CenterNet
Introduction
@article{zhou2019objects,
title={Objects as Points},
author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp},
booktitle={arXiv preprint arXiv:1904.07850},
year={2019}
}
Results and models
Backbone |
DCN |
Mem (GB) |
Box AP |
Flip box AP |
Config |
Download |
ResNet-18 |
N |
3.45 |
25.9 |
27.3 |
config |
model | log |
ResNet-18 |
Y |
3.47 |
29.5 |
30.9 |
config |
model | log |
Note:
- Flip box AP setting is single-scale and
flip=True
.
- Due to complex data enhancement, we find that the performance is unstable and may fluctuate by about 0.4 mAP. mAP 29.4 ~ 29.8 is acceptable in ResNet-18-DCNv2.
- Compared to the source code, we refer to CenterNet-Better, and make the following changes
- fix wrong image mean and variance in image normalization to be compatible with the pre-trained backbone.
- Use SGD rather than ADAM optimizer and add warmup and grad clip.
- Use DistributedDataParallel as other models in MMDetection rather than using DataParallel.