[arXiv] [BibTeX]

• A simple query-based model for fast instance segmentation.
• State-of-the-art real-time performance under the same setting.
• Support major segmentation datasets: COCO, Cityscapes, ADE20K.

• [2023/6] FastInst has been integrated into ModelScope. Try out the Online Demo at FastInst 🚀.
• [2023/4] We have released the code and checkpoints for FastInst. Welcome to your attention!

See installation instructions.

See Results.

See Preparing Datasets for FastInst.

See Getting Started.

This document provides a brief intro of the usage of FastInst.

Please see Getting Started with Detectron2 for full usage.

• You can download our pretrained models and evaluate them with the following commands.

  python train_net.py --eval-only --num-gpus 4 --config-file config_path MODEL.WEIGHTS /path/to/checkpoint_file

  for example, to evaluate our released the fastest model, you can copy the config path from the table, download the pretrained checkpoint into /path/to/checkpoint_file, and run

  python train_net.py --eval-only --num-gpus 4 --config-file configs/coco/instance-segmentation/fastinst_R50_ppm-fpn_x1_576.yaml MODEL.WEIGHTS /path/to/checkpoint_file

  which can reproduce the model.

• Use the above command without eval-only will train the model.

  python train_net.py --num-gpus 4 --config-file config_path

• For R101 backbone, you need to download and specify the path of the pretrained backbones with MODEL.WEIGHTS /path/to/pretrained_checkpoint. The download link can be found in the above table.

  python train_net.py --num-gpus 4 --config-file config_path MODEL.WEIGHTS /path/to/pretrained_checkpoint

• For R50-d-DCN backbone, you need to download and convert the pretrained backbones, and specify the path.

  python tools/convert-timm-to-d2.py /path/to/resnet50d_ra2-464e36ba.pth /path/to/resnet50d_ra2-464e36ba.pkl
  python train_net.py --num-gpus 4 --config-file config_path MODEL.WEIGHTS /path/to/resnet50d_ra2-464e36ba.pkl

FastInst is released under the MIT Licence.

If you find FastInst is useful in your research or applications, please consider giving us a star 🌟 and citing FastInst by the following BibTeX entry.

@article{he2023fastinst,
  title={FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation},
  author={He, Junjie and Li, Pengyu and Geng, Yifeng and Xie, Xuansong},
  journal={arXiv preprint arXiv:2303.08594},
  year={2023}
}

Sincerely thanks to these excellent opensource projects

• DETR
• Mask2Former