每次代码更新版本都要费眼睛去适应，写个教程方便他人
前言
这里是写给有YOLOv5基础的，因为v5的新手教程已经很多了，v8和v5的源码形式上差不多，只是调用的时候有点区别而已。
YOLOv8 github源码
一、直接用v5的环境
v8的环境和v5没什么主要区别，待会儿跑一遍，缺什么补什么就好。
二、代码文件
下载源文件，不用全部解压，只需要将子文件ultralytics到你的项目文件夹下。例如：
这里的main_train.py文件是我自己创的，为的就是运行训练，而且代码也相当简单：
from ultralytics import YOLO
if __name__ == '__main__':
# 加载模型
# model = YOLO("yolov8n.yaml")
model = YOLO("./yolov8n.pt") # 用初始化权重
results = model.train(data="./coco128.yaml") # 载入数据集
# results = model.val() # 在验证集上评估模型性能
这里的data.yaml跟YOLOv5是一模一样的，不用改
二、超参数修改
1.yaml文件
v8把所有的参数放在ultralytics\yolo\cfg\default.yaml文件下了，相当于把v5的cfg.yaml和paser堆到了一起，同时还包含着detect、export和tracing（这个v5没有）的参数。总之就是弄到一块去了：
# Ultralytics YOLO 🚀, GPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training
task: detect # inference task, i.e. detect, segment, classify
mode: train # YOLO mode, i.e. train, val, predict, export
# Train settings -------------------------------------------------------------------------------------------------------
model: # path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: # 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: # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: # number of worker threads for data loading (per RANK if DDP)
project: runs # project name
name: FT # experiment name, results saved to 'project/name' directory
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: True # 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
image_weights: False # use weighted image selection for training
rect: False # support rectangular training if mode='train', support rectangular evaluation if mode='val'
cos_lr: False # use cosine learning rate scheduler
close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
resume: False # resume training from last checkpoint
# Segmentation
overlap_mask: True # masks should overlap during training (segment train only)
mask_ratio: 4 # mask downsample ratio (segment train only)
# Classification
dropout: 0.0 # use dropout regularization (classify train only)
# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True # validate/test during training
split: val # dataset split to use for validation, i.e. 'val', 'test' or 'train'
save_json: False # save results to JSON file
save_hybrid: False # save hybrid version of labels (labels + additional predictions)
conf: 0.001 # object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7 # intersection over union (IoU) threshold for NMS
max_det: 300 # maximum number of detections per image
half: False # use half precision (FP16)
dnn: False # use OpenCV DNN for ONNX inference
plots: True # save plots during train/val
# Prediction settings --------------------------------------------------------------------------------------------------
source: # source directory for images or videos
show: False # show results if possible
save_txt: False # save results as .txt file
save_conf: False # save results with confidence scores
save_crop: False # save cropped images with results
hide_labels: False # hide labels
hide_conf: False # hide confidence scores
vid_stride: 1 # video frame-rate stride
line_thickness: 3 # bounding box thickness (pixels)
visualize: False # visualize model features
augment: False # apply image augmentation to prediction sources
agnostic_nms: False # class-agnostic NMS
classes: # filter results by class, i.e. class=0, or class=[0,2,3]
retina_masks: False # use high-resolution segmentation masks
boxes: True # Show boxes in segmentation predictions
# Export settings ------------------------------------------------------------------------------------------------------
format: onnx # format to export to
keras: False # use Keras
optimize: False # TorchScript: optimize for mobile
int8: False # CoreML/TF INT8 quantization
dynamic: False # ONNX/TF/TensorRT: dynamic axes
simplify: False # ONNX: simplify model
opset: # ONNX: opset version (optional)
workspace: 4 # TensorRT: workspace size (GB)
nms: False # CoreML: add NMS
# Hyperparameters ------------------------------------------------------------------------------------------------------
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
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
label_smoothing: 0.0 # label smoothing (fraction)
nbs: 64 # nominal batch size
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)
# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg: # for overriding defaults.yaml
# Debug, do not modify -------------------------------------------------------------------------------------------------
v5loader: False # use legacy YOLOv5 dataloader
# Tracker settings ------------------------------------------------------------------------------------------------------
tracker: botsort.yaml # tracker type, ['botsort.yaml', 'bytetrack.yaml']
默认参数就能跑，但是我用的pytorch版本比较高，跑的时候会出现warning（具体是啥我也忘了），通过把workers输入0就可以了
2.覆盖超参
可以在model.train的时候覆盖超参数，具体为输入同名参数就行。代码如下（示例）：
from ultralytics import YOLO
if __name__ == '__main__':
model = YOLO("./yolov8n.pt")
results = model.train(data="./coco128.yaml", epoch=100, batch=8)
看上去是简单了，但是用惯了v5就会感觉怪怪的文章来源：https://www.toymoban.com/news/detail-704977.html
总结
这样用了之后是能跑的，而且这代码集合了v3、5、8，习惯了之后也是挺方便。就是那个default.yaml一开始让我有点懵，找不到选择的地方（比如我想用不同的yaml）。但是经过初步查看，应该是在YOLO类中的self.overrides参数，改改这个类的初始化函数应该就能用了。文章来源地址https://www.toymoban.com/news/detail-704977.html
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