## 3 Image Datasets  
In this section, we briefly describe the BEE1, BEE2_1S, BEE2_2S, and BEE3 datasets obtained in the course of our video-based CEBM research in 2014-2020, because these datasets were used in this investigation. We refer interested readers to our previous article [19] for details on how these datasets were curated. All images were obtained from 30 s 24-frames-per-second videos obtained with BeePi monitors deployed on live bee hives [2]. Each dataset is split into _training_, _testing_, and _validation_ subsets. The training and testing datasets include the images used for model fitting (i.e., for training and periodically testing classifiers); the _validation_ datasets include the images not used either in training or testing, but only in the final model selection. BEE1 includes 54,391 32 \(\times\) 32 images obtained in 2017. If an image contains at least one complete bee, it is labeled as BEE; if it contains no complete bee or only a small part of a complete bee, it is labeled as NO_BEE. The training dataset includes 19,082 (35%) BEE and 19,057 (35%) NO_BEE images; the testing dataset includes 6362 (12%) BEE and 6362 NO_BEE (12%) images; the validation dataset consists of 1801 (3%) BEE and 1718 (3%) NO_BEE images. BEE2 contains 112 879 images obtained in 2018. We call a video _1-super_ when it is captured by a camera mounted on a hive that consists of one deep super. We refer to a video as _2-super_ when it is captured by a camera mounted on a hive that consists of two deep supers. BEE2 is split into two subsets: BEE2_15 (58,201 150 \(\times\) 150 images) and BEE2_25 (54,674 90 \(\times\) 90 images). All images are labeled as BEE or NO_BEE. In BEE2_15, 8266 (14%) BEE and 27 108 (46%) NO_BEE images are selected for training; 2828 (5%) BEE images and 9035 (16%) NO_BEE images--for testing; and 8298 (14%) BEE images and 2666 (5%) NO_BEE images--for validation. In BEE2_2S, 12 982 (24%) BEE images and 15,983 (29%) NO_BEE images are for training; 4194 (8%) BEE images and 5327 (10%) NO_BEE images--for testing; and 6823 (12%) BEE and 9369 (17%) NO_BEE images--for validation. BEE3 contains 65,023 64 \(\times\) 64 images obtained in 2019. All images are labeled as BEE, NO_BEE, and SHADOW_BEE. The category SHADOW_BEE includes images that, in the judgment of a human classifier, contains shadows of bees. In BEE3, 12,948 (20%) BEE, 12,857 (20%) NO_BEE, and 12,006 (18%) SHADOW_BEE images were randomly selected for training; 4236 (7%) BEE, 4242 (6%) NO_BEE, and 3993 (6%) SHADOW_BEE--for testing; and 5187 (7%) BEE, 5204 (8%) NO_BEE, 4350 (8%) SHADOW_BEE--for validation. In this article, we refer to BEE3 datasets without SHADOW_BEE images as BEE3_NS (i.e., BEE3_NO_SHADOW), and to the complete BEE3 dataset with SHADOW_BEE images as BEE3_S (i.e., BEE3_SHADOW). BEE4 is a new dataset which we have not previously published. This dataset was obtained from BEE2_2S in 2021 by re-labeling 996 (1.8%) images in BEE2_2S, because during a 2020 labeling verification a human evaluator decided that these images were mislabeled: some bee images were mislabeled as NO_BEE and some images with bee shadows were mislabeled as BEE. Table 1 shows the distribution of images in BEE4. We also used the CIFAR-10 dataset [8] to test our algorithms on a more general image dataset. CIFAR-10 consists of 60,000 \(32\times 32\) color images distributed among 10 different classes. We used the testing subset of CIFAR-10 that includes 10,000 images with 1000 images per class for model selection, and randomly divided the training set into 40,000 training images (4000 images per class) and 10,000 testing images (1000 images per class) for model fitting. \begin{table}
\begin{tabular}{c c c c} \hline  & **BEE** & **NO\_BEE** & **Total** \\ \hline Training & 12,696 (23\%) & 16,269 (30\%) & 28,965 (53\%) \\ \hline Testing & 4142 (7\%) & 5379 (10\%) & 9521 (17\%) \\ \hline Validation & 7243 (14\%) & 8949 (16\%) & 16,192 (30\%) \\ \hline Total & 24,081 (44\%) & 30,597 (56\%) & 54,678 (100\%) \\ \hline \end{tabular}
\end{table}