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|