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--- |
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tags: |
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- computer_vision |
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- pose_estimation |
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- animal_pose_estimation |
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- deeplabcut |
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--- |
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# MODEL CARD: |
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## Model Details |
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• SuperAnimal-TopViewMouse model developed by the [M.W.Mathis Lab](http://www.mackenziemathislab.org/) in 2023, |
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trained to predict mouse topline pose from images. |
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Please see [Shaokai Ye et al. 2023](https://arxiv.org/abs/2203.07436) for details. |
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• The there are three models: |
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- `pose_model.pth` is an HRNet-w32 compatable for DLC3.0+ Pytorch code, trained on our TopViewMouse-5K dataset. |
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- `detector.pt` is a Faster R-CNN that can be used as a detector for top-down detection. |
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- `DLC_ma_supertopview5k_resnet_50_iteration-0_shuffle-1.tar.gz` is a DLCRNet trained on our TopViewMouse-5K dataset. |
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• Full training details can be found in Ye et al. 2023. |
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You can use this model simply with our light-weight loading package called [DLCLibrary](https://github.com/DeepLabCut/DLClibrary). |
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Here is an example useage: |
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```python |
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from pathlib import Path |
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from dlclibrary import download_huggingface_model |
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# Creates a folder and downloads the model to it |
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model_dir = Path("./superanimal_topviewmouse_model") |
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model_dir.mkdir() |
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download_huggingface_model("superanimal_topviewmouse_model", model_dir) |
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``` |
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## Intended Use |
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• Intended to be used for pose tracking of lab mice videos filmed from an overhead view. The models can be used as a plug-and- |
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play solution if extremely high precision is not required (we benchmark the zero-shot performance in the paper). Otherwise, it is |
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recommended to also be used as the weights for transfer learning and fine-tuning. |
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• Intended for academic and research professionals working in fields related to animal behavior, neuroscience, biomechanics, and |
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ecology. |
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• Not suitable for other species and other camera views. Also not suitable for videos that look dramatically different from those we |
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show in the paper. |
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## Factors |
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• Based on the known robustness issues of neural networks, the relevant factors include the lighting, contrast and resolution of the |
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video frames. The present of objects might also cause false detections of the mice and keypoints. When two or more animals are |
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extremely close, it could cause the top-down detectors to only detect only one animal, if used without further fine-tuning. |
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## Metrics |
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• Mean Average Precision (mAP) |
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• Root Mean Square Error (RMSE) |
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## Evaluation Data |
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• The test split of TopViewMouse-5K and in the paper on two benchmarks, DLC Openfield and TriMouse |
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## Training Data |
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It consists of being trained together on the following datasets: |
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- **3CSI, BM, EPM, LDB, OFT** See full details at (1) and in (2). |
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- **BlackMice** See full details at (3). |
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- **WhiteMice** Courtesy of Prof. Sam Golden and Nastacia Goodwin. See details in SIMBA (4). TriMouse See full details |
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at (5). |
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- **DLC-Openfield** See full details at (6). |
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- **Kiehn-Lab-Openfield, Swimming, and treadmill** Courtesy of Prof. Ole |
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Kiehn, Dr. Jared Cregg, and Prof. Carmelo Bellardita; see details at (7). |
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- **MausHaus** We collected video data from five |
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single-housed C57BL/6J male and female mice in an extended home cage, carried out in the laboratory of Mackenzie Mathis |
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at Harvard University and also EPFL (temperature of housing was 20-25C, humidity 20-50%). Data were recorded at 30Hz |
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with 640 × 480 pixels resolution acquired with White Matter, LLC eV cameras. Annotators localized 26 keypoints across 322 |
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frames sampled from within DeepLabCut using the k-means clustering approach (8). All experimental procedures for mice |
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were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by |
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the Harvard Institutional Animal Care and Use Committee (IACUC) (n=1 mouse), and by the Veterinary Office of the Canton |
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of Geneva (Switzerland; license GE01) (n=4 mice). |
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Here is an image with examples from the datasets, the distribution of images per dataset, and the keypoint guide. |
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<p align="center"> |
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<img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1690986892069-I1DP3EQU14DSP5WB6FSI/modelcard-TVM.png?format=1500w" width="95%"> |
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</p> |
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## Ethical Considerations |
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• Data was collected with IUCAC or other governmental approval. Each individual dataset used in training reports the ethics approval |
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they obtained. |
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## Caveats and Recommendations |
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• The model may have reduced accuracy in scenarios with extremely varied lighting conditions or atypical mouse characteristics not |
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well-represented in the training data. For example, this dataset only has one set of white mice, therefore it may not generalize well |
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to diverse settings of white lab mice. |
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• Please note that each training dataset was labeled by separate labs and different individuals, therefore while we map names to a |
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unified pose vocabulary, there will be annotator bias in keypoint placement (See Ye et al. 2023 for our Supplementary Note on |
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annotator bias). |
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• Note the dataset is primarily using C56Blk6/J mice and only some CD1 examples. |
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• We recommend if performance is not as good as you need it to be, first try video adaptation (see Ye et al. 2023), or fine-tune these |
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weights with your own labeling. |
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## License |
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Modified MIT. |
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Copyright 2023 by Mackenzie Mathis, Shaokai Ye, and contributors. |
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Permission is hereby granted to you (hereafter "LICENSEE") a fully-paid, non-exclusive, |
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and non-transferable license for academic, non-commercial purposes only (hereafter “LICENSE”) |
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to use the "MODEL" weights (hereafter "MODEL"), subject to the following conditions: |
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The above copyright notice and this permission notice shall be included in all copies or substantial |
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portions of the Software: |
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This software may not be used to harm any animal deliberately. |
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LICENSEE acknowledges that the MODEL is a research tool. |
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THE MODEL IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING |
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BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. |
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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, |
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WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL |
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OR THE USE OR OTHER DEALINGS IN THE MODEL. |
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If this license is not appropriate for your application, please contact Prof. Mackenzie W. Mathis |
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(mackenzie@post.harvard.edu) and/or the TTO office at EPFL (tto@epfl.ch) for a commercial use license. |
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Please cite **Ye et al** if you use this model in your work https://arxiv.org/abs/2203.07436v2. |
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## References |
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1. Oliver Sturman, Lukas von Ziegler, Christa Schläppi, Furkan Akyol, Mattia Privitera, Daria Slominski, Christina Grimm, Laetitia Thieren, Valerio |
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Zerbi, Benjamin Grewe, et al. Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial |
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solutions. Neuropsychopharmacology, 45(11):1942–1952, 2020. |
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2. Lukas von Ziegler, Oliver Sturman, and Johannes Bohacek. Videos for deeplabcut, noldus ethovision X14 and TSE multi conditioning systems |
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comparisons. https://doi.org/10.5281/zenodo.3608658. Zenodo, January 2020. |
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3. Isaac Chang. Trained DeepLabCut model for tracking mouse in open field arena with topdown view. https://doi.org/10.5281/zenodo.3955216. |
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Zenodo, July 2020. |
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4. Simon RO Nilsson, Nastacia L. Goodwin, Jia Jie Choong, Sophia Hwang, Hayden R Wright, Zane C Norville, Xiaoyu Tong, Dayu Lin, Bran- |
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don S. Bentzley, Neir Eshel, Ryan J McLaughlin, and Sam A. Golden. Simple behavioral analysis (simba) – an open source toolkit for computer |
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classification of complex social behaviors in experimental animals. bioRxiv, 2020. |
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5. Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mohammed Mostafizur Rahman, Valentina Di Santo, |
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Daniel Soberanes, Guoping Feng, Venkatesh N. Murthy, George Lauder, Catherine Dulac, Mackenzie W. Mathis, and Alexander Mathis. Multi- |
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animal pose estimation, identification and tracking with deeplabcut. Nature Methods, 19:496 – 504, 2022. |
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6. Alexander Mathis, Pranav Mamidanna, Kevin M Cury, Taiga Abe, Venkatesh N Murthy, Mackenzie Weygandt Mathis, and Matthias Bethge. Deeplab- |
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cut: markerless pose estimation of user-defined body parts with deep learning. Nature neuroscience, 21:1281–1289, 2018. |
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7. Jared M. Cregg, Roberto Leiras, Alexia Montalant, Paulina Wanken, Ian R. Wickersham, and Ole Kiehn. Brainstem neurons that command |
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mammalian locomotor asymmetries. Nature neuroscience, 23:730 – 740, 2020 |
