【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

可用资源

https://github.com/ultralytics/ultralytics

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：

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。

下载一个demo数据集：https://ultralytics.com/assets/coco128.zip

最终文件：

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：

                   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