整数或整数元组（长度为 2）。在裁剪维度（第 1 个轴）的开始和结束位置应该 裁剪多少个单位。如果只提供了一个整数，那么这两个位置将使用相同的值。 输入尺寸 3D 张量，尺寸为 (batch, axis_to_crop, features)。 输出尺寸 3D 张量，尺寸为 (batch, cropped_axis, features)。 5.3.7 Cropping2D [source] keras.layers 度 和 宽 度 的 两 个 不 同 的 对 称 裁 剪 值： (symmetric_height_crop, symmetric_width_crop)。 • 如果为 2 个整数的 2 个元组：解释为 ((top_crop, bottom_crop), (left_crop, right_crop))。 • data_format: 字符串，channels_last (默认) 或 channels_first (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)。 • 如 果 为 2 个 整 数 的 3 个 元 组： 解 释 为 ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))。 • data_format:

voc_rand_crop(feature, label, height, width): """随机裁剪特征和标签图像""" rect = torchvision.transforms.RandomCrop.get_params( feature, (height, width)) feature = torchvision.transforms.functional.crop(feature crop(feature, *rect) label = torchvision.transforms.functional.crop(label, *rect) return feature, label imgs = [] for _ in range(n): imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300) imgs = VOCSegDataset(torch.utils.data.Dataset): """一个用于加载VOC数据集的自定义数据集""" def __init__(self, is_train, crop_size, voc_dir): self.transform = torchvision.transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0

▪ Random Move & Crop ▪ GAN https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data- part-2-data-augmentation-c26971dc8ced Flip Rotate Rotate Scale Crop Part Noise ▪ Data

ways to apply sentence, word and character augmentations. We shuffle sentences, substitute, swap, crop and delete words, and apply character misspelling augmentations. We also limit the augmentation probability ='delete', **aug_args), naw.RandomWordAug(action='swap', **aug_args), naw.RandomWordAug(action='crop', **aug_args), naw.RandomWordAug(action='substitute', **aug_args), nac.KeyboardAug() ] flow = applies a transformation technique, achieves the best results. Dataset Standard (Random Flip & Crop) Mixup CutMix AugMix CIFAR-10-C 29.0 23.5 30.3 12.5 CIFAR-100-C12 55.6 52.6 55.5 38.3 Table 3-3:

Dataset Preprocessing ▪ Image Resize ▪ 224x224 for ResNet18 ▪ Data Argumentation ▪ Rotate ▪ Crop ▪ Normalize ▪ Mean, std ▪ ToTensor Step2.build model ▪ Inherit from base class ▪ Define forward

of type Scalar. v3: red component of type Scalar. v4: alpha component of type Scalar. crop: flag which indicates whether image will be cropped after resize or not. after: function:

green component of type Scalar. v3: red component of type Scalar. v4: alpha component of type Scalar. crop: flag which indicates whether image will be cropped after resize or not. after: function: name:

# 图片先缩放到稍大尺寸 x = tf.image.resize(x, [244, 244]) # 再随机裁剪到合适尺寸 x = tf.image.random_crop(x, [224,224,3]) 图 9.33 是缩放到244 × 244大小的图片，图 9.34 某次随机裁剪到224 × 224大小的例子， 图 9.35 也是某次随机裁剪的例子。 70x70 transforms.Resize(70), # 随机 crop 出 64x64 大小 transforms.RandomCrop(64),

a DataFrame. Try using .loc[row_index,col_indexer] = value instead A chained assignment can also crop up in setting in a mixed dtype frame. Note: These setting rules apply to all of .loc/.iloc. This

a DataFrame. Try using .loc[row_index,col_indexer] = value instead A chained assignment can also crop up in setting in a mixed dtype frame. Note: These setting rules apply to all of .loc/.iloc/.ix This