Data from: f4-statistics-based ancestry profiling and convolutional neural network phenotyping shed new light on the structure of genetic and spike shape diversity in Aegilops tauschii Coss.
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Spike image dataset of Aegilops tauschii Coss. The dataset contains "dry" and "green" spike image files. dry-spike-images.zip Used for traininig and validating convolutional neural network (CNN). green-spike-images.zip Used for training and validating CNN. dagestan-spike-images.zip Green spike images of Dagestani and Russian accessions used for blind test of the trained CNN. The Accession numbrer of the plant individual from which the spike was sampled, the lineage (TauL1, TauL2, or TauL3) to which the plant individual belongs, and md5sum value are shown in the following CSV files. Lineage information is left blank in labels-dagestan.csv because we used Dagestani and Russian accessions for a blind test of the trained CNN models. labels-dry.csv labels-green.csv labels-dagestan.csv GradCAM, Guided Backpropagation and Guided-GradCAM visualization results CNN prediction explanation image files are archived into gradcam-results.zip. The image files are named in the following format. <dry-or-green>_<filename-in-original-dataset>_<method> The <method> is one of gradcam_cam, gradcam_gb, gradcam_cam_gb, and these names represent GradCAM, Guided Backpropagation, Guided-GradCAM, respectively. Trained ResNet weights The trained ResNet model weights are archived into the following files. Each weight files are named according to the fold number and F1 score on the cross-validation dataset in the fold; e.g. resnet50_2_0.98392.pth represents ResNet weight of fold 2, and the F1 score is 0.98392. resnet-dry-weights.zip resnet-green-weights.zip The weight files are PyTorch state_dict objects, and therefore can be loaded using the example Python code below. import timm import torch model = timm.create_model("timm/resnet50.a1_in1k", num_classes=3) state_dict = torch.load("resnet50_1_1.00000.pth") model.load_state_dict(state_dict)
节节麦（Aegilops tauschii Coss.）穗部图像数据集。 本数据集包含「干燥穗部」与「青绿穗部」两类图像文件。 dry-spike-images.zip：用于卷积神经网络（Convolutional Neural Network, CNN）的训练与验证。 green-spike-images.zip：用于卷积神经网络（CNN）的训练与验证。 dagestan-spike-images.zip：内含达吉斯坦与俄罗斯来源的穗部青绿图像，用于已训练CNN模型的盲测。 采样穗部对应的植株种质编号、植株所属谱系（TauL1、TauL2或TauL3）以及md5校验值（md5sum）均记录于以下CSV文件中。由于将达吉斯坦与俄罗斯来源的材料用于已训练CNN模型的盲测，因此labels-dagestan.csv文件中未填写谱系信息。涉及的CSV文件包括：labels-dry.csv、labels-green.csv、labels-dagestan.csv。 GradCAM、引导反向传播（Guided Backpropagation）与引导GradCAM（Guided-GradCAM）可视化结果： 卷积神经网络预测结果解释图像文件已打包至gradcam-results.zip压缩包中。图像文件命名格式如下： <类型（dry或green）>_<原始数据集内文件名>_<方法名称> 其中<方法名称>可选值为gradcam_cam、gradcam_gb、gradcam_cam_gb，分别对应GradCAM、引导反向传播与引导GradCAM。 已训练的残差网络（Residual Network, ResNet）模型权重： 已训练的ResNet模型权重打包至以下压缩包中。每个权重文件均按照折数与该折交叉验证集上的F1分数命名；例如resnet50_2_0.98392.pth代表第2折的ResNet模型权重，其F1分数为0.98392。涉及的压缩包包括：resnet-dry-weights.zip、resnet-green-weights.zip。 该权重文件为PyTorch的状态字典（state_dict）对象，可通过以下示例Python代码加载： python import timm import torch model = timm.create_model("timm/resnet50.a1_in1k", num_classes=3) state_dict = torch.load("resnet50_1_1.00000.pth") model.load_state_dict(state_dict)
