Skip to content
master
Switch branches/tags
Code
This branch is up to date with master.

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
lib
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Deformable ConvNets is initially described in an arxiv tech report.

R-FCN is initially described in a NIPS 2016 paper.

Soft-NMS is initially described in an arxiv tech report.

Our goal was to test Soft-NMS with a state-of-the-art detector, so Deformable-R-FCN was trained on 800x1200 size images with 15 anchors. Multi-Scale testing was also added with 6 scales. Union of all boxes at each scale was computed before performing NMS. Please note that the repository does not include the scripts for multi-scale testing as I just cache the boxes for each different scale and do NMS separately. The scales used in multi-scale testing were as follows, [(480, 800), (576,900), (688, 1100), (800,1200), (1200, 1600), (1400, 2000)].

The trained model can be downloaded from here.

training data testing data mAP mAP@0.5 mAP@0.75 mAP@S mAP@M mAP@L Recall
Baseline D-R-FCN coco trainval coco test-dev 35.7 56.8 38.3 15.2 38.8 51.5
D-R-FCN, ResNet-v1-101, NMS coco trainval coco test-dev 37.4 59.6 40.2 17.8 40.6 51.4 48.3
D-R-FCN, ResNet-v1-101, SNMS coco trainval coco test-dev 38.4 60.1 41.6 18.5 41.6 52.5 53.8
D-R-FCN, ResNet-v1-101, MST, NMS coco trainval coco test-dev 39.8 62.4 43.3 22.6 42.3 52.2 52.9
D-R-FCN, ResNet-v1-101, MST, SNMS coco trainval coco test-dev 40.9 62.8 45.0 23.3 43.6 53.3 60.4

About

Deformable Convolutional Networks + MST + Soft-NMS

Topics

Resources

License

Releases

No releases published

Packages

No packages published