Comments (4)
Thanks for your interest. The code from the BeyondRGB paper uses multiple data sources (DSM + RGB), it is a multimodal network. I do not have the time right now to update the PyTorch code with this model but I hope to do it in the near future.
from deepnetsforeo.
Yes. I know in your paper you use DSM+RGB. However, in the following table, you achieve about 89.4 and 90% on each dataset, when the SegNet was used in your experiment. When I use the Pytorch code in your repository, it only gets about 86%.
from deepnetsforeo.
In addition, what are your training dataset and validation dataset in Vaihingen and Potsdam? You did not explain it in your article.
Are these your training dataset and testing dataset in your article?
segnet_vaihingen_128x128_fold1_iter_60000.caffemodel (112.4 Mo) (backup link): pre-trained model on the ISPRS Vaihingen dataset (trained on tiles 1, 3, 5, 7, 11, 13, 15, 17, 21, 23, 26, 28, 30, validated on tiles 32, 34, 37).
potsdam_rgb_128_fold1_iter_80000.caffemodel (112.4 Mo) (backup link) : pre-trained model on the ISPRS Potsdam dataset (RGB tiles, trained on (3, 12), (6, 8), (4, 11), (3, 10), (7, 9), (4, 10), (6, 10), (7, 7), (5, 10), (7, 11), (2, 12), (6, 9), (5, 11), (6, 12), (7, 8), (2, 10), (6, 7), (6, 11), validated on tile (2, 11), (7, 12), (3, 11), (5, 12), (7, 10), (4, 12)).
Best,
from deepnetsforeo.
IIRC final results reported on the article are trained on the whole training set and metrics are computed using the ISPRS official test set. Different test splits can have different metrics.
from deepnetsforeo.
Related Issues (20)
- prediction on my image HOT 1
- Typos to load Potsdam data HOT 3
- Problem to get the dataset HOT 1
- Low accuracy during training HOT 2
- Operation on cpu HOT 5
- PyTorch 4.0 compliance
- Data set present in the link does not match the code HOT 1
- Value Error: Axes don't match array! HOT 1
- problems with SGDSolver HOT 1
- Using images with with nodata pixels HOT 4
- Error with train: invalid index of a 0-dim tensor
- nDSM DATA of Vaihingen Dataset HOT 3
- Initialization of V-fusenet HOT 2
- downloading dataset HOT 5
- code for the fusion of DSM data and RGB image HOT 1
- Datasets HOT 1
- DSM, NDSM and NDVI Part HOT 1
- error during model training HOT 7
- the use of another dataset
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