Codes in this folder is an implementation of InstaBoost for yolact of this version.
Prepare and run yolact according to ORIGINAL_README.md.
Besides, InstaBoost should be installed before running the code.
We suggest using pip to install InstaBoost.
pip install instaboost Integrating InstaBoost into yolact is quite simple. Only few lines should be modified to put InstaBoost into use.
data/coco.py, train.py and eval.py should be changed.
coco.py/line 11 imports InstaBoost.
coco.py/line 68 assigns the value of is_train.
coco.py/line 116-121 These lines are originally located at line 139-146. They are put here because the InstaBoost function get_new_data needs both the annotation and the image.
coco.py/line 123-124 checks if it's training or evaluating. When training, the program runs the InstaBoost function and gets new image and annotation. Both the variables target and img are acquired using the official COCO Python API.
train.py/line 134 and train.py/line 140 assign True to the variable of is_train.
eval.py/line 1006 assigns False to the variable of is_train.
For your conveinience of evaluation and comparison, we report the evaluation number on COCO val below. In our paper, the numbers are obtained from test-dev.
If you use this toolbox or benchmark in your research, please cite this project.
@article{Fang2019InstaBoost,
author = {Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu},
title = {InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting},
journal={arXiv preprint arXiv:1908.07801},
year = {2019}
}