The first goal of this work was the collection of a large labelled image dataset to facilitate the classification of a variety of weed species for robotic weed control. The emerging trend of deep learning for object detection and classification necessitates its use for this task. As a result, ...
This repository makes available the source code and public dataset for the work, "DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning", published with open access by Scientific Reports:https://www.nature.com/articles/s41598-018-38343-3. The DeepWeeds dataset consists of 17,50...
The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of ...
3. Design considerations for image Data Augmentation Test-time augmentation Curriculum learning Resolution impact Final dataset size Alleviating class imbalance with Data Augmentation 参考链接 A survey on Image Data Augmentation 数据增强文献综述 - 知乎 欢迎转载,但是未经作者本人同意,转载文章之后必须...
1. featurewise datagen = image.ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True)featurewise_center的官方解释："Set input mean to 0 over the dataset, feature-wise." 大意为使数据集去中心化（使得其均值为0），而samplewise_std_normalization的官方解释是“ Divide inputs by ...
An Image Labeling Tool and Agricultural Dataset for Deep LearningJustin BrooksMedhat MoussaPatrick Wspanialy
使用ImageNet 2012 classification dataset,共有1000类,其中训练图片1.28 million张,验证图片50k张,测试图片100k张。 评估的参数有 top-1 和 top-5 error rate Plain Network:这里作者评估了 18-layer 和 34-layer 两个网络。34-layer plain net如Fig.3所示,而18-layer net则是一种与之相似的网路。详细结构参...
2.2Datasets for Image Dehazing Tasks 为了保证数据的可靠性,研究人员主要使用两种策略来获得成对的有雾图像和无雾图像(即同一场景的无雾图和有雾图)。一个是基于包含深度注释(depth annotation)的数据集而合成的(synthesized),比如D-Hazy【D-hazy: A dataset to evaluate quantitatively dehazing algorithms.】在NYU...
featurewise_center的官方解释:"Set input mean to 0 over the dataset, feature-wise." 大意为使数据集去中心化(使得其均值为0),而samplewise_std_normalization的官方解释是“ Divide inputs by std of the dataset, feature-wise.”,大意为将输入的每个样本除以其自身的标准差。这两个参数都是从数据集整体上...
Annotation of datasets for deep learning applied to satellite and aerial imagery - satellite-image-deep-learning/annotation