DropLoss for Long-Tail Instance Segmentation

Ting-I Hsieh�, Esther Robb�, Hwann-Tzong Chen, Jia-Bin Huang

This project is a pytorch implementation of DropLoss for Long-Tail Instance Segmentation. A majority of the code is modified from facebookresearch/detectron2 and tztztztztz/eql.detectron2.

What we are doing and going to do

• 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 is optional but needed by demo and visualization

Build Detectron2 from Source

gcc & g++ ≥ 5 are required. ninja is recommended for faster build. After having 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 ......

Please remove the latest fvcore package and install older version. run:

pip uninstall fvcore
pip install fvcore==0.1.1.post200513

LVIS Dataset

Following the instruction 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.

Coming soon