# Long-Tailed Metrics and Object Detection in Camera Trap Datasets
## 3 Results
### The Long-Tailedness Metrics Results
According to the definition of small objects in the current benchmark datasets, MS COCO defines objects less than 32 \(\times\) 32 pixels as small; LVIS defines less than 32 \(\times\) 32 as small; between 32 \(\times\) 32-96 \(\times\) 96 are defined as medium; greater than 96 \(\times\) 96 are defined as large; TinyPerson defines objects between 20 \(\times\) 20-32 \(\times\) 32 as small objects, and 2 \(\times\) 2-20 \(\times\) 20 are defined as tiny. We divided the size of the objects of the camera trap datasets into eight ranges, which were 0-16 \(\times\) 16 (Very Small, VS), 16 \(\times\) 16-32 \(\times\) 32 (Small, S), 32 \(\times\) 32-64 \(\times\) 64 (Small Medium, SM), 64 \(\times\) 64-128 \(\times\) 128 (Medium, M), 128 \(\times\) 128-256 \(\times\) 256 (Medium Large), 256 \(\times\) 256-512 \(\times\) 512 (Large), 512 \(\times\) 512-1024 \(\times\) 1024 (Very Large, VL), and \(>\)1024 \(\times\) 1024 (Super Large, SL), to study the object/box-level scale imbalance of the camera trap datasets. The Gini Coefficients of the object/box-level scale imbalance of the three camera trap datasets were all greater than COCO and LVIS 1.0, so the object/box-level scale imbalance was also extremely long-tailed. Moreover, according to the areas of the objects shown in Table 5, we can conclude that there is a huge difference between camera trap and benchmark datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Gini Coef.** & **Gini Coef. of Area** \\ \hline SWG & 0.857 & 0.614 \\ WCS & 0.92 & 0.521 \\ CBL & 0.872 & 0.621 \\ COCO & 0.564 & 0.361 \\ LVIS 1.0 & 0.82 & 0.475 \\ \hline \hline \end{tabular} Note: GC of Area is the Gini Coefficient of object/box-level scale imbalance.  
\end{table} 
Table 4: The Gini coefficient of three object detection datasets, COCO, and LVIS 1.0. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **GC** & **IF** & **SD** & **MM** & **Max Size** & **Min Size** \\ \hline Orinoquía & 0.824 & 24,784 & 4496.7 & 10.64 & 24,784 & 1 \\ SWG & 0.875 & 234,736 & 30,178.71 & 42.68 & 234,736 & 1 \\ Island & 0.817 & 16,338 & 3076.35 & 9.2 & 16,338 & 1 \\ Karoo & 0.8 & 1698 & 363.33 & 14.68 & 1698 & 1 \\ Kgalagadi & 0.847 & 1378 & 253.77 & 7.35 & 1378 & 1 \\ Enonkishu & 0.761 & 1857 & 498.75 & 9.24 & 1857 & 1 \\ Camdeboo & 0.741 & 1167.67 & 744.8 & 9.49 & 3503 & 3 \\ Zebra & 0.773 & 1895 & 284.48 & 5.47 & 1895 & 1 \\ Kruger & 0.792 & 1379 & 220.93 & 8.94 & 1379 & 1 \\ Serengeti & 0.864 & 533,478 & 92,640.68 & 21.24 & 533,478 & 1 \\ WCS & 0.905 & 95,788 & 4598.39 & 33.36 & 95,788 & 1 \\ CBL & 0.872 & 19,728 & 2481.93 & 19.08 & 19,728 & 1 \\ CIFAR & 0.0 & 1.0 & 0.0 & 1.0 & 6000 & 6000 \\ ImageNet-LT & 0.524 & 256 & 139 & 1.58 & 1280 & 5 \\ LVIS 1.0 & 0.82 & 50,552 & 2789 & 11.1 & 50,552 & 1 \\ \hline \hline \end{tabular} Note: GC is the Gini Coefficient; IF is the Imbalance Factor; SD is the Standard Deviation; MM is the Mean/Median. The values of the largest and smallest number of samples of species were named “Max size’ and ’Min size’.  
\end{table} 
Table 3: The metrics results of 12 camera traps datasets, CIFAR, ImageNet-LT, and LVIS 1.0.