Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images

<em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on Chesapeake Land Cover images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://lila.science/datasets/chesapeakelandcover Image size used by model: 512 x 512 x 3 pixels classes:<br> water<br> tree_canopy_forest<br> low_vegetation_field<br> barren land<br> impervious_other<br> impervious_road<br> no_data <br> File descriptions 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 References<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 **Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N. Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019)**Doodleverse/分割模型库（Segmentation Zoo）/用于切萨皮克数据集的Seg2Map残差UNet（Res-UNet）模型：针对512×512分辨率RGB高清图像的7类语义分割任务** 本批残差UNet模型数据基于切萨皮克土地覆盖（Chesapeake Land Cover）图像及其配套标注，通过分割训练框架Segmentation Gym*构建，所用数据集为：https://lila.science/datasets/chesapeakelandcover。 模型输入图像尺寸为512×512×3像素，共包含7个分类类别： 1. 水体（water） 2. 乔木冠层/森林（tree_canopy_forest） 3. 低矮植被/农田（low_vegetation_field） 4. 裸地（barren land） 5. 其他不透水面（impervious_other） 6. 道路不透水面（impervious_road） 7. 无数据区域（no_data） ### 文件说明 每个模型对应5个同名根文件名的文件： 1. **.json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，以及模型推理的操作指南。该文件简洁实用，掌握它即可掌握整个Doodleverse框架。 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文件包含用于完整描述模型来源、训练参数及所用数据集的字段。该文件与上文提及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，但属于重要的元数据，需与构成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、评估指标的NumPy数组，由Segmentation Gym的`train_model.py`脚本生成。 5. **.png训练可视化文件**：该PNG文件包含模型训练过程中训练集与验证集的损失曲线及平均交并比（mean IoU）得分曲线，是上述.npz文件中数据的子集，由Segmentation Gym的`train_model.py`脚本生成。 此外，`BEST_MODEL.txt`文件存储了验证损失与平均交并比表现最优的模型名称。 ### 参考文献 * Segmentation Gym框架：Buscombe D, Goldstein E B. 可复现且可复用的地球科学图像语义分割流程[J]. Earth and Space Science, 2022, 9: e2022EA002332. https://doi.org/10.1029/2022EA002332 详见：https://github.com/Doodleverse/segmentation_gym ** Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N. 基于多分辨率数据的大规模高分辨率土地覆盖制图[C]//2019年计算机视觉与模式识别会议（CVPR 2019）论文集。