapely, pybboxes, fire, ultralytics-thop, sahi, ultralytics
Attempting uninstall: numpy
Found existing installation: numpy 1.24.4
Uninstalling numpy-1.24.4:
Successfully uninstalled numpy-1.24.4
WARNING: The script f2py is installed in '/home/service/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
WARNING: The script sahi is installed in '/home/service/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
WARNING: The scripts ultralytics and yolo are installed in '/home/service/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
gradio 5.9.1 requires markupsafe~=2.0, but you have markupsafe 3.0.2 which is incompatible.
openmind 0.9.1 requires datasets<=2.21.0,>=2.18.0, but you have datasets 3.2.0 which is incompatible.
openmind 0.9.1 requires openmind-hub==0.9.0, but you have openmind-hub 0.9.1 which is incompatible.
openmind-datasets 0.7.1 requires datasets==2.18.0, but you have datasets 3.2.0 which is incompatible.
openmind-evaluate 0.7.0 requires datasets==2.18.0, but you have datasets 3.2.0 which is incompatible.
Successfully installed fire-0.7.0 numpy-1.26.4 pybboxes-0.1.6 sahi-0.11.26 shapely-2.1.1 termcolor-3.1.0 terminaltables-3.1.10 ultralytics-8.3.160 ultralytics-thop-2.0.14
[notice] A new release of pip is available: 24.3.1 -> 25.1.1
[notice] To update, run: pip install --upgrade pip
3. 修改配置文件📝
我们在配置文件中指定数据集路径和类别等信息，用于后续模型的训练：
%%writefile yolo11_train_ascend/pcb.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: /opt/huawei/edu-apaas/src/init/yolo11_train_ascend/pcb_sliced # dataset root dir (absolute path)
train: images/train # train images (relative to 'path')
val: images/val # val images (relative to 'path')
test: # test images (optional)
# Classes，类别
names:
0: mouse_bite
1: open_circuit
2: short
3: spur
4: spurious_copper
Writing yolo11_train_ascend/pcb.yaml
4. 下载 Arial.ttf 字体🖋️
为了避免影响训练进展，可以先提前下载字体文件并拷贝到 /home/service/.config/Ultralytics 路径下。
!wget https://orangepi-ascend.obs.cn-north-4.myhuaweicloud.com/Arial.ttf
!mkdir -p /home/service/.config/Ultralytics
!cp Arial.ttf /home/service/.config/Ultralytics/Arial.ttf
--2025-06-28 05:55:59-- https://pcb-sahi-public.obs.cn-southwest-2.myhuaweicloud.com/Arial.ttf
Resolving pcb-sahi-public.obs.cn-southwest-2.myhuaweicloud.com (pcb-sahi-public.obs.cn-southwest-2.myhuaweicloud.com)... 100.125.6.3, 100.125.7.3, 100.125.6.131
Connecting to pcb-sahi-public.obs.cn-southwest-2.myhuaweicloud.com (pcb-sahi-public.obs.cn-southwest-2.myhuaweicloud.com)|100.125.6.3|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 773236 (755K) [application/x-font-ttf]
Saving to: 'Arial.ttf'
Arial.ttf 100%[===================>] 755.11K --.-KB/s in 0.004s
2025-06-28 05:55:59 (188 MB/s) - 'Arial.ttf' saved [773236/773236]
5. 模型训练🧠🔥
我们使用yolo11n.pt预训练模型，利用昇腾NPU进行模型加速，设置模型的训练次数为10轮、图像的大小为640x640、开启8个数据加载线程每次送入模型32张图像进行迭代优化。
%cd yolo11_train_ascend
import torch
import torch_npu
from torch_npu.contrib import transfer_to_npu
from ultralytics import YOLO
# Load a model
model = YOLO('yolo11n.pt') # load a pretrained model (recommended for training)
# Train the model
results = model.train(data='pcb.yaml', epochs=10, imgsz=640, workers=8, batch=32)
%cd ..
/home/service/.local/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.
self.shell.db['dhist'] = compress_dhist(dhist)[-100:]
/opt/huawei/edu-apaas/src/init/yolo11_train_ascend
/home/service/.local/lib/python3.10/site-packages/torch_npu/utils/path_manager.py:82: UserWarning: Warning: The /usr/local/Ascend/ascend-toolkit/latest owner does not match the current user.
warnings.warn(f"Warning: The {path} owner does not match the current user.")
/home/service/.local/lib/python3.10/site-packages/torch_npu/utils/path_manager.py:82: UserWarning: Warning: The /usr/local/Ascend/ascend-toolkit/8.0.RC3/aarch64-linux/ascend_toolkit_install.info owner does not match the current user.
warnings.warn(f"Warning: The {path} owner does not match the current user.")
/home/service/.local/lib/python3.10/site-packages/torch_npu/contrib/transfer_to_npu.py:301: ImportWarning:
*************************************************************************************************************
The torch.Tensor.cuda and torch.nn.Module.cuda are replaced with torch.Tensor.npu and torch.nn.Module.npu now..
The torch.cuda.DoubleTensor is replaced with torch.npu.FloatTensor cause the double type is not supported now..
The backend in torch.distributed.init_process_group set to hccl now..
The torch.cuda.* and torch.cuda.amp.* are replaced with torch.npu.* and torch.npu.amp.* now..
The device parameters have been replaced with npu in the function below:
torch.logspace, torch.randint, torch.hann_window, torch.rand, torch.full_like, torch.ones_like, torch.rand_like, torch.randperm, torch.arange, torch.frombuffer, torch.normal, torch._empty_per_channel_affine_quantized, torch.empty_strided, torch.empty_like, torch.scalar_tensor, torch.tril_indices, torch.bartlett_window, torch.ones, torch.sparse_coo_tensor, torch.randn, torch.kaiser_window, torch.tensor, torch.triu_indices, torch.as_tensor, torch.zeros, torch.randint_like, torch.full, torch.eye, torch._sparse_csr_tensor_unsafe, torch.empty, torch._sparse_coo_tensor_unsafe, torch.blackman_window, torch.zeros_like, torch.range, torch.sparse_csr_tensor, torch.randn_like, torch.from_file, torch._cudnn_init_dropout_state, torch._empty_affine_quantized, torch.linspace, torch.hamming_window, torch.empty_quantized, torch._pin_memory, torch.autocast, torch.load, torch.Generator, torch.Tensor.new_empty, torch.Tensor.new_empty_strided, torch.Tensor.new_full, torch.Tensor.new_ones, torch.Tensor.new_tensor, torch.Tensor.new_zeros, torch.Tensor.to, torch.nn.Module.to, torch.nn.Module.to_empty
*************************************************************************************************************
warnings.warn(msg, ImportWarning)
/home/service/.local/lib/python3.10/site-packages/torch_npu/contrib/transfer_to_npu.py:260: RuntimeWarning: torch.jit.script and torch.jit.script_method will be disabled by transfer_to_npu, which currently does not support them, if you need to enable them, please do not use transfer_to_npu.
warnings.warn(msg, RuntimeWarning)
Creating new Ultralytics Settings v0.0.6 file ✅
View Ultralytics Settings with 'yolo settings' or at '/home/service/.config/Ultralytics/settings.json'
Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.
[W compiler_depend.ts:623] Warning: expandable_segments currently defaults to false. You can enable this feature by `export PYTORCH_NPU_ALLOC_CONF = expandable_segments:True`. (function operator())
Ultralytics 8.3.160 🚀 Python-3.10.15 torch-2.1.0 CUDA:0 (Ascend910B3, 62432MiB)
engine/trainer: agnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=32, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=pcb.yaml, degrees=0.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=10, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolo11n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None
Overriding model.yaml nc=80 with nc=5
from 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, '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 431647 ultralytics.nn.modules.head.Detect [5, [64, 128, 256]]
/home/service/.local/lib/python3.10/site-packages/torch_npu/utils/storage.py:38: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
if self.device.type != 'cpu':
YOLO11n summary: 181 layers, 2,590,815 parameters, 2,590,799 gradients, 6.4 GFLOPs
Transferred 448/499 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
[W compiler_depend.ts:51] Warning: CAUTION: The operator 'torchvision::nms' is not currently supported on the NPU backend and will fall back to run on the CPU. This may have performance implications. (function npu_cpu_fallback)
AMP: checks passed ✅
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 620.7±42.3 MB/s, size: 454.2 KB)
train: Scanning /opt/huawei/edu-apaas/src/init/yolo11_train_ascend/pcb_sliced/labels/train... 4646 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4646/4646 [00:05<00:00, 848.20it/s]
train: New cache created: /opt/huawei/edu-apaas/src/init/yolo11_train_ascend/pcb_sliced/labels/train.cache
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 471.4±135.5 MB/s, size: 448.2 KB)
val: Scanning /opt/huawei/edu-apaas/src/init/yolo11_train_ascend/pcb_sliced/labels/val... 422 images, 0 backgrounds, 0 corrupt: 100%|██████████| 422/422 [00:00<00:00, 520.44it/s]
val: New cache created: /opt/huawei/edu-apaas/src/init/yolo11_train_ascend/pcb_sliced/labels/val.cache
Plotting labels to runs/detect/train/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=0.001111, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/train
Starting training for 10 epochs...
Closing dataloader mosaic
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/146 [00:00<?, ?it/s]
.
/home/service/.local/lib/python3.10/site-packages/ultralytics/utils/tal.py:274: UserWarning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (Triggered internally at build/CMakeFiles/torch_npu.dir/compiler_depend.ts:74.)
target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)
[W compiler_depend.ts:103] Warning: Non finite check and unscale on NPU device! (function operator())
1/10 7.77G 2.238 5.333 1.761 8 640: 100%|██████████| 146/146 [01:31<00:00, 1.60it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 0%| | 0/7 [00:00<?, ?it/s]
.....
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:58<00:00, 8.39s/it]
all 422 604 0.39 0.0656 0.0888 0.023
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 8.2G 1.876 2.724 1.462 8 640: 100%|██████████| 146/146 [01:16<00:00, 1.92it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:04<00:00, 1.58it/s]
all 422 604 0.451 0.238 0.214 0.0639
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 8.2G 1.825 1.912 1.445 8 640: 100%|██████████| 146/146 [01:12<00:00, 2.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:04<00:00, 1.64it/s]
all 422 604 0.339 0.291 0.244 0.0742
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 8.2G 1.748 1.571 1.398 4 640: 100%|██████████| 146/146 [01:12<00:00, 2.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:04<00:00, 1.58it/s]
all 422 604 0.409 0.361 0.335 0.117
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 8.2G 1.703 1.343 1.372 6 640: 100%|██████████| 146/146 [01:11<00:00, 2.05it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:04<00:00, 1.66it/s]
all 422 604 0.442 0.34 0.321 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 8.2G 1.673 1.26 1.343 5 640: 100%|██████████| 146/146 [01:11<00:00, 2.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:04<00:00, 1.62it/s]
all 422 604 0.605 0.49 0.53 0.224
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 8.2G 1.614 1.145 1.316 6 640: 100%|██████████| 146/146 [01:12<00:00, 2.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:04<00:00, 1.58it/s]
all 422 604 0.595 0.542 0.525 0.206
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 8.2G 1.578 1.067 1.294 7 640: 100%|██████████| 146/146 [01:11<00:00, 2.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:04<00:00, 1.73it/s]
all 422 604 0.754 0.629 0.685 0.307
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 8.21G 1.551 1.009 1.275 8 640: 100%|██████████| 146/146 [01:11<00:00, 2.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:04<00:00, 1.57it/s]
all 422 604 0.782 0.618 0.703 0.315
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 8.21G 1.5 0.9621 1.255 6 640: 100%|██████████| 146/146 [01:12<00:00, 2.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:03<00:00, 1.83it/s]
all 422 604 0.8 0.661 0.732 0.354
10 epochs completed in 0.236 hours.
Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB
Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB
Validating runs/detect/train/weights/best.pt...
Ultralytics 8.3.160 🚀 Python-3.10.15 torch-2.1.0 CUDA:0 (Ascend910B3, 62432MiB)
YOLO11n summary (fused): 100 layers, 2,583,127 parameters, 0 gradients, 6.3 GFLOPs
...
Class Images Instances Box(P R mAP50 mAP50-95): 0%| | 0/7 [00:00<?, ?it/s]
.
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:07<00:00, 1.14s/it]
all 422 604 0.799 0.663 0.732 0.355
mouse_bite 107 169 0.806 0.785 0.829 0.4
open_circuit 73 101 0.656 0.471 0.492 0.219
short 69 87 0.889 0.54 0.701 0.314
spur 95 134 0.864 0.714 0.76 0.342
spurious_copper 95 113 0.782 0.805 0.88 0.5
Speed: 0.1ms preprocess, 8.3ms inference, 0.0ms loss, 2.5ms postprocess per image
Results saved to runs/detect/train
/opt/huawei/edu-apaas/src/init
/home/service/.local/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.
self.shell.db['dhist'] = compress_dhist(dhist)[-100:]
模型训练好后，可以在runs/detect/train目录下查看训练结果，例如损失函数的变化曲线、mAP等评价指标📈💪。
6. 图像切分检测✂️🔍
最后我们利用SAHI框架对高清PCB图像进行切片推理，从而更精准地检测出PCB的瑕疵类别。
import torch
import torch_npu
from torch_npu.contrib import transfer_to_npu
from sahi.predict import get_sliced_prediction
from sahi import AutoDetectionModel
from PIL import Image
detection_model = AutoDetectionModel.from_pretrained(
model_type = 'ultralytics',
model_path = "yolo11_train_ascend/runs/detect/train/weights/best.pt",
confidence_threshold = 0.4,
device = "cuda:0"
)
这里我们使用滑窗检测🔍的技术，将原始图像切分成640x640大小的子图🖼️，同时设置一定的重叠度，再分别预测每张子图，最后将所有的检测结果进行合并处理🛠️。
image_path = "https://orangepi-ascend.obs.cn-north-4.myhuaweicloud.com/001.bmp"
result = get_sliced_prediction(
image_path,
detection_model,
slice_height = 640,
slice_width = 640,
overlap_height_ratio = 0.1,
overlap_width_ratio = 0.1,
perform_standard_pred = False,
postprocess_class_agnostic = True,
postprocess_match_threshold = 0.1,
)
result.export_visuals(export_dir="output/", file_name="sliced_result")
Image.open("output/sliced_result.png")
Performing prediction on 24 slices.
可以看到，模型准确无误的预测出PCB缺陷的位置、类别和置信度😄
7. 小结📌
本案例借助华为云开发者空间💡昇腾910B NPU完成YOLO11模型训练与PCB缺陷检测，并且结合SAHI实现高效切片推理🚀，华为云开发者空间💻AI Notebook开箱即用，大家快来体验吧！🤗
【声明】本内容来自华为云开发者社区博主，不代表华为云及华为云开发者社区的观点和立场。转载时必须标注文章的来源（华为云社区）、文章链接、文章作者等基本信息，否则作者和本社区有权追究责任。如果您发现本社区中有涉嫌抄袭的内容，欢迎发送邮件进行举报，并提供相关证据，一经查实，本社区将立刻删除涉嫌侵权内容，举报邮箱：
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