Doodleverse/Segmentation Zoo Res-UNet models for Aerial/nadir/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

<em><strong>Doodleverse/Segmentation Zoo Res-UNet models for Aerial/nadir/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</strong></em> These Residual-UNet models have been created using Segmentation Gym* using the following dataset**: Image size used by model: 1024 x 768 x 3 pixels classes: water other <br> <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. '.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. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.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`. Models may be ensembled. 3. '_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 4. '_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` 5. '.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` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong> <br> *Segmentation Gym: Buscombe, D., &amp; Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ****Doodleverse/分割模型库（Segmentation Zoo）残差UNet（Res-UNet）模型，用于RGB 1024×768高分辨率图像的航空/天底视角二分类（水体、非水体）分割任务** 本批残差UNet模型基于分割训练流水线（Segmentation Gym）构建，所用数据集信息如下： - 模型输入图像尺寸：1024×768×3像素 - 分类类别：水体、非水体 **文件说明** 每个模型对应5个同名根文件，具体如下： 1. `.json` 配置文件：该文件为分割训练流水线（Segmentation Gym）生成模型权重时所使用的配置文件，包含模型构建规则、训练数据集信息以及模型推理使用说明。该文件是核心配置载体，掌握它即可完整掌控整个Doodleverse工具链。 2. `.h5` 权重文件：该文件由分割训练流水线（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`seg_images_in_folder.py`脚本调用，支持多模型集成。 3. `_modelcard.json` 模型卡片文件：该JSON文件包含模型起源、训练配置、训练数据集等多维度描述字段。该文件与上述配置文件存在部分冗余信息，但不会被程序调用，仅作为重要元数据留存，需与其他模型相关文件一并保存，属于模型套件的组成部分。 4. `_model_history.npz` 模型训练历史文件：该NumPy归档文件存储了训练与验证损失、评估指标的NumPy数组数据，由分割训练流水线（Segmentation Gym）的`train_model.py`脚本生成。 5. `.png` 训练曲线文件：该PNG文件可视化展示了模型训练过程中的训练与验证损失、平均交并比（IoU）得分变化，为`.npz`文件中部分数据的可视化结果，由分割训练流水线（Segmentation Gym）的`train_model.py`脚本生成。 此外，`BEST_MODEL.txt` 文件存储了在验证集上取得最优损失与平均交并比得分的模型名称。 **参考文献** *分割训练流水线（Segmentation Gym）：Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 详见：https://github.com/Doodleverse/segmentation_gym