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# FoveaBox: Beyond Anchor-based Object Detector
[ALGORITHM]
FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797):
Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object.
## Main Results
### Results on R50/101-FPN
| Backbone | Style | align | ms-train| Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
|:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
| R-50 | pytorch | N | N | 1x | 5.6 | 24.1 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219_223025.log.json) |
| R-50 | pytorch | N | N | 2x | 5.6 | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203_112043.log.json) |
| R-50 | pytorch | Y | N | 2x | 8.1 | 19.4 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203_134252.log.json) |
| R-50 | pytorch | Y | Y | 2x | 8.1 | 18.3 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205_112557.log.json) |
| R-101 | pytorch | N | N | 1x | 9.2 | 17.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219_011740.log.json) |
| R-101 | pytorch | N | N | 2x | 11.7 | - | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208_202059.log.json) |
| R-101 | pytorch | Y | N | 2x | 11.7 | 14.7 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208_203337.log.json) |
| R-101 | pytorch | Y | Y | 2x | 11.7 | 14.7 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208_202124.log.json) |
[1] *1x and 2x mean the model is trained for 12 and 24 epochs, respectively.* \
[2] *Align means utilizing deformable convolution to align the cls branch.* \
[3] *All results are obtained with a single model and without any test time data augmentation.*\
[4] *We use 4 GPUs for training.*
Any pull requests or issues are welcome.
## Citations
Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.
```latex
@article{kong2019foveabox,
title={FoveaBox: Beyond Anchor-based Object Detector},
author={Kong, Tao and Sun, Fuchun and Liu, Huaping and Jiang, Yuning and Shi, Jianbo},
journal={arXiv preprint arXiv:1904.03797},
year={2019}
}
```
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