tatigabru / xview2_solution

2nd place solution for Xview2 challenge https://xview2.org/

Xview2 2nd place solution

Data preparation

Download and extract dataset from https://xview2.org/dataset and put it to data directory (or whatever)

Generate Masks

Run python generate_polygons.py --input data/train This script will generate pixel masks from json files.

Training

Dockerfile has all the required libraries.

Most of the hyperparameters for training are defined by json files, see configs directory.

Other parameters are passed directly to train scripts.

Localization and classification networks are trained separately

• train_localization.py - to train binary segmentation models. By default O0 opt level (FP32) is used for Apex due to unstable loss during training.
• train.py - to train classification models. By default O1 opt level (Mixed-Precision) is used for Apex as multiclass loss FocalLossWithDice is stable in mixed precision .

Architectures

For localization network ordinary U-Net like network was used with pretrained DPN92 and Densenet161 encoders (see models/unet.py for U-Nets Zoo)

For classification Siamese-UNet was used with shared encoder weights (see models/siamese_unet.py for Siamese U-Nets Zoo)