Doodleverse/Segmentation Zoo Res-UNet model for NOAA ERI/4-class segmentation of RGB 512x512 images

This Residual-UNet model is trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. The dataset is available here: https://doi.org/10.5281/zenodo.7268082 Models have been created using Segmentation Gym: Code - https://github.com/Doodleverse/segmentation_gym Paper - https://doi.org/10.1029/2022EA002332 The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 4, corresponding to 4 classes: water bare sediment vegetation development (roads, buildings, power lines, parking lots, etc.) Included here are 6 files with the same root name: '.json' config file: this is the file that was used by Segmentation Gym to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. '.h5' weights file: this is the file that was created by the Segmentation Gym function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym function `seg_images_in_folder.py`. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` '.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py` '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU

本残差UNet（Residual-UNet）模型基于1179组人工生成的分割标注与图像对进行训练，所用数据集为美国国家海洋和大气管理局（US National Oceanic and Atmospheric Administration, NOAA）在巴里飓风、德尔塔飓风、多里安飓风、佛罗伦斯飓风、艾达飓风、劳拉飓风、迈克尔飓风、萨莉飓风、泽塔飓风以及热带风暴戈登过后收集的应急响应影像（Emergency Response Imagery, ERI）。该数据集可通过以下链接获取：https://doi.org/10.5281/zenodo.7268082。
本模型通过Segmentation Gym构建：代码仓库地址为https://github.com/Doodleverse/segmentation_gym，相关论文DOI为https://doi.org/10.1029/2022EA002332。

该模型输入为512×512×3像素的图像，输出为512×512×4的张量，对应4个类别：水体、裸露沉积物、植被、开发用地（道路、建筑物、电力线路、停车场等）。

本次提供的文件包含6个同名根文件：
· .json格式配置文件：该文件为Segmentation Gym用于生成权重文件的配置文件，包含模型构建、所用数据集的相关说明，以及模型推理的使用指南。
· .h5格式权重文件：该文件由Segmentation Gym的`train_model.py`脚本生成，存储训练完成的模型参数权重，可通过Segmentation Gym的`seg_images_in_folder.py`脚本调用。
· _model_history.npz模型训练历史文件：该NumPy归档文件包含描述训练与验证损失及评估指标的NumPy数组，由Segmentation Gym的`train_model.py`脚本生成。
· .png格式模型训练损失与平均交并比（mean IoU）绘图：该PNG文件包含模型训练过程中的训练与验证损失、平均交并比得分的可视化曲线，其数据为.npz文件的子集，由Segmentation Gym的`train_model.py`脚本生成。
· TensorFlow「保存模型」格式的模型压缩包（.zip）：该压缩包为TensorFlow保存模型格式的模型文件，由Segmentation Gym的`utils/gen_saved_model.py`脚本生成。
· _modelcard.json模型卡片文件：该JSON文件包含用于完整描述模型来源、训练配置及所用数据集的字段。本文件与上文所述的配置文件存在部分冗余，二者均包含模型训练与部署的相关说明。模型卡片文件虽不被程序调用，但属于重要的元数据，需与构成该模型的其他文件一同留存，因此属于模型的组成部分。

此外，BEST_MODEL.txt文件存储了具有最优验证损失与平均交并比得分的模型名称。