PanopticFCN

Fully Convolutional Networks for Panoptic Segmentation

Yanwei Li, Hengshuang Zhao, Xiaojuan Qi, Liwei Wang, Zeming Li, Jian Sun, Jiaya Jia

This project provides an implementation for the paper "Fully Convolutional Networks for Panoptic Segmentation" based on Detectron2. Panoptic FCN is a conceptually simple, strong, and efficient framework for panoptic segmentation, which represents and predicts foreground things and background stuff in a unified fully convolutional pipeline.

Installation

This project is based on Detectron2, which can be constructed as follows.

  • Install Detectron2 following the instructions.
  • Setup the dataset following the structure.
  • Copy this project to /path/to/detectron2/projects/PanopticFCN

Training

To train a model with 8 GPUs, run:

cd /path/to/detectron2
python3 projects/PanopticFCN/train.py --config-file <config.yaml> --num-gpus 8

For example, to launch PanopticFCN training (1x schedule) with ResNet-50 backbone on 8 GPUs,
one should execute:

cd /path/to/detectron2
python3 projects/PanopticFCN/train.py --config-file projects/PanopticFCN/configs/PanopticFCN-R50-1x.yaml --num-gpus 8

Evaluation

To evaluate a pre-trained model with 8 GPUs, run:

cd /path/to/detectron2
python3 projects/PanopticFCN/train.py --config-file <config.yaml> --num-gpus 8 --eval-only MODEL.WEIGHTS /path/to/model_checkpoint

Results

We provide the results on COCO val set with pretrained models. FPS is measured on a single V100 GPU.

Method Backbone Sched PQ SQ RQ AP mIoU FPS download
PanopticFCN R50 1x 41.1 79.8 49.9 32.2 41.5 12.4 model | metrics
PanopticFCN-400 R50 3x 41.0 81.0 49.6 30.7 43.6 22.5 model | metrics
PanopticFCN-512 R50 3x 42.3 81.1 51.2 32.4 43.2 19.8 model | metrics
PanopticFCN-600 R50 3x 42.7 81.2 51.5 33.6 43.9 17.5 model | metrics
PanopticFCN R50 3x 43.6 81.4 52.5 34.4 43.6 12.8 model | metrics
PanopticFCN* R50 3x 44.2 81.7 52.9 35.6 43.9 9.3 model | metrics

A faster version is also provided with higher threshold but similar PQ results, which shares the same model with the corresponding normal one. This version could be suitable for you if the final panoptic results are taken into consideration only.

Method Backbone Sched PQ SQ RQ AP mIoU FPS download
PanopticFCN R50 1x 41.1 79.8 49.9 30.2 41.4 13.6 model | metrics
PanopticFCN-400 R50 3x 40.8 81.1 49.4 28.9 43.5 26.1 model | metrics
PanopticFCN-512 R50 3x 42.3 81.1 51.2 30.7 43.2 22.0 model | metrics
PanopticFCN-600 R50 3x 42.7 80.8 51.4 31.6 43.9 19.1 model | metrics
PanopticFCN R50 3x 43.6 81.4 52.5 32.4 43.6 13.5 model | metrics
PanopticFCN* R50 3x 44.2 81.7 52.9 33.4 43.9 9.7 model | metrics

Citing PanopticFCN

Consider cite PanopticFCN in your publications if it helps your research.

@article{li2020panopticfcn,
  title={Fully Convolutional Networks for Panoptic Segmentation},
  author={Yanwei Li, Hengshuang Zhao, Xiaojuan Qi, Liwei Wang, Zeming Li, Jian Sun, and Jiaya Jia},
  journal={arXiv preprint arXiv:2012.00720},
  year={2020}
}

Consider cite this project in your publications if it helps your research.

@misc{PanopticFCN,
    author = {Yanwei Li},
    title = {PanopticFCN},
    howpublished = {\url{https://github.com/yanwei-li/PanopticFCN}},
    year ={2020}
}

GitHub