# FastInst
**Repository Path**: lmw0320/FastInst
## Basic Information
- **Project Name**: FastInst
- **Description**: 目前最佳的分割模型
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-11
- **Last Updated**: 2023-10-11
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation
[[`arXiv`](https://arxiv.org/abs/2303.08594)] [[`BibTeX`](#CitingFastInst)]
### Features
* 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.
***
## Updates
* [2023/6] FastInst has been integrated into [ModelScope](https://www.modelscope.cn/home). Try out the Online Demo at [FastInst](https://modelscope.cn/models/damo/cv_resnet50_fast-instance-segmentation_coco/summary) 🚀.
* [2023/4] We have released the code and checkpoints for FastInst. Welcome to your attention!
## Installation
See [installation instructions](INSTALL.md).
## Getting Started
See [Results](#results).
See [Preparing Datasets for FastInst](datasets/README.md).
See [Getting Started](#getting-started-1).
***
# Results
### COCO Instance Segmentation
# Getting Started
This document provides a brief intro of the usage of FastInst.
Please
see [Getting Started with Detectron2](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md) for
full usage.
#### Evaluate our pretrained models
* You can download our pretrained models and evaluate them with the following commands.
```sh
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
```sh
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.
#### Train FastInst to reproduce results
* Use the above command without `eval-only` will train the model.
```sh
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](#results).
```sh
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.
```sh
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
```
## LICNESE
FastInst is released under the [MIT Licence](LICENSE).
## Citing FastInst
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.
```BibTeX
@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}
}
```
## Acknowledgement
Sincerely thanks to these excellent opensource projects
* [DETR](https://github.com/facebookresearch/detr)
* [Mask2Former](https://github.com/facebookresearch/Mask2Former)