Our goal was to take a set of video frames as input and predict the probability that each behavior of interest occurs on a given frame. This task of automated behavior labeling presents several challenges that framed our solution. First, in many cases, the behavior of interest occurs in a relatively small number of video frames, and the accuracy must be judged based on correct identification of these low-frequency events. For example, if a behavior of interest is present in 5% of frames, an algorithm could guess that the behavior is ‘not present’ on every frame and still achieve 95% overall accuracy. Critically, however, it would achieve 0% accuracy on the frames that matter, and an algorithm does not know a priori which frames matter. Second, ideally a method should perform well after being trained on only small amounts of user-labeled video frames, including across different animals, and thus require little manual input. Third, a method should be able to identify the same behavior regardless of the position and orientation of the animal when the behavior occurs. Fourth, methods should require relatively low computational resources in case researchers do not have access to large compute clusters or top-level GPUs.

We modeled our approach after temporal action localization methods used in computer vision aimed to solve related problems (Zeng, 2019; Xie et al., 2019; Chao, 2018; El-Nouby and Taylor, 2018). The overall architecture of our solution includes (1) estimating motion (optic flow) from a small snippet of video frames, (2) compressing a snippet of optic flow and individual still images into a lower dimensional set of features, and (3) using a sequence of the compressed features to estimate the probability of each behavior at each frame in a video (Figure 1C). We implemented this architecture using large, deep CNNs. First, one CNN is used to generate optic flow from a set of images. We incorporate optic flow because some behaviors are only obvious by looking at the animal’s movements between frames, such as distinguishing standing still and walking. We call this CNN the flow generator (Figure 1C, Figure 1—figure supplement 1). We then use the optic flow output of the flow generator as input to a second CNN to compress the large number of optic flow snippets across all the pixels into a small set of features called flow features (Figure 1C). Separately, we use a distinct CNN, which takes single video frames as input, to compress the large number of raw pixels into a small set of spatial features, which contain information about the values of pixels relative to one another spatially but lack temporal information (Figure 1C). We include single frames separately because some behaviors are obvious from a single still image, such as identifying licking just by seeing an extended tongue. Together, we call these latter two CNNs feature extractors because they compress the large number of raw pixels into a small set of features called a feature vector (Figure 1C). Each of these feature extractors is trained to produce a probability for each behavior on each frame based only on their input (optic flow or single frames). We then fuse the outputs of the two feature extractors by averaging (Materials and methods). To produce the final probabilities that each behavior was present on a given frame – a step called inference – we use a sequence model, which has a large temporal receptive field and thus utilizes long timescale information (Figure 1C). We use this sequence model because our CNNs only ‘look at’ either 1 frame (spatial) or about 11 frames (optic flow), but when labeling videos, humans know that sometimes the information present seconds ago can be useful for estimating the behavior of the current frame. The final output of DeepEthogram is a $\left[T,K\right]$ matrix, in which each element is the probability of behavior k occurring at time t. We threshold these probabilities to get a binary prediction for each behavior at each timepoint, with the possibility that multiple behaviors can occur simultaneously (Figure 1B).

For the flow generator, we use the MotionNet (Zhu et al., 2017) architecture to generate 10 optic flow frames from 11 images. For the feature extractors, we use the ResNet family of models (He et al., 2015; Hara et al., 2018) to extract both flow features and spatial features. Finally, we use TGM (Piergiovanni and Ryoo, 2018) models as the sequence model to perform the ultimate classification. Each of these models has many variants with a large range in the number of parameters and the associated computational demands. We therefore created three versions of DeepEthogram that use variants of these models, with the aim of trading off accuracy and speed: DeepEthogram-fast, DeepEthogram-medium, and DeepEthogram-slow. DeepEthogram-fast uses TinyMotionNet (Zhu et al., 2017) for the flow generator and ResNet18 (He et al., 2015) for the feature extractors. It has the fewest parameters, the fastest training of the flow generator and feature extractor models, the fastest inference time, and the smallest requirement for computational resources. As a tradeoff for this speed, DeepEthogram-fast tends to have slightly worse performance than the other versions (see below). In contrast, DeepEthogram-slow uses a novel architecture TinyMotionNet3D for its flow generator and 3D-ResNet34 (He et al., 2015; Simonyan and Zisserman, 2014; Hara et al., 2018) for its feature extractors. It has the most parameters, the slowest training and inference times, and the highest computational demands, but it has the capacity to produce the best performance. DeepEthogram-medium is intermediate and uses MotionNet (Zhu et al., 2017) and ResNet50 (He et al., 2015) for its flow generator and feature extractors. All versions of DeepEthogram use the same sequence model. All variants of the flow generators and feature extractors are pretrained on the Kinetics700 video dataset (Carreira et al., 2019), so that model parameters do not have to be learned from scratch (Materials and methods). TGM networks represent the state of the art on various action detection benchmarks as of 2019 (Piergiovanni and Ryoo, 2018). However, recent work based on multiple temporal resolutions (Feichtenhofer et al., 2019; Kahatapitiya and Ryoo, 2021), graph convolutional networks (Zeng, 2019), and transformer architectures (Nawhal and Mori, 2021) has exceeded this performance. We carefully chose DeepEthogram’s components based on their performance, parameter count, and hardware requirements. DeepEthogram as a whole and its component parts are not aimed to be the state of the art on standard computer vision temporal action localization datasets and instead are focused on practical application to biomedical research of animal behavior.

In practice, the first step in running DeepEthogram is to train the flow generator on a set of videos, which occurs without user input (Figure 1A). In parallel, a user must label each frame in a set of training videos for the presence of each behavior of interest. These labels are then used to train independently the spatial feature extractor and the flow feature extractor to produce separate estimates of the probability of each behavior. The extracted feature vectors for each frame are then saved and used to train the sequence models to produce the final predicted probability of each behavior at each frame. We chose to train the models in series, rather than all at once from end-to-end, due to a combination of concerns about backpropagating error across diverse models, overfitting with extremely large models, and computational capacity (Materials and methods).

To test the performance of our model, we used nine different neuroscience research datasets that span two species and present distinct challenges for computer vision approaches. Please see the examples in Figure 2, Figure 2—figure supplements 1–6, and Videos 1–9 that demonstrate the behaviors of interest and provide an intuition for the ease or difficulty of identifying and distinguishing particular behaviors.

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We collected five datasets of mice in various behavioral arenas. The ‘Mouse-Ventral1’ and ‘Mouse-Ventral2’ datasets are bottom-up videos of a mouse in an open field and small chamber, respectively (Figure 2A,B, Figure 2—figure supplement 1A, B, Videos 1–2). The ‘Mouse-Openfield’ dataset includes commonly used top-down videos of a mouse in an open arena (Figure 2C, Figure 2—figure supplement 2A, Video 3). The ‘Mouse-Homecage’ dataset are top-down videos of a mouse in its home cage with bedding, a hut, and two objects (Figure 2D, Figure 2—figure supplement 3, Video 4). The ‘Mouse-Social’ dataset are top-down videos of two mice interacting in an open arena (Figure 2E, Figure 2—figure supplement 4, Video 5). We also tested three datasets from published work by Sturman et al., 2020 that consist of mice in common behavior assays: the EPM, forced swim test (FST), and open field test (OFT) (Figure 2F–H, Figure 2—figure supplements 5–6, Videos 6–8). Finally, we tested a different species in the ‘Fly’ dataset that includes side view videos of a Drosophila melanogaster and aims to identify a coordinated walking pattern (Fujiwara et al., 2017; Figure 2D, Figure 2—figure supplement 2B, Video 9).

Collectively, these datasets include distinct view angles, a variety of illumination levels, and different resolutions and video qualities. They also pose various challenges for computer vision, including the subject occupying a small fraction of pixels (Mouse-Ventral1, Mouse-Openfield, Mouse-Homecage, Sturman-EPM, Sturman-OFT), complex backgrounds with non-uniform patterns (bedding and objects in Mouse-Homecage) or irrelevant motion (moving water in Sturman-FST), objects that occlude the subject (Mouse-Homecage), poor contrast of body parts (Mouse-Openfield, Sturman-EPM, Sturman-OFT), little motion from frame-to-frame (Fly, due to high video rate), and few training examples (Sturman-EPM, only five videos and only three that contain all behaviors). Furthermore, in most datasets, some behaviors of interest are rare and occur in only a few percent of the total video frames.

In each dataset, we labeled a behavior as present regardless of the location where it occurred and the orientation of the subject when it occurred. We did not note position or direction information, and we did not spatially crop the video frames or align the animal before training our model. In all datasets, we labeled the frames on which none of the behaviors of interest were present as ‘background,’ following the convention in computer vision. Each video in a dataset was recorded using a different individual mouse or fly, and thus training and testing the model across videos measured generalization across individual subjects. The video datasets and researcher annotations are available at the project website: https://github.com/jbohnslav/deepethogram (copy archived at swh:1:rev:ffd7e6bd91f52c7d1dbb166d1fe8793a26c4cb01), Bohnslav, 2021.

We split each dataset into three subsets: training, validation, and test (Materials and methods). The training set was used to update model parameters, such as the weights of the CNNs. The validation set was used to set appropriate hyperparameters, such as the thresholds used to turn the probabilities of each behavior into binary predictions about whether each behavior was present. The test set was used to report performance on new data not used in training the model. We generated five random splits of the data into training, validation, and test sets and averaged our results across these five splits, unless noted otherwise (Materials and methods). We computed three complementary metrics of model performance using the test set. First, we computed the accuracy, which is the fraction of elements of the $\left[T,K\right]$ ethogram that were predicted correctly. We note that in theory accuracy could be high even if the model did not perform well on each behavior. For example, in the Mouse-Ventral2 dataset, some behaviors were incredibly rare, occurring in only ~2% of frames (Figure 2B). Thus, the model could in theory achieve ~98% accuracy simply by guessing that the behavior was absent on all frames. Therefore, we also computed the F1 score, a metric ranging from 0 (bad) to 1 (perfect) that takes into account the rates of false positives and false negatives. The F1 score is the geometric mean of the precision and recall of the model. Precision is the fraction of frames labeled by the model as a given behavior that are actually that behavior (true positives/(true positives + false positives)). Recall is the fraction of frames actually having a given behavior that are correctly labeled as that behavior by the model (true positives/(true positives + false negatives)). We report the F1 score in the main figures and show precision and recall performance in the figure supplements. Because the accuracy and F1 score depend on our choice of a threshold to turn the probability of a given behavior on a given frame into a binary prediction about the presence of that behavior, we also computed the area under the receiver operating curve (AUROC), which summarizes performance as a function of the threshold.

We first considered the entire ethogram, including all behaviors. DeepEthogram performed with greater than 85% accuracy on the test data for all datasets (Figure 3A). Overall metrics were calculated for each element of the ethogram. The model achieved high overall F1 scores, with high precision and recall (Figure 3B, Figure 3—figure supplement 1A, Figure 3—figure supplement 2A). Similarly, high overall performance was observed with the AUROC measures (Figure 3—figure supplement 3A). These results indicate that the model was able to capture the overall patterns of behavior in videos.

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We also analyzed the model’s performance for each individual behavior. The model achieved F1 scores of 0.7 or higher for many behaviors, even reaching F1 scores above 0.9 in some cases (Figure 3C–K). DeepEthogram’s performance significantly exceeded chance levels of performance on nearly all behaviors across datasets (Figure 3C–K). Given that F1 scores may not be intuitive to understand in terms of their values, we examined individual snippets of videos with a range of F1 scores and found that F1 scores similar to the means for our datasets were consistent with overall accurate predictions (Figure 3P, Figure 3—figure supplements 4–11). We note that the F1 score is a demanding metric, and even occasional differences on single frames or a small number of frames can substantially decrease the F1 score. Relatedly, the model achieved high precision, recall, and AUROC values for individual behaviors (Figure 3—figure supplement 1B–J, Figure 3—figure supplement 2B–J, Figure 3—figure supplement 3B–J). The performance of the model depended on the frequency with which a behavior occurred (c.f. Figure 2 right panels and Figure 3C–J). Strikingly, however, performance was relatively high even for behaviors that occurred rarely, that is, in less than 10% of video frames (Figure 3M, Figure 3—figure supplement 1L, Figure 3—figure supplement 2L, and Figure 3—figure supplement 3K). The performance tended to be highest for DeepEthogram-slow and worst for DeepEthogram-fast, but the differences between model versions were generally small and varied across behaviors (Figure 3A,B, Figure 3—figure supplement 1A, Figure 3—figure supplement 2A, Figure 3—figure supplement 3A). The high-performance values are, in our opinion, impressive given that they were calculated based on single-frame predictions for each behavior, and thus performance will be reduced if the model misses the onset or offset of a behavior bout by even a single frame. These high values suggest that the model not only correctly predicted which behaviors happened and when but also had the resolution to correctly predict the onset and offset of bouts.

To better understand the performance of DeepEthogram, we benchmarked the model by comparing its performance to the degree of agreement between expert human labelers. Multiple researchers with extensive experience in monitoring and analyzing mouse behavior videos independently labeled the same set of videos for the Mouse-Ventral1, Mouse-Ventral2, Mouse-Openfield, Mouse-Social, and Mouse-Homecage datasets, allowing us to measure the consistency across human experts. Also, Sturman et al. released the labels of each of three expert human labelers (Sturman et al., 2020). The Fly dataset has more than 3 million frames and thus was too large to label multiple times. Human-human performance was calculated by defining one labeler as the ‘ground truth’ and the other labelers as ‘predictions’ and then computing the same performance metrics as for DeepEthogram. In this way, ‘human accuracy’ is the same as the percentage of scores on which two humans agreed. Strikingly, the overall accuracy, F1 scores, precision, and recall for DeepEthogram approached that of expert human labelers (Figure 3A, B, C, E, H, I, J and L, Figure 3—figure supplement 1A,B,D,G,H,I,K, Figure 3—figure supplement 2). In many cases, DeepEthogram’s performance was statistically indistinguishable from human-level performance, and in the cases in which humans performed better, the difference in performance was generally small. Notably, the behaviors for which DeepEthogram had the lowest performance tended to be the behaviors for which humans had less agreement (lower human-human F1 score) (Figure 3L, Figure 3—figure supplement 1K, Figure 3—figure supplement 2K). Relatedly, DeepEthogram performed best on the frames in which the human labelers agreed and did more poorly in the frames in which humans disagreed (Figure 3N and O, Figure 3—figure supplement 1M, Figure 3—figure supplement 2M). Thus, there is a strong correlation between DeepEthogram and human performance, and the values for DeepEthogram’s performance approach those of expert human labelers.

The behavior with the worst model performance was ‘defecate’ from the Mouse-Openfield dataset (Figures 2C and 3E). Notably, defecation was incredibly rare, occurring in only 0.1% of frames. Furthermore, the act of defecation was not actually visible from the videos. Rather, human labelers marked the ‘defecate’ behavior when new fecal matter appeared, which involved knowledge of the foreground and background, tracking objects, and inferring unseen behavior. This type of behavior is expected to be challenging for DeepEthogram because the model is based on images and local motion and thus will fail when the behavior cannot be directly observed visually.

The model was able to accurately predict the presence of a behavior even when that behavior happened in different locations in the environment and with different orientations of the animal (Figure 3—figure supplement 12). For example, the model predicted face grooming accurately both when the mouse was in the top-left quadrant of the chamber and facing north and when the mouse was in the bottom-right quadrant facing west. This result is particularly important for many analyses of behavior that are concerned with the behavior itself, rather than where that behavior happens.

One striking feature was DeepEthogram’s high performance even on rare behaviors. From our preliminary work building up to the model presented here, we found that simpler models performed well on behaviors that occurred frequently and performed poorly on the infrequent behaviors. Given that, in many datasets, the behaviors of interest are infrequent (Figure 2), we placed a major emphasis on performance in cases with large class imbalances, meaning when some behaviors only occurred in a small fraction of frames. In brief, we accounted for class imbalances in the initialization of the model parameters (Materials and methods). We also changed the cost function to weight errors on rare classes more heavily than errors on common classes. We used a form of regularization specific to transfer learning to reduce overfitting. Finally, we tuned the threshold for converting the model’s probability of a given behavior into a classification of whether that behavior was present. Without these added features, the model simply learned to ignore rare classes. We consider these steps toward identifying rare behaviors to be of major significance for effective application in common experimental datasets.

Because DeepEthogram produces predictions on individual frames, it allows for subsequent analyses of behavior bouts, such as the number of bouts, the duration of bouts, and the transition probability from one behavior to another. These statistics of bouts are often not available if researchers only record the overall time spent on a behavior with a stopwatch, rather than providing frame-by-frame labels. We found a strong correspondence for the statistics of behavior bouts between the predictions of DeepEthogram and those from human labels. We first focused on results at the level of individual videos for the Mouse-Ventral1 dataset, comparing the model predictions and human labels for the percent of time spent on each behavior, the number of bouts per behavior, and the mean bout duration (Figure 4A–C). Note that the model was trained on the labels from Human 1. For the time spent on each behavior, the model predictions and human labels were statistically indistinguishable (one-way ANOVA, p>0.05; Figure 4A). For the number of bouts and bout duration, the model was statistically indistinguishable from the labels of Human 1, on which it was trained. Some differences were present between the model predictions and the other human labels not used for training (Figure 4B,C). However, the magnitude of these differences was within the range of differences between the multiple human labelers (Figure 4B,C).

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To summarize the performance of DeepEthogram on bout statistics for each behavior in all datasets, we averaged the time spent, number of bouts, and bout duration for each behavior across the five random splits of the data into train, validation, and test sets. This average provides a quantity similar to an average across multiple videos, and an average across multiple videos is likely how some end-users will report their results. The values from the model were highly similar to the those from the labels on which it was trained (Human 1 labels) for the time spent per behavior, the number of bouts, and the mean bout duration (Figure 4D–F). Together, these results show that DeepEthogram accurately predicts bout statistics that might be of interest to biologist end-users.

Next, we benchmarked DeepEthogram’s performance on bout statistics by comparing its performance to the level of agreement between expert human labelers. We started by looking at the time spent on each behavior in single videos for the Mouse-Ventral1 and Sturman-OFT datasets. We compared the labels from Human 1 to the model predictions and to the labels from Humans 2 and 3 (Figure 5A,B). In general, there was strong agreement between the model and Human 1 and among human labelers (Figure 5A,B, left and middle). To directly compare model performance to human-human agreement, we plotted the absolute difference between the model and Human 1 versus the absolute difference between Human 1 and Humans 2 and 3 (Figure 5A,B, right). Model agreement was significantly worse than human-human agreement when considering individual videos. However, the magnitude of this difference was small, implying that discrepancies in behavior labels introduced by the model were only marginally larger than the variability between multiple human labelers.

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To summarize our benchmarking of model performance on bout statistics for each behavior in all datasets with multiple human labelers, we again averaged the time spent, number of bouts, and bout duration for each behavior across the five random splits of the data to obtain a quantity similar to an average across multiple videos (Figure 5C–E). For time spent per behavior, number of bouts, and mean bout length, the human-model differences were similar, and not significantly different, in magnitude to the differences between humans (Figure 5C–E, right column). The transition probabilities between behaviors were also broadly similar between Human 1, Human 2, and the model (Figure 5F–H). Furthermore, model-human differences and human-human differences were significantly correlated (Figure 5C–E, right column), again showing that DeepEthogram models are more reliable for situations in which multiple human labelers agree (see Figure 3N–O, Figure 3—figure supplement 1M, Figure 3—figure supplement 2M).

Therefore, the results from Figure 5A,B indicate that the model predictions are noisier than human-human agreement on the level of individual videos. However, when averaged across multiple videos (Figure 5C–E), this noise averages out and results in similar levels of variability for the model and multiple human labelers. Given that DeepEthogram performed slightly worse on F1 scores relative to expert humans but performed similarly to humans on bout statistics, it is possible that for rare behaviors DeepEthogram misses a small number of bouts, which would minimally affect bout statistics but could decrease the overall F1 score.

Together, our results from Figures 3—5 and Figure 3—figure supplements 1–3 indicate that DeepEthogram’s predictions match well the labels defined by expert human researchers. Further, these model predictions allow easy post-hoc analysis of additional statistics of behaviors, which may be challenging to obtain with traditional manual methods.

While DeepEthogram operates directly on the raw pixel values in the videos, other methods exist that first track body keypoints and then perform behavior classification based on these keypoints (Segalin, 2020; Kabra et al., 2013; Nilsson, 2020; Sturman et al., 2020). One such approach that is appealing due to its simplicity and clarity was developed by Sturman et al. and was shown to be superior to commercially available alternatives (Sturman et al., 2020). In their approach, DeepLabCut (Mathis, 2018) is used to estimate keypoints and then a multilayer perceptron architecture is used to classify features of these keypoints into behaviors. We compared the performance of DeepEthogram and this alternate approach using our custom implementation of the Sturman et al. methods (Figure 5—figure supplement 1). We focused our comparison on the Mouse-Openfield dataset, which is representative of videos used in a wide range of biological studies. We used DeepLabCut (Mathis, 2018) to identify the position of the four paws, the base of the tail, the tip of the tail, and the nose. These keypoints could be used to distinguish behaviors. For example, the distance between the nose and the base of the tail was highest when the mouse was locomoting (Figure 5—figure supplement 1C). However, the accuracy and F1 scores for DeepEthogram generally exceeded those identified from classifiers based on features of these keypoints (Figure 5—figure supplement 1D–G). For bout statistics, the two methods performed similarly well (Figure 5—figure supplement 1F–J). Thus, for at least one type of video and dataset, DeepEthogram outperformed an established approach.

There are several reasons why DeepEthogram might have done better on accuracy and F1 score. First, the videos tested were relatively low resolution, which restricted the number of keypoints on the mouse’s body that could be labeled. High-resolution videos with more keypoints may improve the keypoint-based classification approach. Second, our videos were recorded with a top-down view, which means that the paw positions were often occluded by the mouse’s body. A bottom-up or side view could allow for better identification of keypoints and may result in improved performance for the keypoint-based methods.

An alternative approach to DeepEthogram and other supervised classification pipelines could be to use an unsupervised behavior classification followed by human labeling of behavior clusters. In this approach, an unsupervised algorithm identifies behavior clusters without user input, and then the researcher identifies the cluster that most resembles their behavior of interest (e.g., ‘cluster 3 looks like face grooming’). The advantage of this approach is that it involves less researcher time due to the lack of supervised labeling. However, this approach is not designed to identify predefined behaviors of interest and thus, in principle, might not be well suited for the goal of supervised classification. We tested one such approach starting with the Mouse-Openfield dataset and DeepLabCut-generated keypoints (Figure 5—figure supplement 1A, C). We used B-SoID (Hsu and Yttri, 2019), an unsupervised classification pipeline for animal behavior, which identified 11 behavior clusters for this dataset (Figure 5—figure supplement 2A, B). These clusters were separable in a low-dimensional behavior space (Figure 5—figure supplement 2B), and B-SoID’s fast approximation algorithm showed good performance (Figure 5—figure supplement 2C). For every frame in our dataset, we had human labels, DeepEthogram predictions, and B-SoID cluster assignments. By looking at the joint distributions of B-SoID clusters and human labels, there appeared to be little correspondence (Figure 5—figure supplement 2D). To assign human labels to B-SoID clusters, for each researcher-defined behavior, we picked the B-SoID cluster that had the highest overlap with the behavior of interest (red boxes, Figure 5—figure supplement 2D, right). We then evaluated these ‘predictions’ compared to DeepEthogram. For most behaviors, DeepEthogram performed better than this alternative pipeline (Figure 5—figure supplement 2E).

We note that the unsupervised clustering with post-hoc assignment of human labels is not the use for which B-SoID (Hsu and Yttri, 2019) and other unsupervised algorithms (Wiltschko, 2015; Berman et al., 2014) were designed. Unsupervised approaches are designed to discover repeated behavior motifs directly from data, without humans predefining the behaviors of interest (Datta et al., 2019; Egnor and Branson, 2016), and B-SoID succeeded in this goal. However, if one’s goal is the automatic labeling of human-defined behaviors, our results show that DeepEthogram or other supervised machine learning approaches are better choices.

We evaluated how much data a user must label to train a reliable model. We selected 1, 2, 4, 8, 12, or 16 random videos for training and used the remaining videos for evaluation. We only required that each training set had at least one frame of each behavior. We trained the feature extractors, extracted the features, and trained the sequence models for each split of the data into training, validation, and test sets. We repeated this process five times for each number of videos, resulting in 30 trained models per dataset. Given the large number of dataset variants for this analysis, to reduce overall computation time, we used DeepEthogram-fast and focused on only the Mouse-Ventral1, Mouse-Ventral2, and Fly datasets. Also, we trained the flow generator only once and kept it fixed for all experiments. For all but the rarest behaviors, the models performed at high levels even with only one labeled video in the training set (Figure 6A–C). For all the behaviors studied across datasets, the performance measured as accuracy or F1 score approached seemingly asymptotic levels after training on approximately 12 videos. Therefore, a training set of this size or less is likely sufficient for many cases.

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We also analyzed the model’s performance as a function of the number of frames of a given behavior present in the training set. For each random split, dataset, and behavior, we had a wide range of the number of frames containing a behavior of interest. Combining all these splits, datasets, and behaviors together, we found that the model performed with more than 90% accuracy when trained with only 80 example frames of a given behavior and over 95% accuracy with only 100 positive example frames (Figure 6D). Furthermore, DeepEthogram achieved an F1 score of 0.7 with only 9000 positive example frames, which corresponds to about 5 min of example behavior at 30 frames per second (Figure 6E, see Figure 3P for an example of ~0.7 F1 score). The total number of frames required to reach this number of positive example frames depends on how frequent the behavior is. If the behavior happens 50% of the time, then 18,000 total frames are required to reach 9000 positive example frames. Instead, if the behavior occurs 10% of the time, then 90,000 total frames are required. In addition, when the sequence model was used instead of using the predictions directly from the feature extractors, model performance was higher (Figure 6F,G) and required less training data (data not shown), emphasizing the importance of using long timescale information in the prediction of behaviors. Therefore, DeepEthogram models require little training to achieve high performance. As expected, as more training data are added, the performance of the model improves, but this rather light dependency on the amount of training data makes DeepEthogram amenable for even small-scale projects.

A key aspect of the functionality of the software is the speed with which the models can be trained and predictions about behaviors made on new videos. Although the versions of DeepEthogram vary in speed, they are all fast enough to allow functionality in typical experimental pipelines. On modern computer hardware, the flow generator and feature extractors can be trained in approximately 24 hr. In many cases, these models only need to be trained once. Afterwards, performing inference to make predictions about the behaviors present on each frame can be performed at ~150 frames per second for videos at 256 × 256 resolution for DeepEthogram-fast, at 80 frames per second for DeepEthogram-medium, and 13 frames per second for DeepEthogram-slow (Table 1). Thus, for a standard 30 min video collected at 60 frames per second, inference could be finished in 12 min for DeepEthogram-fast or 2 hr for DeepEthogram-slow. Importantly, the training of the models and the inference involve zero user time because they do not require manual input or observation from the user. Furthermore, this speed is rapid enough to get results quickly after experiments to allow fast analysis and experimental iteration. However, the inference time is not fast enough for online or closed-loop experiments.

We developed a GUI for labeling videos, training models, and running inference (Figure 7). Our GUI is similar in behavior to those for BORIS (Friard et al., 2016) and JAABA (Kabra et al., 2013). To train DeepEthogram models, the user first defines which behaviors of interest they would like to detect in their videos. Next, the user imports a few videos into DeepEthogram, which automatically calculates video statistics and organizes them into a consistent file structure. Then the user clicks a button to train the flow generator model, which occurs without user time. While this model is training, the user can go through a set of videos frame-by-frame and label the presence or absence of all behaviors in these videos. Labeling is performed with simple keyboard or mouse clicks at the onset and offset of a given behavior while scrolling through a video in a viewing window. After a small number of videos have been labeled and the flow generator is trained, the user then clicks a button to train the feature extractors, which occurs without user input and saves the extracted features to disk. Finally, the sequence model can be trained automatically on these saved features by clicking another button. All these training steps could in many cases be performed once per project. With these trained models, the user can import new videos and click the predict button, which estimates the probability of each behavior on each frame. This GUI therefore presents a single interface for labeling videos, training models, and generating predictions on new videos. Importantly, this interface requires no programming by the end-user.

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The GUI also includes an option for users to manually check and edit the predictions output by the model. The user can load into the GUI a video and predictions made by the model. By scrolling through the video, the user can see the predicted behaviors for each frame and update the labels of the behavior manually. This allows users to validate the accuracy of the model and to fix errors should they occur. This process is expected to be fast because the large majority of frames are expected to be labeled correctly, based on our accuracy results, so the user can focus on the small number of frames associated with rare behaviors or behaviors that are challenging to detect automatically. Importantly, these new labels can then be used to retrain the models to obtain better performance on future experimental videos. Documentation for the GUI will be included on the project’s website.

Along with this publication, we are releasing open-source Python code for labeling videos, training all DeepEthogram models, and performing inference on new videos. The code, associated documentation, and files for the GUI can be found at https://github.com/jbohnslav/deepethogram.

We implemented DeepEthogram in the Python programming language (version 3.7 or later; Rossum et al., 2010). We used PyTorch (Paszke, 2018; version 1.4.0 or greater) for all deep-learning models. We used PyTorch Lightning for training (Falcon, 2019). We used OpenCV (Bradski, 2008) for image reading and writing. We use Kornia (Riba et al., 2019) for GPU-based image augmentations. We used scikit-learn (Pedregosa, 2021) for evaluation metrics, along with custom Python code. CNN diagram in Figure 1 was generated using PlotNeuralNet (Iqbal, 2018). Other figures were generated in Matplotlib (Caswell, 2021). 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.

All experimental procedures were approved by the Institutional Animal Care and Use Committees at Boston Children’s Hospital (protocol numbers 17-06-3494R and 19-01-3809R) or Massachusetts General Hospital (protocol number 2018N000219) and were performed in compliance with the Guide for the Care and Use of Laboratory Animals.

For human-human comparison, we relabeled all videos for Mouse-Ventral1, Mouse-Ventral2, Mouse-Openfield, Mouse-Social, and Mouse-Homecage using the DeepEthogram GUI. Previous labels were not accessible during relabeling. Criteria for relabeling were written in detail by the original experimenters, and example labeled videos were viewed extensively before relabeling. Mouse-Ventral1 was labeled three times and the other datasets were labeled twice.

Videos and human annotations are available at the project website: https://github.com/jbohnslav/deepethogram.

We used the ADAM optimizer (Kingma and Ba, 2017) with an initial learning rate of 1 × 10^–4 for all models. When validation performance saturated for 5000 training steps, we decreased the learning rate by a factor of $\frac{1}{\sqrt{10}}$ on the Kinetics700 dataset, or by a factor of 0.1 for neuroscience datasets (for speed), $5e-7.$ For Kinetics700, we used the provided train and validation split. For neuroscience datasets, we randomly picked 60% of videos for training, 20% for validation, and 20% for test (with the exception of the subsampling experiments for Figure 5, wherein we only used training and validation sets to reduce overall training time). Our only restriction on random splitting was ensuring that at least one frame of each class was included in each split. We were limited to five random splits of the data for most experiments due to the computational and time demands of retraining models. We save both the final model weights and the best model weights; for inference, we load the best weights. Best is assessed by the minimum validation loss for flow generator models or the mean F1 across all non-background classes for feature extractor and sequence models.

For Kinetics700 models, we stopped when the learning rate dropped below 5e-7. This required about 800,000 training steps. For neuroscience dataset flow generators, we stopped training at 10,000 training steps. For neuroscience dataset feature extractors, we stopped when the learning rate dropped below 5e-7, when 20,000 training steps were complete, or 24 hr elapsed, whichever came first. For sequence models, we stopped when the learning rate dropped below 5e-7, or when 100,000 training steps were complete, whichever came first.

We could, in theory, train the entire DeepEthogram pipeline end-to-end. However, we chose to train the flow generator, feature extractor, and then sequence models sequentially. By backpropagating the classification loss into the flow generator (Zhu et al., 2017), we risk increasing the overall number of parameters and overfitting. Furthermore, we designed the sequence models to have a large temporal receptive window. We therefore train on long sequences (see below). Very long sequences of raw video frames take large amounts of VRAM and exceed our computational limits. By illustration, to train on sequences of, for example, 180 frame snippets of 11 images, our tensor would be of shape [N × 33 × 180 × 256 × 256]. This corresponds to 24 GB of VRAM at a batch size of 16, just for the data and none of the neural activations or gradients, which is impractical. Therefore, we first extract features to disk and subsequently train sequence models.

To improve the robustness and generalization of our models, we augmented the input images with random perturbations for all datasets during training. We used Kornia (Riba et al., 2019) for GPU-based image augmentation to improve training speed. We perturbed the image brightness and contrast, rotated each image by up to 10°, and flipped horizontally and vertically (depending on the dataset). The input to the flow generator model is a set of 11 frames; the same augmentations were performed on each image in this stack. On Mouse-Ventral1 and Mouse-Ventral2, we also flipped images vertically with a probability of 0.5. We calculated the mean and standard deviation of the RGB input channels and standardized the input channel-wise.

All flow generators and feature extractors were first trained to classify videos in the Kinetics700 dataset (see below). These weights were used to initialize models on neuroscience datasets. Sequence models were trained from scratch.

For optic flow extraction, a common algorithm to use is TV-L1 (Carreira and Zisserman, 2017). However, common implementations of this algorithm (Bradski, 2008) require compilation of C++, which would introduce many dependencies and make installation more difficult. Furthermore, recent work (Zhu et al., 2017) has shown that even simple neural-network-based optic flow estimators outperform TV-L1 for action detection. Therefore, we used CNN-based optic flow estimators. Furthermore, we found that saving optic flow as JPEG images, as is common, significantly degraded performance. Therefore, we computed optic flows from a stack of RGB images at runtime for both training and inference. This method is known as Hidden Two-Stream Networks (Zhu et al., 2017).

In brief, we train flow generators to minimize reconstruction errors and minimize high-frequency components (to encourage smooth flow outputs).

With millions of parameters and far fewer data points, it is likely that our models will overfit to the training data. Transfer learning (see above) ameliorates this problem somewhat, as does using dropout (see below). However, increasing dropout to very high levels reduces the representational space of the feature vector. To reduce overfitting, we used $L^{2}SP$ regularization (Li et al., 2018). A common form of regularization is weight decay, in which the sum of squared weights is penalized. However, this simple term could cause the model to ‘forget’ its initial knowledge from transfer learning. Therefore, $L^{2}SP$ regularization uses the initial weights from transfer learning as the target.

For details, see the L2-SP paper (Li et al., 2018). $w$ are all trainable parameters of the network, excluding biases and batch normalization parameters. $\alpha$ is a hyperparameter governing how much to decay weights towards their initial values (from transfer learning). $w_s$ are current model weights, and $w_s^0$ are their values from pre-training. $\beta$ is a hyperparameter decaying new weights $w_{\acute{s}}$ (such as the final linear readout layers in feature extractors) towards zero. For flow generator models, $\alpha =1e^{-5}$ . There are no new weights, so $\beta$ is unused.

The final loss is the weighted sum of the previous components:

Following Zhu et al., 2017, we set ${\lambda }_0=1,{\lambda }_1=1$. During training, the flow generator’s output flows at multiple resolutions. From largest to smallest, we set ${\lambda }_2$ to be 0.01, 0.02, 0.04. For TinyMotionNet3D, we set ${\lambda }_3$ to 0.05 and reduced ${\lambda }_2$ by a factor of 0.25.

The goal of the feature extractor was to model the probability that each behavior was present in the given frame of the video (or optic flow stack). We used two-stream CNNs (Zhu et al., 2017; Simonyan and Zisserman, 2014) to classify inputs from both RGB frames and optic flow frames. These CNNs reduced an input tensor from $\left(N,C,H,W\right)$ pixels to $\left(N,512\right)$ features. Our final fully connected layer estimated probabilities for each behavior, with output shape $\left(N,K\right)$ . Here, $N$ is the batch size. We trained these CNNs on our labels, and then used the penultimate $\left(N,512\right)$ spatial features or flow features as inputs to our sequence models (below).

To improve inference speed, we use a custom inference video pipeline that uses only sequential video reading, batched model predictions, and multiprocessed data loading. Inference speed time is strongly related to input resolution and GPU hardware. We report timing on both a Titan RTX graphics card and a GeForce 1080 Ti graphics card.

The data loss term is the weighted, binary focal loss as for the feature extractor above. For the regularization loss, we used simple L2 regularization because we do not pretrain the sequence models.

We used $\alpha =0.01$ for all datasets.

We compared pixel-based (DeepEthogram) and skeleton-based behavioral classification on the Mouse-Openfield dataset. Our goal was to replicate Sturman et al., 2020 as closely as possible. We chose this dataset because videos with this resolution of mice in an open field arena are a common form of behavioral measurement in biology. We first used DeepLabCut (Mathis, 2018) to label keypoints on the mouse and train pose estimation models. Due to the low resolution of the videos (200–300 pixels on each side), we could only reliably estimate seven keypoints: nose, left and right forepaw (if visible, or shoulder area), left and right hindpaw (if visible, or hip area), the base of the tail, and the tip of the tail. See Figure 5—figure supplement 1A for details. We labeled 1800 images and trained models using the DeepLabCut Colab notebook (ResNet50). Example performance on held-out data for unlabeled frames can be seen in Figure 5—figure supplement 1B. We used linear interpolation for keypoints with confidence below 0.9.

Using these seven keypoints for all videos, we computed a number of pose and behavioral features (Python, NumPy). As a check, we plotted the distribution of these features for each human-labeled behavior (Figure 5—figure supplement 1C). These features contained signal that can reliably discriminate behaviors. For example, the distance between the nose and the tailbase is larger during locomotion than during face grooming (Figure 5—figure supplement 1C, left).

We attempted to replicate Sturman et al., 2020 as closely as possible. However, due to technical considerations, using the exact codebase was not possible. Our dataset is multilabel, meaning that two behaviors can present, and be labeled, on a single frame. Therefore, we could not use cross-entropy loss. Our videos are lower resolution, and therefore we used seven keypoints instead of 10. We normalized pixels by the width and height of the arena. We computed the centroid as the mean of all paw locations. Due to the difference in keypoints we selected, we had to perform our own behavioral feature expansion. (See ‘Time-resolved skeleton representation,’ Sturman et al., 2020 supplementary methods.) We used the following features:

• x and y coordinates of all keypoints in the arena

• x and y coordinates after aligning relative to the body axis, such that the nose was to the right and the tailbase to the left

• Angles between

  • tail and body axis

  • each paw and the body axis

• Distances between

  • nose and tailbase

  • tail base and tip

  • left forepaw and left hindpaw, right forepaw and hindpaw, averaged

  • forepaw and nose

  • left and right forepaw

  • left and right hindpaw

• The area of the body (polygon enclosed by the paws, nose, and tailbase)

This resulted in 44 behavioral features for each frame. Following Sturman et al., we used T-15 frames to T + 15 frames as input to our classifier, which is 1364 features in total (44 * 31). We also used the same model architecture as Sturman et al.: a multilayer perceptron with 1364 neurons in the input layer, two hidden layers with 256 and 128 neurons, respectively, and ReLU activations. For simplicity, we used Dropout (Srivastava et al., 2014) with probability 0.35 between each layer.

To make the comparison as fair as possible, we implemented training tricks from DeepEthogram sequence models to train these keypoint-based models. These include the loss function (binary focal loss with up-weighting of rare behaviors), the stopping criterion (100 epochs or when learning rate reduces below 5e-7), learning rate scheduling based on validation F1 saturation, L2 regularization, thresholds optimized based on F1, postprocessing based on bout length statistics, and inference using the best weights during training (as opposed to the final weights).

To compare DeepEthogram to unsupervised classification, we used B-SoID (Hsu and Yttri, 2019; version 2.0, downloaded January 18, 2021). We used the same DeepLabCut outputs as for the supervised classifiers. We used the Streamlit GUI for feature computation, UMAP embedding, model training, and classification. Our Mouse-Openfield dataset contained videos with a mixture of 30 and 60 frames-per-second videos. The B-SoID app assumed constant framerates; therefore, we down-sampled the poses from 60 Hz to 30 Hz, performed all embedding and classification, and then up-sampled classified behaviors back to 60 Hz using nearest-neighbor up-sampling (PyTorch, see Figure 5—figure supplement 2A). B-SoID identified 11 clusters in UMAP space (Figure 5—figure supplement 2B, left). To compare unsupervised with post-hoc assignment to DeepEthogram, we first computed a simple lookup table that mapped human annotations to B-SoID clusters by counting the frames on which they co-occurred (Figure 5—figure supplement 2D). For each human label, we picked the B-SoID cluster with the maximum number of co-occurring labels; this defines a mapping between B-SoID clusters and human labels. We used this mapping to ‘predict’ human labels on the test set (Figure 5—figure supplement 2E). We compared B-SoID to the DeepEthogram-fast model for a fair comparison as B-SoID inference is relatively fast.

There are many hyperparameters in DeepEthogram models that can dramatically affect performance. We optimized hyperparameters using Ray Tune (Liaw, 2018), a software package for distributed, asynchronous model selection and training. Our target for hyperparameter optimization was the F1 score averaged over classes on the validation set, ignoring the background class. We used random search to select hyperparameters, and the Asynchronous Successive Halving algorithm (Li, 2020) to terminate poor runs. We did not perform hyperparameter optimization on flow generator models. For feature extractors, we optimized the following hyperparameters: learning rate, $\alpha$ and $\beta$ from the regularization loss, $\gamma$ from the focal loss, $\beta$ from the positive example weighting, whether or not to add a batch normalization layer after the final fully connected layer (Kocaman et al., 2020), dropout probability, and label smoothing. For sequence models, we optimized learning rate, regularization $\alpha$, $\gamma$ from the focal loss, $\beta$ from the positive example weighting, whether or not to add a batch normalization layer after the final fully connected layer (Kocaman et al., 2020), input dropout probability, output dropout probability, filter length, number of layers, whether or not to use soft attention (Piergiovanni and Ryoo, 2018), whether or not to add a nonlinear classification layer, and number of features in the nonlinear classification layer.

The absolute best performance could have been obtained by picking the best hyperparameters for each dataset, model size (DeepEthogram-f, DeepEthogram-m, or DeepEthogram-s), and split of the data. However, this would overstate performance for subsequent users that do not have the computational resources to perform such an optimization themselves. Therefore, we manually selected hyperparameters that had good performance on average across all datasets, models, and splits, and used the same parameters for all models. We will release the Ray Tune integration code required for users to optimize their own models, should they choose.

We performed this optimization on the O2 High Performance Compute Cluster, supported by the Research Computing Group, at Harvard Medical School. See http://rc.hms.harvard.edu for more information. Specifically, we used a cluster consisting of 8 RTX6000 GPUs.

The output of the feature extractor and sequence model is the probability of behavior $k$ occurring on frame $t$: $p_{t,k}=f\left(x_{t,k}\right)$ . To convert these probabilities into binary predictions, we thresholded the probabilities:

We picked the threshold ${\tau }_k$ for each behavior $k$ that maximized the F1 score (below). We picked the threshold independently on the training and validation sets. On test data, we used the validation thresholds.

We found that these predictions overestimated the overall number of bouts. In particular, very short bouts were over-represented in model predictions. For each behavior k, we removed both ‘positive’ and ‘negative’ bouts (binary sequences of 1 s and 0 s, respectively) shorter than the first percentile of the bout length distribution in the training set.

Finally, we computed the ‘background’ class as the logical not of the other predictions.

We used the following metrics: overall accuracy, F1 score, and the AUROC by class. Accuracy was defined as

where $TP$ is the number of true positives, $TN$ is the number of true negatives, $FP$ is the number of false positives, and $FN$ is the number of false negatives. We reported overall accuracy, not accuracy for each class.

F1 score was defined as

where $precision=\frac{TP}{TP+FP}$ and $recall=\frac{TP}{TP+FN}$ . The above was implemented by sklearn.metrics.f1_score with argument average=’macro’.

AUROC was computed by taking the AUROC for each class and averaging the result. This was implemented by sklearn.metrics.roc_auc_score with argument average=’macro’.

To compare model performance to random chance, we performed a shuffling procedure. For each model and random split, we randomly circularly permuted each video’s labels 100 times. This means that the distribution of labels was kept the same, but the correspondence between predictions and labels was broken. For each of these 100 repetitions, we computed all metrics and then averaged across repeats; this results in one chance value per split of the data (gray bars, Figure 3C–K, Figure 3—figure supplement 1B–J, Figure 3—figure supplement 2B–J, Figure 3—figure supplement 3B–J).

We randomly assigned input videos to train, validation, and test splits (see above). We then trained flow generator models, feature extractor models, performed inference, and trained sequence models. We repeated this process five times for all datasets. For Sturman-EPM, because only three videos had at least one behavior, we split this dataset three times. When evaluating DeepEthogram performance, this results in N = 5 samples. For each split of the data, the videos in each subset were different; the fully connected layers in the feature extractor were randomly initialized with different weights; and the sequence model was randomly initialized with different weights. When comparing the means of multiple groups (e.g., shuffle, DeepEthogram, and human performance for a single behavior), we used a one-way repeated measures ANOVA, with subjects being splits. If this was significant, we performed a post-hoc Tukey’s honestly significant difference test to compare means pairwise. For cases in which only two groups were being compared (e.g., model and shuffle without human performance), we performed paired t-tests with Bonferroni correction.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We thank Woolf lab members Rachel Moon for data acquisition and scoring and Victor Fattori for scoring, and David Roberson and Lee Barrett for designing and constructing the PalmReader and iBob mouse viewing platforms. We thank the Harvey lab for helpful discussions and feedback on the manuscript. We thank Sturman et al. 2020 for making their videos and human labels publicly available. This work was supported by NIH grants R01 MH107620 (CDH), R01 NS089521 (CDH), R01 NS108410 (CDH), DP1 MH125776 (CDH), F31 NS108450 (JPB), R35 NS105076 (CJW), R01 AT011447 (CJW), R00 NS101057 (LLO), K99 DE028360 (DAY), European Research Council grant ERC-Stg-759782 (EC), an NSF GRFP (NKW), FCT fellowship PD/BD/105947/2014 (TC), a Harvard Medical School Dean’s Innovation Award (CDH), and a Harvard Medical School Goldenson Research Award (CDH).

All experimental procedures were approved by the Institutional Animal Care and Use Committees at Boston Children's Hospital (protocol numbers 17-06-3494R and 19-01-3809R) or Massachusetts General Hospital (protocol number 2018N000219) and were performed in compliance with the Guide for the Care and Use of Laboratory Animals.

© 2021, Bohnslav et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.