Abstract
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 lo-
cations. 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. In this paper,
we investigate why this is the case. We discover that the ex-
treme foreground-background class imbalance encountered
during training of dense detectors is the central cause. We
propose to address this class imbalance by reshaping the
standard cross entropy loss such that it down-weights the
loss assigned to well-classified examples. Our novel Focal
Loss focuses training on a sparse set of hard examples and
prevents the vast number of easy negatives from overwhelm-
ing the detector during training. To evaluate the effective-
ness of our loss, we design and train a simple dense detector
we call RetinaNet. Our results show that when trained with
the focal loss, RetinaNet is able to match the speed of pre-
vious one-stage detectors while surpassing the accuracy of
all existing state-of-the-art two-stage detectors.
