YOLO系列的算法更新实在太快了，前些天刚学习完YOLOV7，YOLOV8就出来了。今天先理解模型的训练过程，后续再学习V8的网络结构等细节。
YOLOV8源码链接：https://github.com/ultralytics/ultralytics
1 数据格式转换
Wider Face数据格式转YOLO数据格式可以参考我之前写的一篇博客：
https://blog.csdn.net/qq_38964360/article/details/128712287?spm=1001.2014.3001.5502
2 修改相关配置文件
首先是模型配置文件'ultralytics/models/v8/yolov8n.yaml'，因为人脸检测是单目标检测，因此该配置文件里的nc应该改成1，部分代码如下：
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# Ultralytics YOLO 🚀, GPL-3.0 license
-
-
# Parameters
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nc:
1
# number of classes
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depth_multiple:
0.33
# scales module repeats
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width_multiple:
0.25
# scales convolution channels
随后仿照 'yolov8/ultralytics/yolo/data/datasets/coco128.yaml' 文件，新建 'yolov8/ultralytics/yolo/data/datasets/wider_face.yaml' 文件，文件内容如下：
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# 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, ..]
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path: /kaxier01/projects/FAS/yolov8/datasets/wider_face
# dataset root dir
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train: images/train
# train images (relative to 'path') 12876 images
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val: images/val
# val images (relative to 'path') 3226 images
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test:
# test images (optional)
-
-
# Classes
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names:
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0: face
-
-
# Download script/URL (optional)
-
download:
数据集文件目录如下(images以及labels均由步骤1生成)：
最后修改 'yolov8/ultralytics/yolo/configs/default.yaml' 文件中的参数，如：batch size、device、lr、损失权重等，代码如下：
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# Ultralytics YOLO 🚀, GPL-3.0 license
-
# Default training settings and hyperparameters for medium-augmentation COCO training
-
-
task:
"detect"
# choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
-
mode:
"train"
# choices=['train', 'val', 'predict'] # mode to run task in.
-
-
# Train settings -------------------------------------------------------------------------------------------------------
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model: null
# i.e. yolov8n.pt, yolov8n.yaml. Path to model file
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data: null
# i.e. coco128.yaml. Path to data file
-
epochs:
300
# number of epochs to train for
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patience:
50
# TODO: epochs to wait for no observable improvement for early stopping of training
-
batch:
32
# number of images per batch
-
imgsz:
640
# size of input images
-
save:
True
# save checkpoints
-
cache:
False
# True/ram, disk or False. Use cache for data loading
-
device:
0,
1,
2,
3
# cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on
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workers:
16
# number of worker threads for data loading
-
project: null
# project name
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name: null
# 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']
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verbose:
False
# whether to print verbose output
-
seed:
0
# random seed for reproducibility
-
deterministic:
True
# whether to enable deterministic mode
-
single_cls:
True
# train multi-class data as single-class
-
image_weights:
False
# use weighted image selection for training
-
rect:
False
# support rectangular training
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cos_lr:
False
# use cosine learning rate scheduler
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close_mosaic:
10
# disable mosaic augmentation for final 10 epochs
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resume:
False
# resume training from last checkpoint
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# Segmentation
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overlap_mask:
True
# masks should overlap during training
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mask_ratio:
4
# mask downsample ratio
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# Classification
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dropout:
0.0
# use dropout regularization
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-
# Val/Test settings ----------------------------------------------------------------------------------------------------
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val:
True
# validate/test during training
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save_json:
False
# save results to JSON file
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save_hybrid:
False
# save hybrid version of labels (labels + additional predictions)
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conf: null
# object confidence threshold for detection (default 0.25 predict, 0.001 val)
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iou:
0.7
# intersection over union (IoU) threshold for NMS
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max_det:
300
# maximum number of detections per image
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half:
False
# use half precision (FP16)
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dnn:
False
# use OpenCV DNN for ONNX inference
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plots:
True
# show plots during training
-
-
# Prediction settings --------------------------------------------------------------------------------------------------
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source: null
# source directory for images or videos
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show:
False
# show results if possible
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save_txt:
False
# save results as .txt file
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save_conf:
False
# save results with confidence scores
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save_crop:
False
# save cropped images with results
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hide_labels:
False
# hide labels
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hide_conf:
False
# hide confidence scores
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vid_stride:
1
# video frame-rate stride
-
line_thickness:
3
# bounding box thickness (pixels)
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visualize:
False
# visualize results
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augment:
False
# apply data augmentation to images
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agnostic_nms:
False
# class-agnostic NMS
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retina_masks:
False
# use retina masks for object detection
-
-
# Export settings ------------------------------------------------------------------------------------------------------
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format: torchscript
# format to export to
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keras:
False
# use Keras
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optimize:
False
# TorchScript: optimize for mobile
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int8:
False
# CoreML/TF INT8 quantization
-
dynamic:
False
# ONNX/TF/TensorRT: dynamic axes
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simplify:
False
# ONNX: simplify model
-
opset:
17
# ONNX: opset version
-
workspace:
4
# TensorRT: workspace size (GB)
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nms:
False
# CoreML: add NMS
-
-
# Hyperparameters ------------------------------------------------------------------------------------------------------
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lr0:
0.02
# initial learning rate (SGD=1E-2, Adam=1E-3)
-
lrf:
0.01
# final OneCycleLR 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
-
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)
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copy_paste:
0.0
# segment copy-paste (probability)
-
-
# Hydra configs --------------------------------------------------------------------------------------------------------
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cfg: null
# for overriding defaults.yaml
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hydra:
-
output_subdir: null
# disable hydra directory creation
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run:
-
dir: .
-
-
# Debug, do not modify -------------------------------------------------------------------------------------------------
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v5loader:
False
# use legacy YOLOv5 dataloader
3 训练及验证
参考源码中的README.md 文件，安装相关依赖库，
pip install ultralytics模型的训练、验证及预测都有两种实现方式：
1）使用Command Line Interface (CLI)。指令如下：
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# 单卡训练
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yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=
0
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# 多卡训练
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yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=\
'0,1,2,3\'
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# Syntax
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yolo task=detect mode=train model=yolov8n.yaml args...
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classify predict yolov8n-cls.yaml args...
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segment val yolov8n-seg.yaml args...
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export yolov8n.pt format=onnx args...
2）使用Python。新建一个脚本，代码如下：
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# filename: python_example.py
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# dir: yolov8/python_example.py
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from ultralytics
import YOLO
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-
-
## 以下模型初始化指令选一个就行
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model = YOLO(
"yolov8/ultralytics/models/v8/yolov8n.yaml")
# 从头开始训练
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model = YOLO(
"yolov8/weights/yolov8n.pt")
# 或者加载预训练好的模型
-
-
# 模型训练
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results = model.train(data=
"yolov8/ultralytics/yolo/data/datasets/wider_face.yaml", epochs=
300)
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# 模型验证
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results = model.val()
-
-
# 模型导出
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success = model.export(
format=
"onnx")
使用以下指令便可多卡训练模型(我这里用了4卡训练，把default.yaml的device值改为0,1,2,3)：
python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 yolov8/python_example.py数据集加载相关文件：'yolov8/ultralytics/yolo/data/dataloaders/v5loader.py'
数据增强相关文件：'yolov8/ultralytics/yolo/data/dataloaders/v5augmentations.py'
模型定义相关文件：'yolov8/ultralytics/yolo/engine/model.py'
模型训练相关文件：'yolov8/ultralytics/yolo/engine/trainer.py'
模型训练过程：
模型验证过程：
测试结果：
转载：https://blog.csdn.net/qq_38964360/article/details/128728145
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