Focal Loss for Dense Object Detection

TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
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

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	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/focal-loss-for-dense-object-detection/object-detection-on-coco)](https://paperswithcode.com/sota/object-detection-on-coco?p=focal-loss-for-dense-object-detection)`

ICCV 2017 • Tsung-Yi Lin • Priya Goyal • Ross Girshick • Kaiming He • Piotr Dollár

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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DENSE OBJECT DETECTION

OBJECT DETECTION

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#41 best model for Object Detection on COCO test-dev

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	COMPARE
Object Detection	COCO test-dev	RetinaNet (ResNeXt-101-FPN)	box AP	40.8	# 41	See all
Object Detection	COCO test-dev	RetinaNet (ResNeXt-101-FPN)	AP50	61.1	# 35	See all
Object Detection	COCO test-dev	RetinaNet (ResNeXt-101-FPN)	AP75	44.1	# 37	See all
Object Detection	COCO test-dev	RetinaNet (ResNeXt-101-FPN)	APS	24.1	# 30	See all
Object Detection	COCO test-dev	RetinaNet (ResNeXt-101-FPN)	APM	44.2	# 29	See all
Object Detection	COCO test-dev	RetinaNet (ResNeXt-101-FPN)	APL	51.2	# 41	See all
Object Detection	COCO test-dev	RetinaNet (ResNet-101-FPN)	box AP	39.1	# 51	See all
Object Detection	COCO test-dev	RetinaNet (ResNet-101-FPN)	AP50	59.1	# 44	See all
Object Detection	COCO test-dev	RetinaNet (ResNet-101-FPN)	AP75	42.3	# 45	See all
Object Detection	COCO test-dev	RetinaNet (ResNet-101-FPN)	APS	21.8	# 44	See all
Object Detection	COCO test-dev	RetinaNet (ResNet-101-FPN)	APM	42.7	# 35	See all
Object Detection	COCO test-dev	RetinaNet (ResNet-101-FPN)	APL	50.2	# 44	See all

Focal Loss for Dense Object Detection

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