Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery

Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery

This model release is part of the Doodleverse: https://github.com/Doodleverse

These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels.

Models have been created using Segmentation Gym* using an as-yet unpublished dataset of images and associated label images. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference

Classes: {0=other, 1=water}

File descriptions

There are two models; v7 has been trained from scratch, and v8 has been fine-tuned using hyperparameter adjustment. 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, 

1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU
2. sample_images.zip contains a few example input files, for model testing

References

*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 See: https://github.com/Doodleverse/segmentation_gym

Doodleverse/Segmentation Gym 残差UNet（Res-UNet）模型，用于对CoastCam潮位时序栈影像进行二类（水体、其他）分割。

本模型发布隶属于Doodleverse项目：https://github.com/Doodleverse

该残差UNet模型数据基于海岸RGB（红、绿、蓝）影像及配套标注数据。

模型通过Segmentation Gym*构建，所用数据集尚未正式发表。如需了解模型训练细节与推理使用方法，请访问https://github.com/Doodleverse 获取更多信息。

类别定义：{0=其他，1=水体}

### 文件说明
本次发布包含两款模型：v7为从零开始训练的模型，v8为经超参数调优后微调得到的模型。每个模型对应5个同名根文件：
1. .json配置文件：该文件为Segmentation Gym*创建权重文件时所使用的配置文件，包含模型构建、所用数据集信息，以及模型预测的使用指南。该配置文件是整个Doodleverse生态的核心组件，掌握该文件即可熟练使用整个工具链。
2. .h5权重文件：该文件由Segmentation Gym*的train_model.py脚本生成，存储了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。支持多模型集成。
3. _modelcard.json模型卡片文件：该JSON文件包含了模型起源、训练设置及基准数据集等元数据字段。其部分内容与上述配置文件存在冗余，但模型卡片文件虽不被程序直接调用，却是至关重要的元数据，需与其他模型文件一并留存，作为模型整体的一部分。
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集的损失值及评估指标的NumPy数组，由Segmentation Gym的train_model.py脚本生成。
5. .png模型训练损失与平均交并比（mean IoU）曲线：该PNG文件可视化了模型训练过程中的训练与验证损失、平均交并比得分，为上述.npz文件中数据的子集，由train_model.py脚本生成。

#### 附加文件
1. BEST_MODEL.txt：存储了验证损失与平均交并比表现最优的模型名称
2. sample_images.zip：包含若干示例输入文件，用于模型测试

### 参考文献
*Segmentation Gym：Buscombe, D., & Goldstein, E. B. (2022). 适用于地球科学影像分割的可复现、可复用流水线. 地球与空间科学, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 项目主页：https://github.com/Doodleverse/segmentation_gym