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

<em><strong>Doodleverse/Segmentation Zoo/Seg2Map Segformer models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images</strong></em> These Segformer 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)<strong>Doodleverse/分割模型库（Segmentation Zoo）/面向切萨皮克数据集的Seg2Map Segformer模型：针对512×512分辨率RGB高清图像的7分类分割任务</strong> 本系列Segformer模型基于切萨皮克土地覆盖（Chesapeake Land Cover）图像及其配套标注构建，通过分割训练库（Segmentation Gym）训练得到，所用数据集链接为：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. 乔木林冠/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. 不透水路面（impervious_road） 7. 无数据区域（no_data） ### 文件说明 针对每个模型，均包含5个同名根文件，具体如下： 1. **.json配置文件**：该文件为分割训练库（Segmentation Gym）训练模型时所用的配置文件，包含模型构建规则、调用数据集的方式以及推理预测的操作说明。该配置文件简洁实用，掌握其用法即可完全掌控整个Doodleverse生态。 2. **.h5权重文件**：由分割训练库（Segmentation Gym）的`train_model.py`脚本生成，存储了训练完成的模型参数权重，可通过分割训练库的`seg_images_in_folder.py`脚本调用，支持模型集成操作。 3. **_modelcard.json模型卡片文件**：该JSON文件包含模型起源、训练设置及所用数据集等元数据字段，与上述配置文件存在部分冗余信息，但无需被程序调用。该文件作为重要元数据，需与构成该模型的其他文件一同留存，因此被视为模型组件的一部分。 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损失、评估指标对应的NumPy数组，由分割训练库的`train_model.py`脚本生成。 5. **.png训练可视化图**：该PNG文件展示了模型训练过程中的训练/验证损失与平均交并比（mean IoU）曲线，是上述.npz文件中数据的子集，由`train_model.py`脚本生成。 此外，`BEST_MODEL.txt`文件记录了验证损失与平均交并比表现最优的模型名称。 ### 参考文献 * 分割训练库（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 ** 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. 2019年计算机视觉与模式识别会议（CVPR 2019）论文集