基于飞浆paddle的Mv3驾驶员行为识别

 “其实一开始并没有想学深度学习”

! pip install --upgrade pip
! pip install paddlex
! pip install --user --upgrade pyarrow==11.0.0
# 配置环境

 train_list格式（test同理）：图片路径+\t+标签

newLabels格式：标签

训练代码

import paddlex as pdx

from paddlex import transforms as T

train_transforms = T.Compose(
    [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])

eval_transforms = T.Compose([
    T.ResizeByShort(short_size=256), T.CenterCrop(crop_size=224), T.Normalize()
])
# 定义数据集的transform

train_dataset = pdx.datasets.ImageNet(
    data_dir='train',
    file_list='train_list.txt',
    label_list='newLabels.txt',
    transforms=train_transforms,
    shuffle=True)
    
eval_dataset = pdx.datasets.ImageNet(
    data_dir='train',
    file_list='val_list.txt',
    label_list='newLabels.txt',
    transforms=eval_transforms)
# 定义数据集

num_classes = len(train_dataset.labels)
model = pdx.cls.MobileNetV3_large_ssld(num_classes=num_classes)
model.train(num_epochs=6, # 训练轮次
            train_dataset=train_dataset, #训练集
            train_batch_size=32,# 训练batch
            eval_dataset=eval_dataset, #测试集
            lr_decay_epochs=[2, 4],# 学习率变化轮次
            save_interval_epochs=2, # 保存模型轮次
            learning_rate=0.00125,# 起始学习率
            save_dir='output/mobilenetv3_large_ssld3',# 保存模型目录
            use_vdl=True)
# 开始训练