Cleanlab/segmentation-tutorial

该数据集包含用于cleanlab教程的图像分割掩码：[图像分割](https://docs.cleanlab.ai/stable/tutorials/segmentation.html)。数据集展示了如何使用cleanlab来识别和纠正语义分割数据集中的标签问题，其中图像中的每个像素都被分配了一个类别标签。数据集包含30张带有分割掩码的图像，图像尺寸为1088 x 1920像素。文件包括`given_masks.npy`（真实分割掩码）和`predicted_masks.npy`（模型预测的分割掩码）。数据集创建用于教育目的，演示cleanlab在检测分割数据集问题方面的能力，如错误标记的区域/像素、边界注释错误、不一致的分割掩码、低质量的预测和错误标记的类别。

This dataset contains image segmentation masks used in the cleanlab tutorial: [Image Segmentation](https://docs.cleanlab.ai/stable/tutorials/segmentation.html). The dataset demonstrates how to use cleanlab to identify and correct label issues in semantic segmentation datasets, where each pixel in an image is assigned a class label. It includes 30 images with segmentation masks of size 1088 x 1920 pixels. Files consist of `given_masks.npy` (ground truth segmentation masks) and `predicted_masks.npy` (model predicted segmentation masks). The dataset was created for educational purposes to demonstrate cleanlabs capabilities for detecting issues in segmentation datasets, such as incorrectly labeled regions/pixels, boundary annotation errors, inconsistent segmentation masks, poor quality predictions, and mislabeled classes.