[AAAI 2021]DropLoss for Long-Tail Instance Segmentation
[AAAI 2021] DropLoss for Long-Tail Instance Segmentation
Ting-I Hsieh*, Esther Robb*, Hwann-Tzong Chen, Jia-Bin Huang.
Association for the Advancement of Artificial Intelligence (AAAI), 2021
Figure: Measuring the performance tradeoff. Comparison
between rare, common, and frequent categories AP for baselines
and our method. We visualize the tradeoff for ‘common
vs. frequent’ and ‘rare vs. frequent’as a Pareto frontier, where
the top-right position indicates an ideal tradeoff between objectives.
DropLoss achieves an improved tradeoff between
object categories, resulting in higher overall AP.
This project is a pytorch implementation of DropLoss for Long-Tail Instance Segmentation. DropLoss improves long-tail instance segmentation by adaptively removing discouraging gradients to infrequent classes. A majority of the code is modified from facebookresearch/detectron2 and tztztztztz/eql.detectron2.
Progress
- Training code.
- Evaluation code.
- LVIS v1.0 datasets.
- Provide checkpoint model.
Installation
Requirements
- Linux or macOS with Python = 3.7
- PyTorch = 1.4 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
- OpenCV (optional but needed for demos and visualization)
Build Detectron2 from Source
gcc & g++ ≥ 5 are required. ninja is recommended for faster build.
After installing them, run:
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
# (add --user if you don't have permission)
# Or, to install it from a local clone:
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2
# Or if you are on macOS
CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ......
Remove the latest fvcore package and install an older version:
pip uninstall fvcore
pip install fvcore==0.1.1.post200513
LVIS Dataset
Following the instructions of README.md to set up the LVIS dataset.
Training
To train a model with 8 GPUs run:
cd /path/to/detectron2/projects/DropLoss
python train_net.py --config-file configs/droploss_mask_rcnn_R_50_FPN_1x.yaml --num-gpus 8
Evaluation
Model evaluation can be done similarly:
cd /path/to/detectron2/projects/DropLoss
python train_net.py --config-file configs/droploss_mask_rcnn_R_50_FPN_1x.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint
Citing DropLoss
If you use DropLoss, please use the following BibTeX entry.
@inproceedings{DBLP:conf/aaai/Ting21,
author = {Hsieh, Ting-I and Esther Robb and Chen, Hwann-Tzong and Huang, Jia-Bin},
title = {DropLoss for Long-Tail Instance Segmentation},
booktitle = {Proceedings of the Workshop on Artificial Intelligence Safety 2021
(SafeAI 2021) co-located with the Thirty-Fifth {AAAI} Conference on
Artificial Intelligence {(AAAI} 2021), Virtual, February 8, 2021},
year = {2021}
}