| # SCNet | |
| ## Introduction | |
| <!-- [ALGORITHM] --> | |
| We provide the code for reproducing experiment results of [SCNet](https://arxiv.org/abs/2012.10150). | |
| ``` | |
| @inproceedings{vu2019cascade, | |
| title={SCNet: Training Inference Sample Consistency for Instance Segmentation}, | |
| author={Vu, Thang and Haeyong, Kang and Yoo, Chang D}, | |
| booktitle={AAAI}, | |
| year={2021} | |
| } | |
| ``` | |
| ## Dataset | |
| SCNet requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. | |
| The directory should be like this. | |
| ```none | |
| mmdetection | |
| ├── mmdet | |
| ├── tools | |
| ├── configs | |
| ├── data | |
| │ ├── coco | |
| │ │ ├── annotations | |
| │ │ ├── train2017 | |
| │ │ ├── val2017 | |
| │ │ ├── test2017 | |
| | | ├── stuffthingmaps | |
| ``` | |
| ## Results and Models | |
| The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) | |
| | Backbone | Style | Lr schd | Mem (GB) | Inf speed (fps) | box AP | mask AP | TTA box AP | TTA mask AP | Config | Download | | |
| |:---------------:|:-------:|:-------:|:--------:|:---------------:|:------:|:-------:|:----------:|:-----------:|:------:|:------------:| | |
| | R-50-FPN | pytorch | 1x | 7.0 | 6.2 | 43.5 | 39.2 | 44.8 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py) | [model](https://drive.google.com/file/d/1K5_8-P0EC43WZFtoO3q9_JE-df8pEc7J/view?usp=sharing) \| [log](https://drive.google.com/file/d/1ZFS6QhFfxlOnDYPiGpSDP_Fzgb7iDGN3/view?usp=sharing) | | |
| | R-50-FPN | pytorch | 20e | 7.0 | 6.2 | 44.5 | 40.0 | 45.8 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/15VGLCt5-IO5TbzB4Kw6ZyoF6QH0Q511A/view?usp=sharing) \| [log](https://drive.google.com/file/d/1-LnkOXN8n5ojQW34H0qZ625cgrnWpqSX/view?usp=sharing) | | |
| | R-101-FPN | pytorch | 20e | 8.9 | 5.8 | 45.8 | 40.9 | 47.3 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r101_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/1aeCGHsOBdfIqVBnBPp0JUE_RSIau3583/view?usp=sharing) \| [log](https://drive.google.com/file/d/1iRx-9GRgTaIDsz-we3DGwFVH22nbvCLa/view?usp=sharing) | | |
| | X-101-64x4d-FPN | pytorch | 20e | 13.2 | 4.9 | 47.5 | 42.3 | 48.9 | 44.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/1YjgutUKz4TTPpqSWGKUTkZJ8_X-kyCfY/view?usp=sharing) \| [log](https://drive.google.com/file/d/1OsfQJ8gwtqIQ61k358yxY21sCvbUcRjs/view?usp=sharing) | | |
| ### Notes | |
| - Training hyper-parameters are identical to those of [HTC](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc). | |
| - TTA means Test Time Augmentation, which applies horizonal flip and multi-scale testing. Refer to [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py). | |