Training

Current this code base works for Python version >= 3.5.

Please install the dependencies: pip install -r requirements.txt

First, you could register and download the Deep Globe "Land Cover Classification" dataset here: https://competitions.codalab.org/competitions/18468

Then please sequentially finish the following steps:

1. ./train_deep_globe_global.sh
2. ./train_deep_globe_global2local.sh
3. ./train_deep_globe_local2global.sh

The above jobs complete the following tasks:

• create folder "saved_models" and "runs" to store the model checkpoints and logging files (you could configure the bash scrips to use your own paths).
• step 1 and 2 prepare the trained models for step 2 and 3, respectively. You could use your own names to save the model checkpoints, but this requires to update values of the flag path_g and path_g2l.

Evaluation

1. Please download the pre-trained models for the Deep Globe dataset and put them into folder "saved_models":

• fpn_deepglobe_global.pth
• fpn_deepglobe_global2local.pth
• fpn_deepglobe_local2global.pth

2. Download (see above "Training" section) and prepare the Deep Globe dataset according to the train.txt and crossvali.txt: put the image and label files into folder "train" and folder "crossvali"
3. Run script ./eval_deep_globe.sh