## Description
This is an implementation of [SparseInst](https://github.com/hustvl/SparseInst) based on [MMDetection](https://github.com/open-mmlab/mmdetection/tree/main), [MMCV](https://github.com/open-mmlab/mmcv), and [MMEngine](https://github.com/open-mmlab/mmengine).
**SparseInst** is a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation.
In contrast to region boxes or anchors (centers), SparseInst adopts a sparse set of **instance activation maps** as object representation, to highlight informative regions for each foreground objects.
Then it obtains the instance-level features by aggregating features according to the highlighted regions for recognition and segmentation.
The bipartite matching compels the instance activation maps to predict objects in a one-to-one style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves **40 FPS** and **37.9 AP** on COCO (NVIDIA 2080Ti), significantly outperforms the counter parts in terms of speed and accuracy.
## Usage
### Training commands
In MMDetection's root directory, run the following command to train the model:
```bash
python tools/train.py projects/SparseInst/configs/sparseinst_r50_iam_8xb8-ms-270k_coco.py
```
For multi-gpu training, run:
```bash
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=${NUM_GPUS} --master_port=29506 --master_addr="127.0.0.1" tools/train.py projects/SparseInst/configs/sparseinst_r50_iam_8xb8-ms-270k_coco.py
```
### Testing commands
In MMDetection's root directory, run the following command to test the model:
```bash
python tools/test.py projects/SparseInst/configs/sparseinst_r50_iam_8xb8-ms-270k_coco.py ${CHECKPOINT_PATH}
```
## Results
Here we provide the baseline version of SparseInst with ResNet50 backbone.
To find more variants, please visit the [official model zoo](https://github.com/hustvl/SparseInst#models).
| Backbone | Style | Lr schd | Mem (GB) | FPS | mask AP val2017 | Config | Download |
| :------: | :-----: | :-----: | :------: | :--: | :-------------: | :---------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| R-50 | PyTorch | 270k | 8.7 | 44.3 | 32.9 | [config](./configs/sparseinst_r50_iam_8xb8-ms-270k_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/sparseinst/sparseinst_r50_iam_8xb8-ms-270k_coco/sparseinst_r50_iam_8xb8-ms-270k_coco_20221111_181051-72c711cd.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/sparseinst/sparseinst_r50_iam_8xb8-ms-270k_coco/sparseinst_r50_iam_8xb8-ms-270k_coco_20221111_181051.json) |
## Citation
If you find SparseInst is useful in your research or applications, please consider giving a star 🌟 to the [official repository](https://github.com/hustvl/SparseInst) and citing SparseInst by the following BibTeX entry.
```BibTeX
@inproceedings{Cheng2022SparseInst,
title = {Sparse Instance Activation for Real-Time Instance Segmentation},
author = {Cheng, Tianheng and Wang, Xinggang and Chen, Shaoyu and Zhang, Wenqiang and Zhang, Qian and Huang, Chang and Zhang, Zhaoxiang and Liu, Wenyu},
booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}
```
## Checklist
- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
- [x] Finish the code
- [x] Basic docstrings & proper citation
- [x] Test-time correctness
- [x] A full README
- [x] Milestone 2: Indicates a successful model implementation.
- [x] Training-time correctness
- [ ] Milestone 3: Good to be a part of our core package!
- [ ] Type hints and docstrings
- [ ] Unit tests
- [ ] Code polishing
- [ ] Metafile.yml
- [ ] Move your modules into the core package following the codebase's file hierarchy structure.
- [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.
