import os from paddlex.det import transforms import paddlex as pdx # 下载和解压昆虫检测数据集 insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz' pdx.utils.download_and_decompress(insect_dataset, path='./') # 定义训练和验证时的transforms train_transforms = transforms.Compose([ transforms.MixupImage(mixup_epoch=250), transforms.RandomDistort(), transforms.RandomExpand(), transforms.RandomCrop(), transforms.Resize(target_size=608, interp='RANDOM'), transforms.RandomHorizontalFlip(), transforms.Normalize() ]) eval_transforms = transforms.Compose([ transforms.Resize(target_size=608, interp='CUBIC'), transforms.Normalize() ]) # 定义训练和验证所用的数据集 train_dataset = pdx.datasets.VOCDetection( data_dir='insect_det', file_list='insect_det/train_list.txt', label_list='insect_det/labels.txt', transforms=train_transforms, shuffle=True) eval_dataset = pdx.datasets.VOCDetection( data_dir='insect_det', file_list='insect_det/val_list.txt', label_list='insect_det/labels.txt', transforms=eval_transforms) # 初始化模型,并进行训练 # 可使用VisualDL查看训练指标 # VisualDL启动方式: visualdl --logdir output/yolov3_darknet/vdl_log --port 8001 # 浏览器打开 https://0.0.0.0:8001即可 # 其中0.0.0.0为本机访问,如为远程服务, 改成相应机器IP num_classes = len(train_dataset.labels) model = pdx.det.YOLOv3(num_classes=num_classes, backbone='MobileNetV3_large') model.train( num_epochs=270, train_dataset=train_dataset, train_batch_size=8, eval_dataset=eval_dataset, learning_rate=0.000125, lr_decay_epochs=[210, 240], save_dir='output/yolov3_mobilenetv3', use_vdl=True)