The code is an unofficial pytorch implementation of SOLO: Segmenting Objects by Locations

The code is based on mmdetection. Please check Install.md for installation instructions.

Follows the same way as mmdetection.

single GPU: python tools/train.py configs/solo/r50.py

multi GPU (for example 8): ./tools/dist_train.sh configs/solo/r50.py 8

The code only implements the simplest version of SOLO:

• without CoordConv
• using vanilla SOLO instead of Decoupled SOLO
• 3x training schedule
• using the default FPN featuremaps: in the paper it is with different specific strides and instance scale selection
• implemented the simplest mask-nms: as the authors did not describe it in detail in the paper, the implemented nms is slow, will improve it in the future.
• still in progress

After training 36 epochs on the coco dataset using the resnet-50 backbone, the mAP is 0.221 on COCO val2017 dataset:

Both good and bad results: