The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far... (read more)
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#41 best model for
Object Detection
on COCO test-dev
TASK | DATASET | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK | COMPARE |
---|---|---|---|---|---|---|
Object Detection | COCO test-dev | RetinaNet (ResNeXt-101-FPN) | box AP | 40.8 | # 41 | |
Object Detection | COCO test-dev | RetinaNet (ResNeXt-101-FPN) | AP50 | 61.1 | # 35 | |
Object Detection | COCO test-dev | RetinaNet (ResNeXt-101-FPN) | AP75 | 44.1 | # 37 | |
Object Detection | COCO test-dev | RetinaNet (ResNeXt-101-FPN) | APS | 24.1 | # 30 | |
Object Detection | COCO test-dev | RetinaNet (ResNeXt-101-FPN) | APM | 44.2 | # 29 | |
Object Detection | COCO test-dev | RetinaNet (ResNeXt-101-FPN) | APL | 51.2 | # 41 | |
Object Detection | COCO test-dev | RetinaNet (ResNet-101-FPN) | box AP | 39.1 | # 51 | |
Object Detection | COCO test-dev | RetinaNet (ResNet-101-FPN) | AP50 | 59.1 | # 44 | |
Object Detection | COCO test-dev | RetinaNet (ResNet-101-FPN) | AP75 | 42.3 | # 45 | |
Object Detection | COCO test-dev | RetinaNet (ResNet-101-FPN) | APS | 21.8 | # 44 | |
Object Detection | COCO test-dev | RetinaNet (ResNet-101-FPN) | APM | 42.7 | # 35 | |
Object Detection | COCO test-dev | RetinaNet (ResNet-101-FPN) | APL | 50.2 | # 44 |