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: 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.
| 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 | |
| R-50 | pytorch | N | N | 2x | - | - | 36.9 | model | |
| R-50 | pytorch | Y | N | 2x | - | - | 37.9 | model | |
| R-50 | pytorch | Y | Y | 2x | - | - | 40.1 | model | |
| R-101 | pytorch | N | N | 1x | 9.4 | - | 38.5 | model | |
| R-101 | pytorch | N | N | 2x | - | - | 38.5 | model | |
| R-101 | pytorch | Y | N | 2x | - | - | 39.4 | model | |
| R-101 | pytorch | Y | Y | 2x | - | - | 41.9 | model |
[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.
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}
}