# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 4 Experiments and Results
### Statistical Experiments on WAID  
To comprehensively understand the characteristics and distribution of the WAID dataset, we conducted statistical experiments. This included analyzing the instance data of different categories in the test set and plotting the distribution of label box sizes, aspect ratios, and object distributions. Through statistical analysis, we obtained information about the instance count of different categories, the spatial distribution of wildlife, and the distribution of label box sizes. This helps us gain deeper insights into the dataset's  
Figure 5: Comparison of the original YOLOv7 backbone network after improvement. structure, characteristics, and patterns, providing valuable information for further model training and wildlife ecological research. Additionally, we applied the T-SNE (t-distributed Stochastic Neighbor Embedding) method [79], a common dimensionality reduction technique used for visualizing high-dimensional data. T-SNE maps high-dimensional data into a lower-dimensional space, aiding visualization in two- or three-dimensional planes. In this experiment, we first preprocessed the data, handling missing values and standardizing and normalizing the data to ensure consistency across dimensions. Next, we applied the T-SNE algorithm, setting parameters such as learning rate, to perform dimensionality reduction and visualization. We plotted the reduced data points on a two-dimensional plane using a scatter plot, with different colors distinguishing different categories. The instance counts of different categories in the dataset and the distribution of the dataset are illustrated in Figure 6. The histogram of animal category counts (top left) indicates an uneven distribution of sample quantities in the dataset, presenting a significant challenge to the model. Examining the distribution of label box widths and heights (top right) as well as the variable histograms plotted based on label box width and height (bottom right), it is evident that the label boxes in the dataset tend to be small. This suggests that the WAID dataset contains a relatively high number of small target wildlife instances. In the plot of label box center positions in the x and y dimensions (bottom left), darker regions indicate a higher concentration of label boxes. This suggests that the distribution of wildlife positions in the images is relatively dense. The visualization results of the data after dimensionality reduction using the T-SNE method are shown in Figure 7. In the figure, we can clearly observe that samples from the six categories (Sheep, Cattle, Seal, Camelus, Kiang, Zebra) exhibit relatively distinct clustering structures. Each category forms a tight cluster in the plot, and there are noticeable gaps between the clusters, indicating their high distinctiveness in the feature space. Specifically observing the clustering of different animal categories, we can draw the following observations: the samples from the Camelus, Cattle, and Kiang categories are relatively independent clusters, each forming a closely grouped cluster, and they are relatively isolated from other categories. This suggests that they exhibit significant differences and unique semantic features in the feature space, making them distinctly separated in the reduced space. Samples from the Zebra category form a relatively independent cluster, but it is closely located to samples from the Sheep and Seal categories. This implies that there is some similarity between Zebra and Sheep and Seal in certain features that cause them to cluster together in the reduced dimensionality space. Additionally, a few Zebra samples appear in the connecting region between Sheep and Seal clusters, suggesting that these samples possess attributes that lie between the two categories and share some common properties. Samples from the Seal and Sheep categories are extremely close in the reduced space, almost adjacent to each other.