| # Feature Selective Anchor-Free Module for Single-Shot Object Detection | |
| <!-- [ALGORITHM] --> | |
| FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)). | |
| Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level. | |
| And this is how we implemented it. | |
| Only the anchor-free branch is released for its better compatibility with the current framework and less computational budget. | |
| In the original paper, feature maps within the central 0.2-0.5 area of a gt box are tagged as ignored. However, | |
| it is empirically found that a hard threshold (0.2-0.2) gives a further gain on the performance. (see the table below) | |
| ## Main Results | |
| ### Results on R50/R101/X101-FPN | |
| | Backbone | ignore range | ms-train| Lr schd |Train Mem (GB)| Train time (s/iter) | Inf time (fps) | box AP | Config | Download | | |
| |:----------:| :-------: |:-------:|:-------:|:------------:|:---------------:|:--------------:|:-------------:|:------:|:--------:| | |
| | R-50 | 0.2-0.5 | N | 1x | 3.15 | 0.43 | 12.3 | 36.0 (35.9) | | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) | | |
| | R-50 | 0.2-0.2 | N | 1x | 3.15 | 0.43 | 13.0 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json)| | |
| | R-101 | 0.2-0.2 | N | 1x | 5.08 | 0.58 | 10.8 | 39.3 (37.9) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json)| | |
| | X-101 | 0.2-0.2 | N | 1x | 9.38 | 1.23 | 5.6 | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json)| | |
| **Notes:** | |
| - *1x means the model is trained for 12 epochs.* | |
| - *AP values in the brackets represent those reported in the original paper.* | |
| - *All results are obtained with a single model and single-scale test.* | |
| - *X-101 backbone represents ResNext-101-64x4d.* | |
| - *All pretrained backbones use pytorch style.* | |
| - *All models are trained on 8 Titan-XP gpus and tested on a single gpu.* | |
| ## Citations | |
| BibTeX reference is as follows. | |
| ```latex | |
| @inproceedings{zhu2019feature, | |
| title={Feature Selective Anchor-Free Module for Single-Shot Object Detection}, | |
| author={Zhu, Chenchen and He, Yihui and Savvides, Marios}, | |
| booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, | |
| pages={840--849}, | |
| year={2019} | |
| } | |
| ``` | |