Published February 6, 2023 | Version v1.0
Dataset Open

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

  • 1. Marda Science LLC

Description

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

Models have been created using Segmentation Gym* using the following dataset**: https://arxiv.org/abs/1911.09296

These SegFormer model data are based on 1m spatial footprint images and associated labels of undamaged/damaged buildings.

Image size used by model: 768 x 768 x 3 pixels

classes:
no-damage
minor-damage
major-damage
unclassified

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
*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

**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

Files

BEST_MODEL.txt

Files (61.0 MB)

Name Size Download all
md5:0965287d7233f17e7746c57af3f33b7f
27 Bytes Preview Download
md5:feb8549463e79c7336dc19f41cc78be7
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2.9 kB Preview Download
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969 Bytes Preview Download
md5:7945c84f88adc4a7a905029fe691e0d2
15.1 MB Download
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882 Bytes Download
md5:345b70f7841a62554047715490d61782
85.0 kB Preview Download
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969 Bytes Preview Download
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15.1 MB Download
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1.3 kB Download
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967 Bytes Preview Download
md5:91a420438f449661613c7bd2953faba3
15.1 MB Download
md5:a912b1b856ae3495b541e62d103af763
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md5:d1c1d07eed8c4381394f71f2c6317af6
15.1 MB Download
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md5:34e47d003fe0f3c15db4c9966e36d59a
1.9 kB Preview Download
md5:d223d66e5dd250546882d61cb138bbdf
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