[Target detection] YOLOV8 practical entry (3) model training

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trainModes are used to train models on custom datasets YOLOv8. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the class and location of objects in images.
Note : YOLOv8 datasets such as COCO, VOC, ImageNet and many others are automatically downloaded when first used, i.e.yolo train data=coco.yaml

model = YOLO('yolov8n.yaml')
# 利用官方提供的数据集配置文件进行训练，如COCO、VOC、ImageNet和许多其他数据集，在首次使用时自动下载
results = model.train(data='coco128.yaml', epochs=3)

# 不提供数据集配置文件，根据预训练文件中提供的相关信息进行训练
model = YOLO('yolov8n.pt') 
model.train(epochs=5)

# 恢复上次中断的训练
model = YOLO("last.pt")
model.train(resume=True)

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The training settings of the YOLOv8 model refer to the various hyperparameters and configurations used to train the model on the dataset. These settings affect the performance, speed, and accuracy of the model. Some common training settings for YOLOv8 include batch size, learning rate, momentum, and weight decay. Other factors that can affect the training process include the choice of optimizer, the choice of loss function, and the size and composition of the training set. It is important to carefully tune and experiment with these settings to achieve the best performance for a given task.

The relevant parameters are as follows:

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	'SGD'	optimizer to use, choices=[‘SGD’, ‘Adam’, ‘AdamW’, ‘RMSProp’]
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]
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