Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images

<em><strong>Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images</strong></em> Models have been created using Segmentation Gym* using the following dataset**: https://arxiv.org/abs/1911.09296 These <em><strong>SegFormer </strong></em>model data are based on 1m spatial footprint images and associated labels of buildings. Image size used by model: 768 x 768 x 3 pixels classes:<br> other<br> building 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 **Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston. Creating xbd: A dataset for assessing building damage from satellite imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019. https://arxiv.org/abs/1911.09296**Doodleverse/分割模型库（Segmentation Zoo）/Seg2Map SegFormer 模型：用于对RGB 768×768高分辨率图像中的xBD/建筑物进行语义分割** 本系列模型基于分割训练框架（Segmentation Gym）构建，所用数据集来源如下**：https://arxiv.org/abs/1911.09296** 本SegFormer模型数据集基于空间分辨率为1米的影像及对应的建筑物标注信息构建。模型输入图像尺寸为768×768×3像素，类别包括： 其他（other） 建筑物（building） ### 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：该文件由分割训练框架（Segmentation Gym）用于生成权重文件，其中包含模型构建、所用数据集配置以及模型推理的相关说明。该文件为核心配置文件，掌握它即可完全掌握整个Doodleverse工具体系。 2. ".h5" 权重文件：该文件由分割训练框架（Segmentation Gym）的`train_model.py`脚本生成，存储经训练后的模型参数权重，可通过分割训练框架（Segmentation Gym）的`seg_images_in_folder.py`脚本调用。支持模型集成。 3. "_modelcard.json" 模型卡片文件：该JSON文件包含用于完整描述模型起源、训练配置及所属数据集的字段。该文件与上文提及的配置文件存在部分冗余信息，虽无需被程序调用，但属于重要的元数据文件，需与其他模型文件一并留存，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归档文件存储了描述模型训练与验证损失及评估指标的NumPy数组，由分割训练框架（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及平均交并比得分曲线，为上述".npz"文件中数据的子集，由分割训练框架（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 * 分割训练框架（Segmentation Gym）：Buscombe, D., & Goldstein, E. B. (2022). 可复现且可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 ** Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, 及 Matthew Gaston. 构建xBD：用于评估卫星影像中建筑物损毁情况的数据集. 见《IEEE/CVF计算机视觉与模式识别会议（CVPR）研讨会论文集》, 2019年6月. https://arxiv.org/abs/1911.09296