We used OpenCV (Bradski, 2008) for image reading and writing. We use Kornia (Riba et al., 2019) for GPU-based image augmentations. We used PyTorch (Paszke, 2018; version 1.4.0 or greater) for all deep-learning models. Other figures were generated in Matplotlib (Caswell, 2021). CNN diagram in Figure 1 was generated using PlotNeuralNet (Iqbal, 2018). We used scikit-learn (Pedregosa, 2021) for evaluation metrics, along with custom Python code. For training, we used one of the following Nvidia GPUs: GeForce 1080Ti, Titan RTX, Quadro RTX6000, or Quadro RTX8000. Inference speed was evaluated on a computer running Ubuntu 18.04, an AMD Ryzen Threadripper 2950 X CPU, an Nvidia Titan RTX, an Nvidia Geforce 1080Ti, a Samsung 970 Evo hard disk, and 128 GB DDR4 memory. We implemented DeepEthogram in the Python programming language (version 3.7 or later; Rossum et al., 2010). We used PyTorch Lightning for training (Falcon, 2019).

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( A ) Workflows for supervised behavior labeling. ( B ) Ethogram schematic. Bottom: ethogram with human labels. Top: example images from Mouse-Ventral1 dataset. Example shown is from Mouse-Ventral1 dataset. Left: a common traditional approach based on manual labeling. ( C ) DeepEthogram-fast model schematic. Middle: workflow with DeepEthogram. Right: Schematic of expected scaling of user time for each workflow. Left: a sequence of 11 frames is converted into 10 optic flows. Images have been cropped, brightened, and converted to grayscale for clarity. Dark colors indicate which behavior is present. Middle: the center frame and the stack of 10 optic flows are converted into 512-dimensional representations via deep convolutional neural networks (CNNs). Example images are from the Fly dataset. Right: these features are converted into probabilities of each behavior via the sequence model.

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( A ) Overall accuracy for each model size and dataset. All results are from the test sets only. *p≤0.05, **p≤0.01, ***p≤0.001, repeated measures ANOVA with a post-hoc Tukey’s honestly significant difference test. ( D ) Similar to ( C ), but for Mouse-Ventral2. Error bars indicate mean ± SEM across five random splits of the data (three for Sturman-EPM). ( E ) Similar to ( C ), but for Mouse-Openfield. Gray bars indicate shuffle (Materials and methods). ( B ) Similar to ( A ), except for overall F1 score. ( H ) Similar to ( C ), binary options but for Sturman-EPM. ( G ) Similar to ( D ), but for Mouse-Social. ( J ) Similar to ( C ), but for Sturman-OFT. ( F ) Similar to ( D ), but for Mouse-Homecage. Model and shuffle were compared with paired t-tests with Bonferroni correction. ( C ) F1 score for DeepEthogram-medium for individual behaviors on the Mouse-Ventral1 dataset. ( K ) Similar to ( D ), but for Fly dataset. ( I ) Similar to ( C ), but for Sturman-FST. the percent of frames in the training set with the given behavior. ( N ) Model accuracy on frames for which two human labelers agreed or disagreed. human performance: p=0.067, paired t-test. Paired t-tests with Bonferroni correction. Circles indicate the average performance across splits for behaviors in datasets with multiple human labels. ( P ) Ethogram examples. Dark color indicates the behavior is present. ( L ) F1 score on individual behaviors (circles) for DeepEthogram-medium vs. Examples were chosen to be similar to the model’s average by behavior. Each circle is one behavior for one split of the data. The accuracy and F1 score for each behavior, and the overall accuracy and F1 scores are shown. ( O ) Similar to ( N ), but for F1. Bottom: DeepEthogram-medium predictions.