LiCAM: Long-Tailed Instance Segmentation with Real-Time Classification Accuracy Monitoring

The overview of the LiCAM.

This project is a pytorch implementation of LiCAM: Long-Tailed Instance Segmentation with Real-Time Classification Accuracy Monitoring

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 2 GPUs run:

cd /projects/LiCAM/licam
python train_net.py --config-file configs/licam_resampling_mask_rcnn_R_50_FPN_1x.yaml --num-gpus 2

Evaluation

Model evaluation can be done similarly:

cd /projects/LiCAM/licam
python train_net.py --config-file configs/licam_resampling_mask_rcnn_R_50_FPN_1x.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint

Citation

you can cite our work if this work is useful for you.

@article{ye2022licam, title={LiCAM: Long-Tailed Instance Segmentation with Real-Time Classification Accuracy Monitoring}, author={Ye, Rongguang and Guo, Yantong and Shuai, Xian and Ye, Rongye and Jiang, Siyang and Jiang, Hui}, journal={Journal of Circuits, Systems and Computers}, pages={2350032}, year={2022}, publisher={World Scientific} }