Published January 27, 2023 | Version v1.0
Dataset Open

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

  • 1. Marda Science LLC

Description

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

These Residual-UNet model data are based on the [DeepGlobe dataset](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset)

Models have been created using Segmentation Gym* using the following dataset**: https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset

Image size used by model:  512 x 512 x 3 pixels

classes:
1. urban
2. agricultural
3. rangeland
4. forest
5. water
6. bare
7. unknown

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

**Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D. and Raskar, R., 2018. Deepglobe 2018: A challenge to parse the earth through satellite images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 172-181).

 

 

Files

BEST_MODEL.txt

Files (418.0 MB)

Name Size Download all
md5:cdefe2d4148ecd46b3bc97c4350e2355
37 Bytes Preview Download
md5:75b5b8512242c3793296dacce7aa562a
54 Bytes Preview Download
md5:6ddb4e9adf67e701b09ba3ee5d717211
970 Bytes Preview Download
md5:4c8cb9ba2140e189bdf42e0d14da1284
69.4 MB Download
md5:8a1b7d14988489f3fadd7ddd442eca0c
4.0 kB Download
md5:c98dedbfb46984d96d254561d88a94d1
2.4 kB Preview Download
md5:fd3b783b875b704fe15c17fdd894b86d
233.3 kB Preview Download
md5:0ef3a9889aed8013743831312e1e1217
970 Bytes Preview Download
md5:784740b9d6965a57f2dc90762ffc8a4f
69.4 MB Download
md5:2037a6f10a27a9b2b5fbd8252e3fe6b0
2.1 kB Download
md5:3c243d01841bde303f65fe54c5dc5ea4
2.4 kB Preview Download
md5:79d2c22eb36c6f41a148b67d376237c9
253.1 kB Preview Download
md5:cf0d57df64c0456790f8b3aa7d1635e6
970 Bytes Preview Download
md5:6e5f03edbfa8493dab7c59c38955bb98
69.4 MB Download
md5:1077d8a3509df1d92bb540ccbfbf00d7
3.7 kB Download
md5:0199e2b820006fe07537b3cc5886d3cf
2.4 kB Preview Download
md5:a57491492e49eb869ba465f4bd1b868b
237.5 kB Preview Download
md5:b611dda91f5088217ccd164b910fcc16
969 Bytes Preview Download
md5:0f9e94ff01c44fa8593a51a9fb4052f1
69.4 MB Download
md5:383acfa5cb6e78ea773ba737b7a056ac
4.2 kB Download
md5:2eac797c91009f909aaca861da284af9
2.4 kB Preview Download
md5:c4435a1e0353927b0b17c69e6a921f76
216.0 kB Preview Download
md5:b2c2e8815b2075e574cfb9dad1ece34c
969 Bytes Preview Download
md5:2659c8b238e70d8fee3e3247063dd803
69.4 MB Download
md5:97e6e8e66e5e8c87671dffff8cec62eb
3.6 kB Download
md5:2042262df1e89785fb0d49d7ec539aa5
2.4 kB Preview Download
md5:8af7efcd63c323975e64db6a8ec364ee
246.9 kB Preview Download
md5:bd5126ecf0e793802d3e4a12dfe965a7
970 Bytes Preview Download
md5:366fc30797a108b4d7058319ec78e0c6
69.4 MB Download
md5:58fa80fd19a191276d651d3b047958db
3.7 kB Download
md5:22c779c9817606fccb599f177776bd34
2.3 kB Preview Download
md5:9c24675ce8c7b2e094b1344ec2fab573
252.5 kB Preview Download
md5:e5436b5dc9f2a6ccf888682f2aad13ae
3.0 kB Preview Download