Torch3Dseg - Dataset

Dataset for Training a 3D-UNet for semantic segmentation of textile reinforcements. The Following zip files and xlxs file have been uploaded: ├── 285-10_layer_00 │ ├── 285_10-layer_00_0 │ │ ├── 285_10-layer_00_0.seg.nrrd │ │ ├── 285_10-layer_00_0.vol.nrrd │ │ └── 285_10-layer_00_0.vol.seg.train.h5 │ ├── 285_10-layer_00_1 │ │ ├── 285_10-layer_00_1.seg.nrrd │ │ ├── 285_10-layer_00_1.vol.nrrd │ │ └── 285_10-layer_00_1.vol.seg.train.h5 │ └── 285_10-layer_00_3 │ ├── 285_10-layer_00_3.seg.nrrd │ ├── 285_10-layer_00_3.vol.nrrd │ └── 285_10-layer_00_3.vol.seg.eval.h5 ├── 285-10_layer_00.zip ├── 285-10_layer_01 │ ├── 285_10-layer_01_0 │ │ ├── 285_10-layer_01_0.seg.nrrd │ │ ├── 285_10-layer_01_0.vol.nrrd │ │ └── 285_10-layer_01_0.vol.seg.train.h5 │ ├── 285_10-layer_01_1 │ │ ├── 285_10-layer_01_1.seg.nrrd │ │ ├── 285_10-layer_01_1.vol.nrrd │ │ └── 285_10-layer_01_1.vol.seg.train.h5 │ └── 285_10-layer_01_3 │ ├── 285_10-layer_01_3.seg.nrrd │ ├── 285_10-layer_01_3.vol.nrrd │ └── 285_10-layer_01_3.vol.seg.eval.h5 ├── 285-10_layer_02.zip │ └── 285_10-layer_02_0 │ ├── 285_10-layer_02_0.seg.nrrd │ ├── 285_10-layer_02_0.vol.nrrd │ ├── 285_10-layer_02_0.vol.seg.eval.h5 │ ├── 285_10-layer_02_0.vol.seg.h5 │ └── 285_10-layer_02_0.vol.seg.train.h5 ├── 285-10_layer_03.zip │ └── 285_10-layer_03_0 │ ├── 285_10-layer_03_0.seg.nrrd │ ├── 285_10-layer_03_0.vol.nrrd │ ├── 285_10-layer_03_0.vol.seg.eval.h5 │ ├── 285_10-layer_03_0.vol.seg.h5 │ └── 285_10-layer_03_0.vol.seg.train.h5 └── 285_10_layer_machine_data.xlsx The zips contain the labeled data sets per stage. In each subfolder one can find the *.nrrd file of the volume and labeled segmentation. In the h5 file, you will find the following data: volume: Contains the raw X-ray computed tomography (CT) data, stored as a three-dimensional array of int16 values with shape [x_0, y_0, z_0]. This dataset represents the greyscale attenuation values in the scanned volume. labels: Holds the corresponding ground truth segmentation map, also as a three-dimensional array of int16 values with identical shape [x_0, y_0, z_0]. Each voxel is assigned a discrete label index representing the semantic class (e.g., background, weft, or fill yarn). masks: Provides binary masks as a one-hot channel for each semantic class, stored as a four-dimensional boolean array with shape [c, x_0, y_0, z_0], where c denotes the number of distinct classes. This dataset was used for training and evaluation. instances: Provides an instance label map for each yarn in weft and fill direction as a three-dimensional array [x_0, y_0, z_0] of uint16.用于训练3D-UNet以实现织物增强体语义分割（semantic segmentation）的数据集。已上传以下压缩包与XLSX文件： ├── 285-10_layer_00 │ ├── 285_10-layer_00_0 │ │ ├── 285_10-layer_00_0.seg.nrrd │ │ ├── 285_10-layer_00_0.vol.nrrd │ │ └── 285_10-layer_00_0.vol.seg.train.h5 │ ├── 285_10-layer_00_1 │ │ ├── 285_10-layer_00_1.seg.nrrd │ │ ├── 285_10-layer_00_1.vol.nrrd │ │ └── 285_10-layer_00_1.vol.seg.train.h5 │ └── 285_10-layer_00_3 │ ├── 285_10-layer_00_3.seg.nrrd │ ├── 285_10-layer_00_3.vol.nrrd │ └── 285_10-layer_00_3.vol.seg.eval.h5 ├── 285-10_layer_00.zip ├── 285-10_layer_01 │ ├── 285_10-layer_01_0 │ │ ├── 285_10-layer_01_0.seg.nrrd │ │ ├── 285_10-layer_01_0.vol.nrrd │ │ └── 285_10-layer_01_0.vol.seg.train.h5 │ ├── 285_10-layer_01_1 │ │ ├── 285_10-layer_01_1.seg.nrrd │ │ ├── 285_10-layer_01_1.vol.nrrd │ │ └── 285_10-layer_01_1.vol.seg.train.h5 │ └── 285_10-layer_01_3 │ ├── 285_10-layer_01_3.seg.nrrd │ ├── 285_10-layer_01_3.vol.nrrd │ └── 285_10-layer_01_3.vol.seg.eval.h5 ├── 285-10_layer_02.zip │ └── 285_10-layer_02_0 │ ├── 285_10-layer_02_0.seg.nrrd │ ├── 285_10-layer_02_0.vol.nrrd │ ├── 285_10-layer_02_0.vol.seg.eval.h5 │ ├── 285_10-layer_02_0.vol.seg.h5 │ └── 285_10-layer_02_0.vol.seg.train.h5 ├── 285-10_layer_03.zip │ └── 285_10-layer_03_0 │ ├── 285_10-layer_03_0.seg.nrrd │ ├── 285_10-layer_03_0.vol.nrrd │ ├── 285_10-layer_03_0.vol.seg.eval.h5 │ ├── 285_10-layer_03_0.vol.seg.h5 │ └── 285_10-layer_03_0.vol.seg.train.h5 └── 285_10_layer_machine_data.xlsx 各压缩包内含各阶段的标注数据集。在每个子文件夹中，均可找到对应体数据与标注分割结果的*.nrrd文件。在H5文件中，可获取如下数据： 体数据（volume）：包含原始X射线计算机断层扫描（X-ray Computed Tomography, CT）数据，以形状为[x_0, y_0, z_0]的int16类型三维数组存储。该数据集表征扫描体的灰度衰减值。 标签（labels）：存储对应的真实语义分割标注图，同样以形状为[x_0, y_0, z_0]的int16类型三维数组存储。每个体素（voxel）被赋予一个离散标签索引，用于表征其所属的语义类别（例如背景、纬纱或填充纱）。 掩码（masks）：为每个语义类别提供独热编码（one-hot）的二进制掩码，以形状为[c, x_0, y_0, z_0]的四维布尔数组存储，其中c代表不同语义类别的总数。该数据集可用于模型训练与评估。 实例（instances）：为纬向与填充方向的每一根纱线提供实例标签图，以形状为[x_0, y_0, z_0]的uint16类型三维数组存储。