YOLOv8 是 YOLO 系列实时目标检测器中的较新迭代版本，在准确性和速度方面提供了前沿性能。基于之前 YOLO 版本的进步，YOLOv8 引入了新的特性和优化，使其成为各种应用中各种目标检测任务的理想选择。
一、安装显卡驱动与CUDA：
这个系统已经安装好了显卡驱动与CUDA 11.8。
查看一下CUDA 的版本：
ai@jupyter:~$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0
二、安装 YOLOv8:
ai@jupyter:~$ git clone https://github.com/ultralytics/ultralytics.git
Cloning into 'ultralytics'...
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remote: Counting objects: 100% (1546/1546), done.
remote: Compressing objects: 100% (882/882), done.
remote: Total 40899 (delta 1026), reused 1058 (delta 659), pack-reused 39353 (from 1)
Receiving objects: 100% (40899/40899), 31.48 MiB | 12.85 MiB/s, done.
Resolving deltas: 100% (30115/30115), done.
ai@jupyter:~$ pip install ultralytics
ai@jupyter:~$ pip install ultralytics
Looking in indexes: https://mirror.baidu.com/pypi/simple/, https://mirrors.aliyun.com/pypi/simple/
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Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics)Downloading https://mirrors.aliyun.com/pypi/packages/a8/48/a9775d377cb95585fb188b469387f58ba6738e268de22eae2ad4cedb2c41/nvidia_nvjitlink_cu12-12.6.68-py3-none-manylinux2014_x86_64.whl (19.7 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 19.7/19.7 MB 16.6 MB/s eta 0:00:00
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Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)
Requirement already satisfied: mpmath>=0.19 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)
Installing collected packages: py-cpuinfo, triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, nvidia-cusparse-cu12, nvidia-cudnn-cu12, seaborn, nvidia-cusolver-cu12, torch, ultralytics-thop, torchvision, ultralytics
Successfully installed networkx-3.3 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.68 nvidia-nvtx-cu12-12.1.105 py-cpuinfo-9.0.0 seaborn-0.13.2 torch-2.4.1 torchvision-0.19.1 triton-3.0.0 ultralytics-8.2.92 ultralytics-thop-2.0.6
WARNING: Skipping page https://mirror.baidu.com/pypi/simple/pip/ because the GET request got Content-Type: application/octet-stream. The only supported Content-Types are application/vnd.pypi.simple.v1+json, application/vnd.pypi.simple.v1+html, and text/html
查看一下 PyTorch 的版本：
ai@jupyter:~$ pip show torch
Name: torch
Version: 2.4.1
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Home-page: https://pytorch.org/
Author: PyTorch Team
Author-email: packages@pytorch.org
License: BSD-3
Location: /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages
Requires: filelock, fsspec, jinja2, networkx, nvidia-cublas-cu12, nvidia-cuda-cupti-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-runtime-cu12, nvidia-cudnn-cu12, nvidia-cufft-cu12, nvidia-curand-cu12, nvidia-cusolver-cu12, nvidia-cusparse-cu12, nvidia-nccl-cu12, nvidia-nvtx-cu12, sympy, triton, typing-extensions
Required-by: torchvision, ultralytics, ultralytics-thop
用 Python 查看一下 PyTorch 的版本：
ai@jupyter:~$ python
Python 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.__version__)
2.4.1+cu121
>>> exit()
ai@jupyter:~$ yolo cfg
Printing '/opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages/ultralytics/cfg/default.yaml'
task: detect
mode: train
model: null
data: null
epochs: 100
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: null
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: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
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: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: false
opset: null
workspace: 4
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
label_smoothing: 0.0
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
auto_augment: randaugment
erasing: 0.4
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml三、训练模型：
以 coco128 为例：（自动下载数据集及模型，需要联网）
ai@jupyter:~$ yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...
如果本地没有 yolov8n.pt 文件，会直接从 github 上下载，长时间如果下载不下来，可以先手动下载到本地。
ai@jupyter:~$ yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
Ultralytics YOLOv8.2.92 🚀 Python-3.10.10 torch-2.4.1+cu121 CUDA:0 (Tesla V100-SXM2-32GB, 16384MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=100, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, 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=False, opset=None, workspace=4, 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, label_smoothing=0.0, 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, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/trainDataset 'coco128.yaml' images not found ⚠️, missing path '/home/aistudio/datasets/coco128/images/train2017'
Downloading https://ultralytics.com/assets/coco128.zip to '/home/aistudio/datasets/coco128.zip'...2%|██▍ | 128k/6.66M [00:30<11:33, 9.88kB/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.66M/6.66M [01:09<00:00, 100kB/s]
Unzipping /home/aistudio/datasets/coco128.zip to /home/aistudio/datasets/coco128...: 100%|██████████| 263/263 [00:00<00:00, 2433.26file/s]
Dataset download success ✅ (75.3s), saved to /home/aistudio/datasetsDownloading https://ultralytics.com/assets/Arial.ttf to '/home/aistudio/.config/Ultralytics/Arial.ttf'...0%| | 0.00/755k [00:00<?, ?B/s]68%|████████████████████████████████████████████████████████████████████████████████████████▊ | 512k/755k [01:03<00:29, 8.44kB/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 755k/755k [01:35<00:00, 8.11kB/s]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 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 897664 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]]
Model summary: 225 layers, 3,157,200 parameters, 3,157,184 gradients, 8.9 GFLOPsTransferred 355/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/aistudio/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128 [00:00<00:00, 491.28it/s]
train: New cache created: /home/aistudio/datasets/coco128/labels/train2017.cache
val: Scanning /home/aistudio/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128 [00:00<?, ?it/s]
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=0.000119, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/train
Starting training for 100 epochs...Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size1/100 2.69G 1.214 1.669 1.27 217 640: 100%|██████████| 8/8 [00:04<00:00, 1.60it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:03<00:00, 1.16it/s]all 128 929 0.65 0.516 0.611 0.453Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size2/100 2.56G 1.2 1.434 1.229 218 640: 100%|██████████| 8/8 [00:03<00:00, 2.55it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:01<00:00, 2.35it/s]all 128 929 0.656 0.545 0.624 0.466Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size3/100 2.89G 1.155 1.372 1.251 215 640: 100%|██████████| 8/8 [00:02<00:00, 2.94it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:01<00:00, 2.21it/s]all 128 929 0.679 0.546 0.635 0.47297/100 2.49G 0.8143 0.6814 0.997 97 640: 100%|██████████| 8/8 [00:02<00:00, 3.33it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:01<00:00, 2.57it/s]all 128 929 0.889 0.809 0.874 0.724Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size98/100 2.56G 0.808 0.6858 0.9922 117 640: 100%|██████████| 8/8 [00:02<00:00, 3.14it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:01<00:00, 2.29it/s]all 128 929 0.888 0.807 0.874 0.724Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size99/100 2.55G 0.8293 0.6906 0.9987 72 640: 100%|██████████| 8/8 [00:02<00:00, 3.10it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:01<00:00, 2.18it/s]all 128 929 0.892 0.804 0.872 0.723Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size100/100 2.49G 0.8595 0.7396 1.023 75 640: 100%|██████████| 8/8 [00:02<00:00, 3.15it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:01<00:00, 2.30it/s]all 128 929 0.89 0.805 0.871 0.723100 epochs completed in 0.153 hours.
Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB
Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MBValidating runs/detect/train/weights/best.pt...
Ultralytics YOLOv8.2.92 🚀 Python-3.10.10 torch-2.4.1+cu121 CUDA:0 (Tesla V100-SXM2-32GB, 16384MiB)
Model summary (fused): 168 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPsClass Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:04<00:00, 1.02s/it]all 128 929 0.927 0.802 0.884 0.743person 61 254 0.983 0.696 0.881 0.702bicycle 3 6 0.869 0.5 0.616 0.476car 12 46 0.928 0.281 0.597 0.337motorcycle 4 5 0.939 1 0.995 0.964airplane 5 6 0.953 1 0.995 0.964bus 5 7 1 0.773 0.978 0.845train 3 3 0.936 1 0.995 0.764truck 5 12 0.937 0.5 0.62 0.488boat 2 6 0.758 0.526 0.789 0.627traffic light 4 14 1 0.298 0.433 0.273stop sign 2 2 0.89 1 0.995 0.895bench 5 9 1 0.828 0.975 0.799bird 2 16 1 0.962 0.995 0.808cat 4 4 0.928 1 0.995 0.95dog 9 9 0.993 1 0.995 0.884horse 1 2 0.892 1 0.995 0.897elephant 4 17 1 0.93 0.985 0.877bear 1 1 0.813 1 0.995 0.995zebra 2 4 0.931 1 0.995 0.995giraffe 4 9 0.999 1 0.995 0.913backpack 4 6 0.952 0.667 0.834 0.691umbrella 4 18 0.945 0.96 0.992 0.825handbag 9 19 1 0.494 0.757 0.506tie 6 7 0.938 0.857 0.861 0.762suitcase 2 4 1 0.935 0.995 0.809frisbee 5 5 0.905 0.8 0.802 0.742skis 1 1 0.865 1 0.995 0.895snowboard 2 7 0.854 0.837 0.944 0.752sports ball 6 6 1 0.555 0.673 0.397kite 2 10 1 0.275 0.802 0.341baseball bat 4 4 0.734 0.5 0.825 0.476baseball glove 4 7 0.986 0.429 0.439 0.381skateboard 3 5 1 0.798 0.84 0.648tennis racket 5 7 1 0.66 0.718 0.498bottle 6 18 1 0.477 0.835 0.577wine glass 5 16 0.902 0.438 0.806 0.568cup 10 36 0.992 0.667 0.898 0.674fork 6 6 0.94 0.833 0.872 0.704knife 7 16 0.85 0.625 0.824 0.549spoon 5 22 0.939 0.698 0.775 0.603bowl 9 28 0.959 0.826 0.9 0.762banana 1 1 0.792 1 0.995 0.995sandwich 2 2 0.876 1 0.995 0.995orange 1 4 0.789 1 0.995 0.798broccoli 4 11 0.834 0.364 0.544 0.412carrot 3 24 0.955 0.876 0.965 0.702hot dog 1 2 0.871 1 0.995 0.995pizza 5 5 0.942 1 0.995 0.958donut 2 14 0.916 1 0.995 0.938cake 4 4 0.927 1 0.995 0.96chair 9 35 0.96 0.685 0.892 0.683couch 5 6 1 0.884 0.995 0.828potted plant 9 14 0.935 0.857 0.978 0.852bed 3 3 0.912 1 0.995 0.995dining table 10 13 0.986 1 0.995 0.879toilet 2 2 0.869 1 0.995 0.848tv 2 2 0.88 1 0.995 0.946laptop 2 3 0.912 1 0.995 0.908mouse 2 2 0.759 0.5 0.504 0.403remote 5 8 0.954 0.75 0.751 0.656cell phone 5 8 1 0.58 0.653 0.442microwave 3 3 0.971 1 0.995 0.901oven 5 5 0.755 0.618 0.722 0.588sink 4 6 0.977 1 0.995 0.788refrigerator 5 5 0.93 1 0.995 0.977book 6 29 0.967 0.448 0.709 0.509clock 8 9 0.958 0.889 0.961 0.856vase 2 2 0.86 1 0.995 0.895scissors 1 1 0.805 1 0.995 0.697teddy bear 6 21 1 0.851 0.958 0.797toothbrush 2 5 0.99 1 0.995 0.929
Speed: 0.2ms preprocess, 3.1ms inference, 0.0ms loss, 5.2ms postprocess per image
Results saved to runs/detect/train
💡 Learn more at https://docs.ultralytics.com/modes/train训练完的最好模型为：/home/aistudio/runs/detect/train/weights/best.pt
训练过程保存为：runs/detect/train/results.png，如下图：
四、推理验证：
- 使用经典的图片验证：
直接使用网上的图片作为测试：source='https://ultralytics.com/images/bus.jpg'，或者先下载到本地。
ai@jupyter:~$ yolo detect predict model=runs/detect/train/weights/best.pt source=bus.jpg
Ultralytics YOLOv8.2.92 🚀 Python-3.10.10 torch-2.4.1+cu121 CUDA:0 (Tesla V100-SXM2-32GB, 16384MiB)
Model summary (fused): 168 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPs
image 1/1 /home/aistudio/bus.jpg: 640x480 3 persons, 1 bus, 1 stop sign, 39.6ms
Speed: 7.0ms preprocess, 39.6ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 480)
Results saved to runs/detect/predict
💡 Learn more at https://docs.ultralytics.com/modes/predict
- 换一张图片测试：
ai@jupyter:~$ yolo detect predict model=runs/detect/train/weights/best.pt source=030.jpg
Ultralytics YOLOv8.2.92 🚀 Python-3.10.10 torch-2.4.1+cu121 CUDA:0 (Tesla V100-SXM2-32GB, 16384MiB)
Model summary (fused): 168 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPs
image 1/1 /home/aistudio/030.jpg: 448x640 1 potted plant, 1 vase, 139.9ms
Speed: 7.6ms preprocess, 139.9ms inference, 2.5ms postprocess per image at shape (1, 3, 448, 640)
Results saved to runs/detect/predict2
💡 Learn more at https://docs.ultralytics.com/modes/predict
五、导出（Export）
将 训练好的模型导出为不同的格式，如 ONNX、CoreML 等。
yolo export model=runs/detect/train/weights/best.pt format=onnx
小结：
YOLOv8 在多个领域中表现出色，特别是在需要高精度和高速度的实时检测任务中，如无人驾驶、智能监控和工业检测等。这些应用场景对目标检测的实时性和准确性有着极高的要求，而 YOLOv8 正是满足这些需求的理想选择。
综上所述，YOLOv8 通过模型结构改进、数据增强和训练策略优化、多尺度检测和轻量化以及提供多种模型变体等新特性和优化措施，进一步提升了目标检测的精度和速度，并扩展了其应用场景。
老徐，仲秋，2024/9/17
