## 4 Results and Discussion
### Comparative Experiments with Snapshot Serengeti Dataset  
To further validate the validity of the model, the experiments on a public dataset called Snapshot Serengeti dataset [24] was conducted. It was acquired with 225 camera-traps placed in Serengeti National Park, Tanzania. More than one million sets of pictures were acquired, and each set contains 1-3 photographs. This dataset allows the computer science community to study and overcome the challenges presented in camera trapping frameworks. Additionally, it also helps ecology researchers and educators in their camera-trap studies. In this work, 26 classes were selected form the dataset for recognition (they are listed in Table 5). [PERSON] et al. [25] adopted six very deep convolutional neural networks (ConvNets) to train the Snapshot Serengeti dataset and obtained satisfactory results. The six models are AlexNet [26], VGG Net [27], GoogLenet [28], ResNet-50, ResNet-101, and ResNet-152, which are the state-of-the-art in object recognition. Accuracy in the validation set is used as a performance metric. Top-1 (when the correct class is the most probable class) and Top-5 (when the correct class is within the five most probable classes) accuracies are presented to show how well the model performs in ranking possible species. The best results in [PERSON]'s experiments were obtained using ResNet-101, showing 88.9% for Top-1 and 98.1% for Top-5. Using the same dataset, this paper employs SE-ResNeXt50 and SE-ResNeXt101 to compare with [PERSON]'s best results. Figure 10 shows the results of experiments, including Top-1 and Top-5 results alongside each experiment. The results reach an accuracy of 88.8% and 97.1% for Top-1 and Top-5 respectively when the model is SE-ResNeXt50. When the model is SE-ResNeXt101, the results are 95.3% and 98.8% for Top-1 and Top-5 respectively. Figure 10 shows that the Top-1 and Top-5 of SE-ResNeXt50 have a very subtle difference from those of ResNet101. When trained with SE-ResNeXt101, the accuracy of Top-1 reaches 95.3%, 6.4% higher than that of ResNet101. The value of Top-5 is also slightly higher than that of ResNet101, which proves that SE-ResNeXt performs better than other state-of-the-art models in object recognition, when using the same dataset. Table 6 compares the accuracy of each species using the ResNet-101 in [PERSON]'s experiments and the SE-ResNeXt101 in this paper. Table 6 shows that SE-ResNeXt101 has an accuracy of more than 90% for all 26 species. The lowest accuracy is 91.7% on Wildebebeest and the highest is 99.8% on Human. As for ResNet-101, the lowest accuracy is 65% on Grants gazelle and the highest is 99.5% on Guinea fowl. For Grants gazelle, the accuracy of SE-ResNeXt101 reaches 96.2%, surpassing that of ResNet-101. According to statistics, the average recognition accuracies of SE-ResNeXt101 and ResNet-101 for 26 species are 95.3% and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **\# Images** & **Species** & **\# Images** \\ \hline Wildebebeest & 212,973 & Lion, female and cub & 8773 \\ Zebra & 181,043 & Eland & 7395 \\ Thomson’s gazelle & 116,421 & Topi & 6247 \\ Buffalo & 34,684 & Baboon & 4618 \\ Human & 26,557 & Reedbuck & 4141 \\ Elephant & 25,294 & Dik disk & 3364 \\ Guinea fowl & 23,023 & Cheetah & 3354 \\ Giraffe & 22,439 & Hippocampus & 3231 \\ Impala & 22,281 & Lion male & 2413 \\ Warthog & 22,041 & Kori Bustard & 2042 \\ Grants gazelle & 21,340 & Ostrich & 1945 \\ Hartebeest & 15,401 & Secretary bird & 1302 \\ Spotted hyena & 10,242 & Jackal & 1207 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Set of species selected from the Snapshot Serengeti dataset. 88.9%, respectively. It is shown that when the species characteristics are obvious, for example Giraffe and Human, the performance of the two models is similar. In thirteen classes, the SE-ResNeXt101 outperformed the ResNet-101 and the maximum difference reached 31.2% when recognizing Grants gazelle. The color of the Grants gazelle's hide is similar to that of the background, which makes the recognition more difficult.