CVPR 2019 [presentation (youtube)]
Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, Xiangyu Zhang, Carnegie Mellon University & Megvii Inc.
Table of Contents
Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced when labeling the bounding boxes. In this paper, we propose a novel bounding box regression loss for learning bounding box transformation and localization variance together. Our loss greatly improves the localization accuracies of various architectures with nearly no additional computation. The learned localization variance allows us to merge neighboring bounding boxes during non-maximum suppression (NMS), which further improves the localization performance. On MS-COCO, we boost the Average Precision (AP) of VGG-16 Faster R-CNN from 23.6% to 29.1%. More importantly, for ResNet-50-FPN Mask R-CNN, our method improves the AP and AP90 by 1.8% and 6.2% respectively, which significantly outperforms previous state-of-the-art bounding box refinement methods.
Citation
If you find the code useful in your research, please consider citing:
@InProceedings{klloss, author = {He, Yihui and Zhu, Chenchen and Wang, Jianren and Savvides, Marios and Zhang, Xiangyu}, title = {Bounding Box Regression With Uncertainty for Accurate Object Detection}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2019} }
Installation
Please find installation instructions for Caffe2 and Detectron in
INSTALL.md.When installing cocoapi, please use my fork to get AP80 and AP90 scores.
Testing
Inference without Var Voting (8 GPUs):
python2 tools/test_net.py -c configs/e2e_faster_rcnn_R-50-FPN_2x.yaml
You will get:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.385 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.578 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.412 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.412 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.499 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.321 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.553 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.680 Average Precision (AP) @[ IoU=0.60 | area= all | maxDets=100 ] = 0.533 Average Precision (AP) @[ IoU=0.70 | area= all | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.80 | area= all | maxDets=100 ] = 0.350 Average Precision (AP) @[ IoU=0.85 | area= all | maxDets=100 ] = 0.269 Average Precision (AP) @[ IoU=0.90 | area= all | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.95 | area= all | maxDets=100 ] = 0.032
Inference with Var Voting:
python2 tools/test_net.py -c configs/e2e_faster_rcnn_R-50-FPN_2x.yaml STD_NMS True
You will get:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.392 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.576 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.425 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.212 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.417 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.526 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.324 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.528 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.564 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.594 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.736 Average Precision (AP) @[ IoU=0.60 | area= all | maxDets=100 ] = 0.536 Average Precision (AP) @[ IoU=0.70 | area= all | maxDets=100 ] = 0.472 Average Precision (AP) @[ IoU=0.80 | area= all | maxDets=100 ] = 0.363 Average Precision (AP) @[ IoU=0.85 | area= all | maxDets=100 ] = 0.281 Average Precision (AP) @[ IoU=0.90 | area= all | maxDets=100 ] = 0.165 Average Precision (AP) @[ IoU=0.95 | area= all | maxDets=100 ] = 0.037
Training
python2 tools/train_net.py -c configs/e2e_faster_rcnn_R-50-FPN_2x.yaml
PyTorch re-implementations
Stronger-yolo-pytorch: yolov3 + KL-loss
FAQ
Please create a new issue.