Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images
收藏资源简介：
Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images These Residual-UNet models have been created using Segmentation Gym* Image size used by model: 1024 x 768 x 3 pixels classes: water other <br> <strong>File descriptions</strong> 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 <strong>References</strong><br> *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 Zoo 模型库中适用于v2版本PCMSC/平面相机（planecam）二分类（水体、非水体）分割任务的Res-UNet（残差UNet）模型，用于处理1024×768分辨率的RGB高分辨率图像。本系列残差UNet模型基于Segmentation Gym*（分割训练框架）构建。 模型输入图像尺寸：1024×768×3像素 类别：水体、非水体 <strong>文件说明</strong> 每个模型对应5个共享同一根文件名的文件： 1. ".json" 配置文件：该文件由Segmentation Gym用于生成权重文件，其中包含模型构建、训练所用数据集的相关说明，以及模型预测的操作指南。该文件简洁实用，掌握它即可完全掌握整个Doodleverse项目。 2. ".h5" 权重文件：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" 模型卡片文件：该JSON文件包含描述模型起源、训练配置及训练所用数据集的相关字段。其与上文提及的用于模型训练和部署的配置文件存在部分冗余信息，虽不被程序调用，但属于重要的元数据，需与构成模型的其他文件一并留存，因此也被视为模型的组成部分。 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" 模型训练损失与平均交并比（mean IoU）曲线文件：该PNG文件展示了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，是".npz"文件中部分数据的可视化结果，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。 <strong>参考文献</strong> *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 详见：https://github.com/Doodleverse/segmentation_gym
