configs/foveabox/README.md

+# FoveaBox: Beyond Anchor-based Object Detector
+
+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) | Train time (s/iter) | Inf time (fps) | box AP | Download |
+|:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
+| R-50      | pytorch | N       | N       | 1x      | 5.7      | -                   |                | 36.5   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/foveabox/fovea_r50_fpn_4gpu_1x_20190905-3b185a5d.pth) |
+| R-50      | pytorch | N       | N       | 2x      | -        | -                   |                | 36.9   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/foveabox/fovea_r50_fpn_4gpu_2x_20190905-4a07f6e0.pth) |
+| R-50      | pytorch | Y       | N       | 2x      | -        | -                   |                | 37.9   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/foveabox/fovea_align_gn_r50_fpn_4gpu_2x_20190905-3e6bc82f.pth) |
+| R-50      | pytorch | Y       | Y       | 2x      | -        | -                   |                | 40.1   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/foveabox/fovea_align_gn_ms_r50_fpn_4gpu_2x_20190905-13374f33.pth) |
+| R-101     | pytorch | N       | N       | 1x      | 9.4      | -                   |                | 38.5   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/foveabox/fovea_r101_fpn_4gpu_1x_20190905-80ff93a6.pth) |
+| R-101     | pytorch | N       | N       | 2x      | -        | -                   |                | 38.5   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/foveabox/fovea_r101_fpn_4gpu_2x_20190905-d9c99fb1.pth) |
+| R-101     | pytorch | Y       | N       | 2x      | -        | -                   |                | 39.4   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/foveabox/fovea_align_gn_r101_fpn_4gpu_2x_20190905-407ddad6.pth) |
+| R-101     | pytorch | Y       | Y       | 2x      | -        | -                   |                | 41.9   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/foveabox/fovea_align_gn_ms_r101_fpn_4gpu_2x_20190905-936c7277.pth) |
+
+[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 NVIDIA Tesla V100 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.
+```
+@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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