Doodleverse/Segmentation Gym SegFormer models for 2-class (other, sediment) segmentation of RGB aerial orthomosaic imagery

<strong>Doodleverse/Segmentation Gym SegFormer models for 2-class (other, sediment) segmentation of RGB aerial orthomosaic imagery</strong> 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 alluvial river corridors and associated labels. Models are designed to identify water, wood, sediment, and other in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here: <em>Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC.</em> Classes: {0=other, 1=sediment}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference File descriptions 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<br> 2. sample_images.zip contains a few example input files, for model testing References *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/Segmentation Gym 用于RGB航空正射影像二分类（其他、沉积物）的SegFormer模型** 本模型发布隶属于Doodleverse项目：https://github.com/Doodleverse。本残差UNet（Residual-UNet）模型数据集基于冲积河道及对应标注的RGB（红、绿、蓝）影像构建。模型旨在识别航空正射影像中的水体、木质覆盖物、沉积物及其他地物。本模型通过Segmentation Gym*基于下述发布的影像数据集构建： > Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., & Duda, J.J. (2018). 支持大坝拆除后埃尔瓦河五年沉积物预算与地貌动力学分析的数据：美国地质调查局数据发布, https://doi.org/10.5066/F7PG1QWC. 类别设置为：{0=其他，1=沉积物}。有关模型训练与推理部署的更多细节，请访问https://github.com/Doodleverse查询。 ## 文件说明 1. **.json格式配置文件**：该文件为Segmentation Gym*生成权重文件所使用的配置文件，包含模型构建、所用数据集的相关说明，以及模型预测的操作指南。该文件小巧实用，掌握它即可完全掌握整个Doodleverse工具链。 2. **.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。支持模型集成。 3. **_modelcard.json格式模型卡片文件**：该JSON文件包含若干字段，用于完整描述模型的起源、训练参数及所基于的数据集。本文件与前述的配置文件存在部分冗余内容，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直接调用，但属于关键元数据，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。 4. **_model_history.npz格式模型训练历史文件**：该NumPy归档文件存储了描述模型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。 5. **.png格式模型训练损失与平均交并比（mean IoU）曲线文件**：该PNG文件包含模型训练过程中的训练与验证损失曲线，以及平均交并比得分曲线，为上述.npz归档文件中数据的子集，由Segmentation Gym的`train_model.py`脚本生成。 ## 附加文件 1. `BEST_MODEL.txt`：存储验证损失与平均交并比表现最优的模型名称； 2. `sample_images.zip`：包含若干用于模型测试的示例输入影像文件。 ## 参考文献 *Segmentation Gym：Buscombe, D., & Goldstein, E. B. (2022). 可复现与复用的地球科学影像分割流程. 《地球与空间科学》, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 详见：https://github.com/Doodleverse/segmentation_gym