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  • 机器学习 - Data Augmentation 策略

    Data Augmentation 策略数据增强是提高模型性能和泛化能力的重要方法.随机调整 - Random resize 平移 - Shift 水平垂直翻转 - HorizontalVerticalflip RotationReflection 旋转仿射变换Noise - 高斯噪声,模糊处理颜色增强 - Color Augmentation,包括色彩饱和度、亮度和对比度等.PCA Jittering常用有两种方式: Scale Jittering,如 VGG 和 ResNet;尺度和长宽比增强变换 - Scale and aspect ratio augmentation,如 Google Inception监督数据增强 - Supervised Data Augmentation(SDA) (海康威视2016提出)类别标签不均衡问题 - Imbalanced Class Problem.Reference - 深度学习大讲堂 - 海康威视研究院ImageNet2016竞赛经验分享 - 使用深度学习(CNN)算法进行图像识别工作时,有哪些data augmentation 的奇技淫巧?
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  • Data augmentation

    只有这样才能确保神经网络学到的特征更加全面但在现实中,若想达到以上的目的要付出巨大的代价,并且还要对照片上出现的东西进行准确标注,另外对于一些稀有的物种信息收集更是十分困难因此我们这里介绍一些为神经网络提供更多数据的方法——数据增强(Dataaugmentation)数据增强有很多手段,包括对原照片进行旋转、拉伸、加噪声等。torch.nnimport torch.nn.functional as Ffrom torchvision import datasets, transforms train_loader = torch.utils.data.DataLoader( datasets.MNIST(..data, train=True, download=True, transform=transforms.Compose()), batch_size=batch_size( datasets.MNIST(..data, train=True, download=True, transform=transforms.Compose() # 随机在90°、180°、270°
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  • trick—Data Augmentation

    我们对其进行了改进,提出Supervised Data Augmentation方法。 ?       
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  • 深度学习: 数据扩充 (Data Augmentation)

    Introduction数据扩充(data augmentation),又名 数据增强 数据增广。其本质即:缺少海量数据时,为了保证模型的有效训练,一分钱掰成两半花。
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  • 深度学习样本生成data augmentation

    google了一下deep learning data augmentation 发现了github几种开源的的方法主要是使用opencv结合python的PIL库。path_var.g_dst_dir = new_item print flip %s, flip dir %s % (dir_item, new_item) do_all_flip(base_dir=dir_item) def augmentationSIZE = 5 * 4 p.sample(SIZE) if __name__ == __main__: # img_flip() # flip_all_in_dir() # do_all_flip() augmentation
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  • 图片数据集太少?Keras Image Data Augmentation 各参数详解

    图像深度学习任务中,面对小数据集,我们往往需要利用Image Data Augmentation图像增广技术来扩充我们的数据集,而keras的内置ImageDataGenerator很好地帮我们实现图像增广=K.image_data_format())官方提供的参数解释因为太长就不贴出来了,大家可以直接点开上面的链接看英文原介绍,我们现在就从每一个参数开始看看它会带来何种效果。针对mnist的其它DataAugmentation结果可以看这个博客:Image Augmentation for Deep Learning With Keras(http:t.cnRY0ZYWF)import imageimport glob # 设置生成器参数datagen = image.ImageDataGenerator(fill_mode=wrap, zoom_range=) gen_data上面所有内容已经公布在我的github(https:github.comJustinhoCHNkeras-image-data-augmentation)上面,附上了实验时的jupyter notebook
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  • 开发 | 图片数据集太少?看我七十二变,Keras Image Data Augmentation 各参数详解

    图像深度学习任务中,面对小数据集,我们往往需要利用Image Data Augmentation图像增广技术来扩充我们的数据集,而keras的内置ImageDataGenerator很好地帮我们实现图像增广=K.image_data_format())官方提供的参数解释因为太长就不贴出来了,大家可以直接点开上面的链接看英文原介绍,我们现在就从每一个参数开始看看它会带来何种效果。针对mnist的其它DataAugmentation结果可以看这个博客:Image Augmentation for Deep Learning With Keras,有修改意见的朋友欢迎留言。?import imageimport glob # 设置生成器参数datagen = image.ImageDataGenerator(fill_mode=wrap, zoom_range=) gen_data上面所有内容已经公布在我的github(https:github.comJustinhoCHNkeras-image-data-augmentation)上面,附上了实验时的jupyter notebook
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  • DADA, Differentiable Automatic Data Augmentation

    地址:http:xxx.itp.ac.cnpdf2006.09541.pdf???????
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  • image——Data Augmentation的代码

    切片(crop):def crop(image, random_crop, image_size): if image.shape>image_size: sz1 = int(image.shape2) sz2 = int(image_size2) if random_crop: diff = sz1-sz2 (h, v) = (np.random.randint(-diff, diff+1), np.random.randint(-diff, diff+1)) else: (h, v) = (0,0) image = image return image############################################################################# 函数:crop# 描述:随机裁剪图像## 输入:图像image, crop_size# 返回:图像image############################################################################def crop(image, crop_size, random_crop=True): if random_crop: # 若随机裁剪 if image.shape > crop_size: sz1 = image.shape 2 sz2 = crop_size 2 diff = sz1 - sz2 (h, v) = (np.random.randint(0, diff + 1), np.random.randint(0, diff + 1)) image = image return image# 左右上下翻转def flip(image, random_flip=True): if random_flip and np.random.choice(): image = np.fliplr(image) if random_flip and np.random.choice(): image = np.flipud(image) return image#图像旋转def random_rotate_image(image): angle = np.random.uniform(low=-10.0, high=10.0) return misc.imrotate(image, angle, bicubic)############################################################################# 函数:rotation# 描述:随机旋转图片,增强数据,用图像边缘进行填充。## 输入:图像image# 返回:图像image############################################################################def rotation(image, random_flip=True): if random_flip and np.random.choice(): w,h = image.shape, image.shape # 0-180随机产生旋转角度。 angle = np.random.randint(0,180) RotateMatrix = cv2.getRotationMatrix2D(center=(image.shape2, image.shape2), angle=angle, scale=0.7) # image = cv2.warpAffine(image, RotateMatrix, (w,h), borderValue=(129,137,130)) #image = cv2.warpAffine(image, RotateMatrix, (w,h),borderValue=(129,137,130)) image = cv2.warpAffine(image, RotateMatrix, (w,h),borderMode=cv2.BORDER_REPLICATE) return image图像归一化处理:def prewhiten(x): mean = np.mean(x) std = np.std(x) std_adj = np.maximum(std, 1.0np.sqrt(x.size)) y = np.multiply(np.subtract(x, mean), 1std_adj) return y图像平移:############################################################################# 函数:translation# 描述:随机平移图片,增强数据,用图像边缘进行填充。## 输入:图像image# 返回:图像image############################################################################def translation(image, random_flip=True): if random_flip and np.random.choice(): w,h = 1920, 1080 H1 = np.float32(,]) H2 = np.random.uniform(50,500, ) H = np.hstack() # H = np.float32(,]) print (H) image = cv2.warpAffine(image, H, (w,h), borderMode=cv2.BORDER_REPLICATE) return image调整光照from skimage import exposureimport numpy as npdef gen_exposure(image, random_xp=True): if random_xp and np.random.choice(): image = exposure.adjust_gamma(image, 1.2) # 调暗 if random_xp and np.random.choice(): image = exposure.adjust_gamma(image, 1.5) # 调暗 if random_xp and np.random.choice(): image = exposure.adjust_gamma(image, 0.9) # 调亮 if random_xp and np.random.choice(): image = exposure.adjust_gamma(image, 0.8) # 调亮 if random_xp and np.random.choice(): image = exposure.adjust_gamma(image, 0.7) # 调暗 return image
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  • On the Generalization Effects of Linear Transformations in Data Augmentation

    地址:https:arxiv.orgabs2005.00695????
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  • 目标检测图像数据增强(Data Augmentation)—— 旋转

    应用场景由于业务需求,需要对部分不符合检测结果的图像进行过滤,因此需要对之前的检测项目进行优化。常见问题有如下亮点:图像中检测目标是倾斜角度;图像中是通过镜子自拍或者加了滤镜处理后的相片; 这两种情况是由于训练样本中含有这两种情况的少,因此需要增加此类样本数。本文只针对第一种情况进行数据增强,解决办法——旋转。素材项目是对服装进行检测,样本图(来源于用户晒图): ?其对应的xml文件: well 15278480618780.jpg 15278480618780.jpg 828 1104 3 0 3 Unspecified 1 0 250 672 531 1104 从xml信息中可以看见图像的具体信息,包括图像名称,尺寸以及检测方框的坐标范围。 ?处理程序这里介绍处理批量处理文件夹中的情形,单张图像处理类似。处理思想读取对应的图像,解析对应的xml,根据旋转的角度来变换之前检测到的坐标,以及保存变换后的图像。处理代码#!usrbinenv pythonimport cv2import mathimport numpy as npimport osimport pdbimport xml.etree.ElementTree as ETclass ImgAugemention(): def __init__(self): self.angle = 90 # rotate_img def rotate_image(self, src, angle, scale=1.): w = src.shape h = src.shape # convet angle into rad rangle = np.deg2rad(angle) # angle in radians # calculate new image width and height nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale # ask OpenCV for the rotation matrix rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale) # calculate the move from the old center to the new center combined # with the rotation rot_move = np.dot(rot_mat, np.array()) # the move only affects the translation, so update the translation # part of the transform rot_mat += rot_move rot_mat += rot_move # map return cv2.warpAffine( src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4) def rotate_xml(self, src, xmin, ymin, xmax, ymax, angle, scale=1.): w = src.shape h = src.shape rangle = np.deg2rad(angle) # angle in radians # now calculate new image width and height # get width and heigh of changed image nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale # ask OpenCV for the rotation matrix rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale) # calculate the move from the old center to the new center combined # with the rotation rot_move = np.dot(rot_mat, np.array()) # the move only affects the translation, so update the translation # part of the transform rot_mat += rot_move rot_mat += rot_move # rot_mat: the final rot matrix # get the four center of edges in the initial martix,and convert the coord point1 = np.dot(rot_mat, np.array()) point2 = np.dot(rot_mat, np.array()) point3 = np.dot(rot_mat, np.array()) point4 = np.dot(rot_mat, np.array()) # concat np.array concat = np.vstack((point1, point2, point3, point4)) # change type concat = concat.astype(np.int32) print(concat) rx, ry, rw, rh = cv2.boundingRect(concat) return rx, ry, rw, rh def process_img(self, imgs_path, xmls_path, img_save_path, xml_save_path, angle_list): # assign the rot angles for angle in angle_list: for img_name in os.listdir(imgs_path): # split filename and suffix n, s = os.path.splitext(img_name) # for the sake of use yol model, only process .jpg if s == .jpg: img_path = os.path.join(imgs_path, img_name) img = cv2.imread(img_path) rotated_img = self.rotate_image(img, angle) # 写入图像 cv2.imwrite(img_save_path + n + _ + str(angle) + d.jpg, rotated_img) print(log: %s is processed. % (angle, img)) xml_url = img_name.split(.) + .xml xml_path = os.path.join(xmls_path, xml_url) tree = ET.parse(xml_path) root = tree.getroot() for box in root.iter(bndbox): xmin = float(box.find(xmin).text) ymin = float(box.find(ymin).text) xmax = float(box.find(xmax).text) ymax = float(box.find(ymax).text) x, y, w, h = self.rotate_xml(img, xmin, ymin, xmax, ymax, angle) # change the coord box.find(xmin).text = str(x) box.find(ymin).text = str(y) box.find(xmax).text = str(x+w) box.find(ymax).text = str(y+h) box.set(updated, yes) # write into new xml tree.write(xml_save_path + n + _ + str(angle) + d.xml) print( %s is processed. % (angle, img_name))if __name__ == __main__: img_aug = ImgAugemention() imgs_path = .image xmls_path = .xml img_save_path = .rotate xml_save_path = .xml_rot angle_list = img_aug.process_img(imgs_path, xmls_path, img_save_path, xml_save_path, angle_list)处理结果旋转60度 well 15278480618780.jpg 15278480618780.jpg 828 1104 3 0 3 Unspecified 1 0 777 701 1152 945 ?旋转90度 well 15278480618780.jpg 15278480618780.jpg Unknown 828 1104 3 0 3 Unspecified 1 0 672 297 1105 579 ?参考https:blog.csdn.netu014540717articledetails53301195https:www.cnblogs.comwangguchangqingp4045150.html
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  • 深度学习中的数据增强技术:Augmentation

    概念数据增强(Data Augmentation)是一种通过利用算法来扩展训练数据的技术。人工智能三要素之一为数据,但获取大量数据成本高,但数据又是提高模型精度和泛化效果的重要因素。参考A survey on Image Data Augmentation for Deep Learning https:link.springer.comarticle10.1186s40537-019-0197-0https:github.comguanxsdata-augmentation
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  • pose shape expression augmentation(0.9807)

    该文章的主要思路是对数据集进行扩增(data augmentation)。CNN深度学习模型,比如face++,DeepID,FaceNet等需要基于百万级人脸图像的训练才能达到高精度。
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  • Python数据增强(data augmentation)库--Augmentor 使用介绍

    Augmentor 使用介绍原图?1.random_distortion(probability, grid_height, grid_width, magnitude)最终选择参数为p.random_distortion(probability=0.8, grid_height=3, grid_width=3, magnitude=6)?其他参数效果:magnitude和grid_width,grid_height越大,扭曲程度越大----p.random_distortion(probability=0.6, grid_height=6, grid_width=6, magnitude=5)?p.random_distortion(probability=0.6, grid_height=6, grid_width=6, magnitude=9)?p.random_distortion(probability=0.6, grid_height=10, grid_width=10, magnitude=5)?2.random_erasing(probability, rectangle_area)rectangle_area表示覆盖区域的比例,值越大比例越大。但是设置为1的时候并不是全覆盖,不知道为什么,反正也没必要弄清楚p.random_erasing(1,1)?3.zoom_random(probability, percentage_area)放大图片,然后按照percenta_area的比例对图片进行crop。p.zoom_random(probability=1, percentage_area=0.2)?p.zoom_random(probability=1, percentage_area=0.8)?4.zoom(probability, min_factor, max_factor)p.zoom(probability=1, min_factor=1.1, max_factor=1.5)?p.zoom(probability=1, min_factor=2, max_factor=2)?组合操作p.rotate_random_90(probability=0.8)p.random_distortion(probability=0.8, grid_height=3, grid_width=3, magnitude=6)p.random_erasing(0.3, 0.2)p.zoom(probability=0.4, min_factor=1.1, max_factor=1.5)p.sample(6)??????
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  • 数据扩充在NLP中什么时候有助于泛化?(CS.CL)

    原文题目:When does data augmentation help generalization in NLP?Recent work has proposed using data augmentation--that is, generating training examples on which theseWe design a series of toy learning problems to investigate the conditions under which such data augmentationnumber of counterexamples needed to reach a given error rate is independent of the amount of training data, and that this type of data augmentation becomes less effective as the target strong feature becomes
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  • 号称百万年薪的架构师开发的“傻瓜式”分类器模型“集”

    augmentation)parser.add_argument(--v_flip, type=str2bool, default=False, help=Whether to randomly flipthe image vertically for data augmentation)parser.add_argument(--rotation, type=float, default=0.0,help=Whether to randomly rotate the image for data augmentation)parser.add_argument(--zoom, type=float, default=0.0, help=Whether to randomly zoom in for data augmentation)parser.add_argument(--shear, type=float, default=0.0, help=Whether to randomly shear in for data augmentation)parser.add_argument(--model
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  • 视频动作识别--Towards Good Practices for Very Deep Two-Stream ConvNets

    另一个是针对训练数据过少的问题: (i) pre-training for both spatial and temporal nets, (ii) smaller learningrates, (iii) more dataaugmentation techniques, (iv) high drop out ratio.2 Very Deep Two-stream ConvNets 2.1.ConvNets首先是预训练 Pre-training for Two-stream ConvNets, 预训练完之后,我们对两个网络分别使用 Smaller Learning Rate,数据增强 More DataAugmentation Techniques. random cropping and horizontal flippingtwo new data augmentation techniques
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  • 形式化样式传输的并行数据增强(CS CL)

    原文标题:Parallel Data Augmentation for Formality Style Transfe原文:The main barrier to progress in the taskof Formality Style Transfer is the inadequacy of training data.In this paper, we study how to augment parallel data and propose novel and simple data augmentation methodsExperiments demonstrate that our augmented parallel data largely helps improve formality style transfer
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  • 一文综述生成更多图像训练数据的方法|视觉进阶

    使用数据增强(data augmentation)生成更多训练数据当我们只有少量图像数据用于训练深度卷积神经网络时,我们可以使用数据增强技术从已经拥有的图像数据中生成更多训练数据。?The efectiveness of data augmentation in image classifcation using deep learning.:nanonets.comblogdata-augmentation-how-to-use-deep-learning-when-you-have-limited-data-part-2Shorten,A survey on Image Data Augmentation for Deep Learning.Data Augmentation Using GANs.
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