【深度学习】YOLOv8训练过程，YOLOv8实战教程，目标检测任务SOTA，关键点回归-Toy模板网

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可用资源

https://github.com/ultralytics/ultralytics
【深度学习】YOLOv8训练过程，YOLOv8实战教程，目标检测任务SOTA，关键点回归
官方教程：https://docs.ultralytics.com/modes/train/

资源安装

更建议下载代码后使用下面指令安装，这样可以更改源码，如果不需要更改源码就直接pip install ultralytics也是可以的。

pip install -e .

这样安装后，可以直接修改yolov8源码，并且可以立即生效。此图是命令解释：
【深度学习】YOLOv8训练过程，YOLOv8实战教程，目标检测任务SOTA，关键点回归
安装成功后：

pip list可以看到安装的包：

模型训练（检测）

可以重新创建一个新的工程去使用安装好的ultralytics包，这样修改源码可以在別的工程。
【深度学习】YOLOv8训练过程，YOLOv8实战教程，目标检测任务SOTA，关键点回归
下载一个demo数据集：https://ultralytics.com/assets/coco128.zip

最终文件：
【深度学习】YOLOv8训练过程，YOLOv8实战教程，目标检测任务SOTA，关键点回归
train_coco.py，我给的绝对路径：

from ultralytics import YOLO

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"

model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights

# Train the model
model.train(data='/ssd/xiedong/workplace/yolov8_script/coco128.yaml', epochs=100, imgsz=640)

coco128.yaml，这个文件在yolov8的源码中是有的，拉出来改一下，改为绝对路径。

# 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: /ssd/xiedong/workplace/yolov8_script/coco128  # dataset root dir
train: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes
names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  5: bus
  6: train
  7: truck
  8: boat
  9: traffic light
  10: fire hydrant
  11: stop sign
  12: parking meter
  13: bench
  14: bird
  15: cat
  16: dog
  17: horse
  18: sheep
  19: cow
  20: elephant
  21: bear
  22: zebra
  23: giraffe
  24: backpack
  25: umbrella
  26: handbag
  27: tie
  28: suitcase
  29: frisbee
  30: skis
  31: snowboard
  32: sports ball
  33: kite
  34: baseball bat
  35: baseball glove
  36: skateboard
  37: surfboard
  38: tennis racket
  39: bottle
  40: wine glass
  41: cup
  42: fork
  43: knife
  44: spoon
  45: bowl
  46: banana
  47: apple
  48: sandwich
  49: orange
  50: broccoli
  51: carrot
  52: hot dog
  53: pizza
  54: donut
  55: cake
  56: chair
  57: couch
  58: potted plant
  59: bed
  60: dining table
  61: toilet
  62: tv
  63: laptop
  64: mouse
  65: remote
  66: keyboard
  67: cell phone
  68: microwave
  69: oven
  70: toaster
  71: sink
  72: refrigerator
  73: book
  74: clock
  75: vase
  76: scissors
  77: teddy bear
  78: hair drier
  79: toothbrush


# Download script/URL (optional)
download: https://ultralytics.com/assets/coco128.zip

即可看到成功训练起来的情况：

Transferred 355/355 items from pretrained weights
Ultralytics YOLOv8.0.119 🚀 Python-3.7.16 torch-1.12.1+cu116 CUDA:0 (NVIDIA A100-PCIE-40GB, 40390MiB)
WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.
yolo/engine/trainer: task=detect, mode=train, model=yolov8n.yaml, data=/ssd/xiedong/workplace/yolov8_script/coco128.yaml, epochs=100, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train5

                   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]]          
YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs

Transferred 355/355 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train5', view at http://localhost:6006/
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /ssd/xiedong/workplace/yolov8_script/coco128/labels/train2017...
train: New cache created: /ssd/xiedong/workplace/yolov8_script/coco128/labels/train2017.cache
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
val: Scanning /ssd/xiedong/workplace/yolov8_script/coco128/labels/train2017.cach
Plotting labels to runs/detect/train5/labels.jpg... 
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/train5
Starting training for 100 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      1/100      2.55G      1.179      1.595      1.254        127        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.641      0.534      0.612      0.454

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      2/100      2.54G      1.249      1.534      1.247        209        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        128        929      0.693      0.532      0.632      0.469

训练的参数调整：

Key	Value	Description
`model`	`None`	path to model file, i.e. yolov8n.pt, yolov8n.yaml
`data`	`None`	path to data file, i.e. coco128.yaml
`epochs`	`100`	number of epochs to train for
`patience`	`50`	epochs to wait for no observable improvement for early stopping of training
`batch`	`16`	number of images per batch (-1 for AutoBatch)
`imgsz`	`640`	size of input images as integer or w,h
`save`	`True`	save train checkpoints and predict results
`save_period`	`-1`	Save checkpoint every x epochs (disabled if < 1)
`cache`	`False`	True/ram, disk or False. Use cache for data loading
`device`	`None`	device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
`workers`	`8`	number of worker threads for data loading (per RANK if DDP)
`project`	`None`	project name
`name`	`None`	experiment name
`exist_ok`	`False`	whether to overwrite existing experiment
`pretrained`	`False`	whether to use a pretrained model
`optimizer`	`'auto'`	optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
`verbose`	`False`	whether to print verbose output
`seed`	`0`	random seed for reproducibility
`deterministic`	`True`	whether to enable deterministic mode
`single_cls`	`False`	train multi-class data as single-class
`rect`	`False`	rectangular training with each batch collated for minimum padding
`cos_lr`	`False`	use cosine learning rate scheduler
`close_mosaic`	`0`	(int) disable mosaic augmentation for final epochs
`resume`	`False`	resume training from last checkpoint
`amp`	`True`	Automatic Mixed Precision (AMP) training, choices=[True, False]
`fraction`	`1.0`	dataset fraction to train on (default is 1.0, all images in train set)
`profile`	`False`	profile ONNX and TensorRT speeds during training for loggers
`lr0`	`0.01`	initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
`lrf`	`0.01`	final learning rate (lr0 * lrf)
`momentum`	`0.937`	SGD momentum/Adam beta1
`weight_decay`	`0.0005`	optimizer weight decay 5e-4
`warmup_epochs`	`3.0`	warmup epochs (fractions ok)
`warmup_momentum`	`0.8`	warmup initial momentum
`warmup_bias_lr`	`0.1`	warmup initial bias lr
`box`	`7.5`	box loss gain
`cls`	`0.5`	cls loss gain (scale with pixels)
`dfl`	`1.5`	dfl loss gain
`pose`	`12.0`	pose loss gain (pose-only)
`kobj`	`2.0`	keypoint obj loss gain (pose-only)
`label_smoothing`	`0.0`	label smoothing (fraction)
`nbs`	`64`	nominal batch size
`overlap_mask`	`True`	masks should overlap during training (segment train only)
`mask_ratio`	`4`	mask downsample ratio (segment train only)
`dropout`	`0.0`	use dropout regularization (classify train only)
`val`	`True`	validate/test during training

模型pridict

https://docs.ultralytics.com/modes/predict/

import cv2
from ultralytics import YOLO
import os

os.environ["CUDA_VISIBLE_DEVICES"] = "2"

import matplotlib.pyplot as plt

img = cv2.imread("img.png")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

img_1 = cv2.imread("img_1.png")
img_1 = cv2.cvtColor(img_1, cv2.COLOR_BGR2RGB)

model = YOLO('yolov8n.yaml').load('yolov8n.pt')

inputs = [img, img_1]  # list of numpy arrays
results = model(inputs)  # list of Results objects

for img,result in zip(inputs, results):
    boxes = result.boxes  # Boxes object for bbox outputs
    masks = result.masks  # Masks object for segmentation masks outputs
    probs = result.probs  # Class probabilities for classification outputs

模型导出

https://docs.ultralytics.com/modes/export/#arguments

from ultralytics import YOLO

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"

model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights

# Export the model
model.export(format='onnx')

导出成功：文章来源地址https://www.toymoban.com/news/detail-497984.html

                   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]]          
YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs

Transferred 355/355 items from pretrained weights
Ultralytics YOLOv8.0.119 🚀 Python-3.7.16 torch-1.12.1+cu116 CPU
YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs

PyTorch: starting from yolov8n.yaml with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (0.0 MB)

ONNX: starting export with onnx 1.14.0 opset 10...
ONNX: export success ✅ 10.7s, saved as yolov8n.onnx (12.2 MB)

Export complete (28.4s)
Results saved to /ssd/xiedong/workplace/yolov8_script
Predict:         yolo predict task=detect model=yolov8n.onnx imgsz=640 
Validate:        yolo val task=detect model=yolov8n.onnx imgsz=640 data=None 
Visualize:       https://netron.app