@@ -18,7 +18,7 @@ Produce (Rocha et al. 2010) and (b) Grozi-3.2k (George and Floerkemeier 2014), a
et al. 2018), (d) Freiburg Groceries (Jund et al. 2016), and (e) Sku110k (Goldman et al. 2019). We introduce the Loccount task,
aiming to localize groups of objects of interest with the numbers of instances, which is natural in grocery store scenarios, shown
in the last row, i.e., (f), (g), (h), (i), and (j). The numbers on the right hand indicate the numbers of object instances enclosed in
the bounding boxes. Different colors denotes different object categories. Best viewed in color and zoom in.](https://isrc.iscas.ac.cn/gitlab/research/locount-dataset/-/tree/master/Images/dataset-comparison.jpg)
the bounding boxes. Different colors denotes different object categories. Best viewed in color and zoom in.](Images/dataset-comparison.jpg)
## Locount dataset
To solve the above issues, we collect a large-scale object localization and counting dataset at 28 different stores and apartments, which consists of 50,394 images with the JPEG image resolution of 1920x1080 pixels.
