train_transforms = T.Compose([ T.MixupImage(mixup_epoch=-1), T.RandomDistort(), T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(), T.RandomHorizontalFlip(), T.BatchRandomResize(target_sizes=[ 320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704, 736, 768 ], interp='RANDOM'), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) eval_transforms = T.Compose([ T.Resize(target_size=640, interp='CUBIC'), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # 定义训练和验证所用的数据集 train_dataset = pdx.datasets.VOCDetection( data_dir='/home/aistudio/dataset', file_list='/home/aistudio/dataset/train_list.txt', label_list='/home/aistudio/dataset/labels.txt', transforms=train_transforms, shuffle=True) eval_dataset = pdx.datasets.VOCDetection( data_dir='/home/aistudio/dataset', file_list='/home/aistudio/dataset/val_list.txt', label_list='/home/aistudio/dataset/labels.txt', transforms=eval_transforms,
import paddlex as pdx from paddlex import transforms as T # 定义预处理变换 train_transforms = T.Compose([ T.Resize(target_size=[128, 800], interp='LINEAR', keep_ratio=False), T.RandomHorizontalFlip(), T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) eval_transforms = T.Compose([ T.Resize(target_size=[128, 800], interp='LINEAR', keep_ratio=False), T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) # 定义数据集 train_dataset = pdx.datasets.SegDataset(data_dir='steel', file_list='steel/train_list.txt', label_list='steel/labels.txt', transforms=train_transforms, num_workers='auto', shuffle=True) eval_dataset = pdx.datasets.SegDataset(data_dir='steel', file_list='steel/val_list.txt', label_list='steel/labels.txt', transforms=eval_transforms, shuffle=False) # 定义模型 num_classes = len(train_dataset.labels)
# 定义训练和验证时的transforms # API说明:https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py train_transforms = T.Compose([ T.MixupImage(mixup_epoch=250), T.RandomDistort(), T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(), T.RandomHorizontalFlip(), T.BatchRandomResize( target_sizes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608], interp='RANDOM'), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) eval_transforms = T.Compose([ T.Resize(608, interp='CUBIC'), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # 下载和解压表计检测数据集,如果已经预先下载,可注释掉下面两行 meter_det_dataset = 'https://bj.bcebos.com/paddlex/examples/meter_reader/datasets/meter_det.tar.gz' pdx.utils.download_and_decompress(meter_det_dataset, path='./') # 定义训练和验证所用的数据集 # API说明:https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/cv/datasets/coco.py#L26 train_dataset = pdx.datasets.CocoDetection( data_dir='meter_det/train/', ann_file='meter_det/annotations/instance_train.json', transforms=train_transforms, shuffle=True) eval_dataset = pdx.datasets.CocoDetection(
import paddlex as pdx from paddlex import transforms as T # 定义训练和验证时的transforms # API说明:https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py train_transforms = T.Compose([ T.Resize(target_size=512), T.RandomHorizontalFlip(), T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) eval_transforms = T.Compose([ T.Resize(target_size=512), T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), ]) # 下载和解压指针刻度分割数据集,如果已经预先下载,可注释掉下面两行 meter_seg_dataset = 'https://bj.bcebos.com/paddlex/examples/meter_reader/datasets/meter_seg.tar.gz' pdx.utils.download_and_decompress(meter_seg_dataset, path='./') # 定义训练和验证所用的数据集 # API说明:https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22 train_dataset = pdx.datasets.SegDataset(data_dir='meter_seg', file_list='meter_seg/train.txt', label_list='meter_seg/labels.txt', transforms=train_transforms, shuffle=True) eval_dataset = pdx.datasets.SegDataset(data_dir='meter_seg', file_list='meter_seg/val.txt', label_list='meter_seg/labels.txt',