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hellock and xvjiarui Rewrite dataset evaluation (#2042)
* move evaluation part to datasets

* use dataset.evaluate() in testing scripts

* remove redundant codes

* fix some log formats

* update config with evaludation

* move evaluation below dataset

* remove and fix test_robustness

Co-authored-by: Jerry Jiarui XU <>
Latest commit 1d7b0a7 Feb 11, 2020

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 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
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.

  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},
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