DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
- Max Planck Institute of Animal Behavior, Germany
- Princeton University, United States
Abstract
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2× with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
Data availability
Data used and generated for experiments and model comparisons are included in the supporting files. Posture datasets can be found at: https://github.com/jgraving/deepposekit-dataThe code for DeepPoseKit is publicly available at the URL we provided in the paper: https://github.com/jgraving/deepposekit/The reviewers should follow the provided instructions for installation in the README file https://github.com/jgraving/deepposekit/blob/master/README.md#installation. Example Jupyter notebooks for how to use the code are provided here: https://github.com/jgraving/deepposekit/tree/master/examples
-
Fast animal pose estimation using deep neural networkshttp://arks.princeton.edu/ark:/88435/dsp01pz50gz79z.
Article and author information
Author details
Funding
National Science Foundation (IOS-1355061)
- Iain D Couzin
Horizon 2020 Framework Programme (Marie Sklodowska-Curie grant agreement No. 748549)
- Blair R Costelloe
Nvidia (GPU Grant)
- Blair R Costelloe
Office of Naval Research (N00014-09-1-1074)
- Iain D Couzin
Office of Naval Research (N00014-14-1-0635)
- Iain D Couzin
Army Research Office (W911NG-11-1-0385)
- Iain D Couzin
Army Research Office (W911NF14-1-0431)
- Iain D Couzin
Deutsche Forschungsgemeinschaft (DFG Centre of Excellence 2117)
- Iain D Couzin
University of Konstanz (Zukunftskolleg Investment Grant)
- Blair R Costelloe
The Strukture-und Innovations fonds fur die Forschung of the State of Baden-Wurttemberg
- Iain D Couzin
Max Planck Society
- Iain D Couzin
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures for collecting the zebra (E. grevyi) dataset were reviewed and approved by Ethikrat, the independent Ethics Council of the Max Planck Society. The zebra dataset was collected with the permission of Kenya's National Commission for Science, Technology and Innovation (NACOSTI/P/17/59088/15489 and NACOSTI/P/18/59088/21567) using drones operated by B.R.C. with the permission of the Kenya Civil Aviation Authority (authorization numbers: KCAA/OPS/2117/4 Vol. 2 (80), KCAA/OPS/2117/4 Vol. 2 (81), KCAA/OPS/2117/5 (86) and KCAA/OPS/2117/5 (87); RPAS Operator Certificate numbers: RPA/TP/0005 AND RPA/TP/000-0009).
Copyright
© 2019, Graving et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 24,846
- views
-
- 2,413
- downloads
-
- 398
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
eLife 8:e47994.
https://doi.org/10.7554/eLife.47994
Further reading
-
- Neuroscience
Decoders for brain-computer interfaces (BCIs) assume constraints on neural activity, chosen to reflect scientific beliefs while yielding tractable computations. Recent scientific advances suggest that the true constraints on neural activity, especially its geometry, may be quite different from those assumed by most decoders. We designed a decoder, MINT, to embrace statistical constraints that are potentially more appropriate. If those constraints are accurate, MINT should outperform standard methods that explicitly make different assumptions. Additionally, MINT should be competitive with expressive machine learning methods that can implicitly learn constraints from data. MINT performed well across tasks, suggesting its assumptions are well-matched to the data. MINT outperformed other interpretable methods in every comparison we made. MINT outperformed expressive machine learning methods in 37 of 42 comparisons. MINT’s computations are simple, scale favorably with increasing neuron counts, and yield interpretable quantities such as data likelihoods. MINT’s performance and simplicity suggest it may be a strong candidate for many BCI applications.
-
- Neuroscience
Dendritic branching and synaptic organization shape single-neuron and network computations. How they emerge simultaneously during brain development as neurons become integrated into functional networks is still not mechanistically understood. Here, we propose a mechanistic model in which dendrite growth and the organization of synapses arise from the interaction of activity-independent cues from potential synaptic partners and local activity-dependent synaptic plasticity. Consistent with experiments, three phases of dendritic growth – overshoot, pruning, and stabilization – emerge naturally in the model. The model generates stellate-like dendritic morphologies that capture several morphological features of biological neurons under normal and perturbed learning rules, reflecting biological variability. Model-generated dendrites have approximately optimal wiring length consistent with experimental measurements. In addition to establishing dendritic morphologies, activity-dependent plasticity rules organize synapses into spatial clusters according to the correlated activity they experience. We demonstrate that a trade-off between activity-dependent and -independent factors influences dendritic growth and synaptic location throughout development, suggesting that early developmental variability can affect mature morphology and synaptic function. Therefore, a single mechanistic model can capture dendritic growth and account for the synaptic organization of correlated inputs during development. Our work suggests concrete mechanistic components underlying the emergence of dendritic morphologies and synaptic formation and removal in function and dysfunction, and provides experimentally testable predictions for the role of individual components.
