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[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

Image 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}
  }

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Implementation of DropLoss for Long-Tail Instance Segmentation in Pytorch

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