From cb92f637751b51b21f2611ca96d2e4b1b98061c3 Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Tue, 24 Sep 2019 15:38:41 +0200 Subject: [PATCH] Fixed dashes Signed-off-by: Jim Martens --- body.tex | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/body.tex b/body.tex index d10e7d1..6286f65 100644 --- a/body.tex +++ b/body.tex @@ -443,7 +443,7 @@ SSD network are the predictions with class confidences, offsets to the anchor box, anchor box coordinates, and variance. The model loss is a weighted sum of localisation and confidence loss. As the network has a fixed number of anchor boxes, every forward pass creates the same -number of detections - 8732 in the case of SSD 300x300. +number of detections---8732 in the case of SSD 300x300. Notably, the object proposals are made in a single run for an image - single shot. @@ -960,8 +960,8 @@ averaging was not reported in their paper. There is no visible impact of entropy thresholding on the object detection performance for vanilla SSD. This indicates that the network has almost no uniform or close to uniform predictions, the vast majority of predictions -has a high confidence in one class - including the background. -However, the entropy plays a larger role for the Bayesian variants - as +has a high confidence in one class---including the background. +However, the entropy plays a larger role for the Bayesian variants---as expected: the best performing thresholds are 1.0, 1.3, and 1.4 for micro averaging, and 1.5, 1.7, and 2.0 for macro averaging. In all of these cases the best threshold is not the largest threshold tested. A lower threshold likely @@ -986,7 +986,7 @@ have the same number of observations everywhere before the entropy threshold. Af Without NMS 79\% of observations are left. Irrespective of the absolute number, this discrepancy clearly shows the impact of non-maximum suppression and also explains a higher count of false positives: more than 50\% of the original observations were removed with NMS and -stayed without - all of these are very likely to be false positives. +stayed without---all of these are very likely to be false positives. A clear distinction between micro and macro averaging can be observed: recall is hardly effected with micro averaging (0.300) but goes down equally with macro averaging (0.229). For micro averaging, it does