This is the code for the paper "Few Shot Instance Segmentation with Class Hierarchy Mining".
This code is based on Detectron2 and parts of MTFA's source code.
We advise the users to create a new conda environment and install our source code in the same way as the detectron2 source code. See INSTALL.md.
After setting up the dependencies, installation should simply be:
pip install -e . in this folder.
All our configs can be found in the configs/ours directory.
The first training stage is: configs/ours/mask_rcnn_R_101_FPN_base_220k.yaml
1shot,5shot and 10_shot SMS+LR configs for the all classes are named as such:
configs/ours/fs/SMS_LR_{shot_number}shot.yaml
Pre-trained weights are reported in Table 4 of the main paper here
To run the training, the tools/run_train.py script is used. Run it with -h to get all available options
Alternatively, we provide the scripts in bash to easily produce the experiments.
We use the same datasets folder used in Detectron2 and MTFA. Download and unzip the cocosplit folder here.
Also, setup a coco directory in datasets, exactly the same way as MTFA. For this, just download COCO2014 train + val and place them in trainval, similarly download COCO2014 test.
See prepare_coco_few_shot.py for generating them manually, but the cocosplit folder provided above already includes the splits