Name: 자동화된 행동 분석을 위한 딥비헤이비어, 딥 러닝 툴박스의 단계별 구현 (동영상) | JoVE Journal
Uploaded: 2020-02-06T15:00:00.000+00:00
Duration: 5 min 41 s
Description: 학습하세요 딥러닝 기반 자동 행동 분석 방법을 사용하여 영상 데이터에서 객체를 추적하고 정량화합니다. 이 기술은 신경과학 연구에서 주관성을 줄이고 효율성을 높입니다.

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We would like to thank Pingping Zhao and Peyman Golshani for providing the raw data for two-mouse social interaction tests used in the original paper2. This study was supported by NIH NS109315 and NVIDIA GPU grants (AA).

1. GPU and Python Setup
<ol>
	<li>GPU Software 
	When the computer is first setup for deep learning applications, GPU-appropriate software and drivers should be installed which can be found on the GPU&#39;s respective website. (see the Table of Materials for those used in this study).</li>
	<li>Python 2.7 Installation 
	Open a command line prompt on your machine. 
	Command line: sudo apt-get install python-pip python-dev python-virtualenv</li>
</ol>
2. TENSORBOX
<ol>
	<li>Tensorbox Setup
	<ol>
		<li>Create Virtual Environment for Tensorbox 
		Command line: cd ~ 
		Command line: virtualenv --system-site-packages ~/tensorflow 
		NOTE: &#39;~/tensorflow&#39; is the name of the environment and is arbitrary</li>
		<li>Activate environment 
		Command line: source ~/tensorflow/bin/activate</li>
	</ol>
	</li>
	<li>Tensorbox Installation 
	We will be using GitHub to clone TensorBox from <a href="http://github.com/aarac/TensorBox" target="_blank">http://github.com/aarac/TensorBox</a> and install it on our machine as well as installing additional dependencies. 
	Command line: cd ~ 
	Command line: git clone <a href="http://github.com/aarac/TensorBox" target="_blank">http://github.com/aarac/TensorBox</a> 
	Command line: cd TensorBox 
	Command line: pip install -r requirements.txt</li>
	<li>Label Data
	<ol>
		<li>Create a folder of images of behavior 
		Open source tools such as ffmpeg are useful to accomplish converting videos to individual frames We recommend labeling at least 600 images from a wide-distribution of behavior frames for training. Put these images in a folder.</li>
		<li>Launch labeling graphical user interface 
		Command line: python make_json.py &#60;path to image folder&#62; labels.json 
		To label an image, click the top left corner of the object of interest (i.e. paw) first and then click the bottom right corner of the object of interest (Figure 4). Inspect that the bounding box captures the entire object of interest. Press &#39;undo&#39; to re-label the same image or press &#39;next&#39; to move onto the next frame.</li>
	</ol>
	</li>
	<li>Train TensorBox
	<ol>
		<li>Link training images to network hyperparameters file 
		Within the tensorbox folder, open the following folder in a text editor: 
		/TensorBox/hypes/overfeat_rezoom.json. Navigate to the attribute under data named train_idl and replace the file path from ./data/brainwash/train_boxes.json to the labels.json filepath. Save the changes to file.</li>
		<li>Begin training script 
		Command line: cd ~/TensorBox 
		Command line: python train.py --hypes hypes/overfeat_rezoom.json --gpu 0 --logdir output 
		The network will then begin training for 600,000 iterations. In the output folder, the resulting trained weights of the convolutional neural network will be generated.</li>
	</ol>
	</li>
	<li>Predict on New Images 
	For image labeling: 
	Command line: cd ~/TensorBox 
	Command line: python label_images.py --folder &#60;path to image folder&#62; --weights output/overfeat_rezoom_&#60;timestamp&#62;/save.ckpt-600000 --hypes /hypes/overfeat_rezoom.json --gpu 0 
	To get coordinates of bounding boxes: 
	Command line: cd ~/TensorBox 
	Command line: python predict_images_to_json.py --folder &#60;path to image folder&#62; --weights 
	output/overfeat_rezoom_&#60;timestamp&#62;/save.ckpt-600000 --hypes 
	/hypes/overfeat_rezoom.json --gpu 0</li>
	<li>MATLAB Post-Processing for TensorBox 
	Additional MATLAB code has been provided to extract kinematics and visualizations of the coordinates using the resulting JSON coordinate file from the model 
	Run the &#34;Process_files_3Dreaching_mouse.m&#34; script for 3D kinematic analysis of single food pellet reaching task.</li>
</ol>
3. YOLOv3
<ol>
	<li>Install YOLOv3 
	Command line: cd ~ 
	Command line: git clone cd darknet 
	For GPU usage, open &#39;Makefile&#39; and change the following lines: GPU=1; CUDNN=1. 
	Command line: make</li>
	<li>Labeling Training Data using Yolo_mark 
	Command line: cd ~ 
	Command line: git clone cd ~/Yolo_Mark 
	Command line: cmake . 
	Command line: make 
	Place the training images in ~/Yolo_mark/data/obj folder 
	Command line: chmod +x ./linux_mark.sh 
	Command line: ./linux_mark.sh 
	Label the images one by one in the graphical user interface (Figure 5). The recommended amount of images is approximately 200.</li>
	<li>Training YOLOv3
	<ol>
		<li>Setup configuration file 
		Command line: cd ~/Yolo_mark 
		Command line: scp -r ./data ~/darknet 
		Command line: cd ~/darknet/cfg 
		Command line: cp yolov3.cfg yolo-obj.cfg</li>
		<li>Modify the configuration file 
		Open the yolo-obj.cfg folder and modify the following lines: batch=64, subdivision=8, classes=(# of class to detect), and for each convolutional layer before a yolo layer change the filter=(classes+5)x3. Details on these changes can be found at <a href="https://github.com/aarac/darknet/blob/master/README.md" target="_blank">https://github.com/aarac/darknet/blob/master/README.md</a></li>
		<li>Download network weights 
		Download the network weights from <a href="https://www.dropbox.com/s/613n2hwm5ztbtuf/darknet53.conv.74?dl=0" target="_blank">https://www.dropbox.com/s/613n2hwm5ztbtuf/darknet53.conv.74?dl=0</a> 
		Place the downloaded weight file into ~/darknet/build/darknet/x64</li>
		<li>Run training algorithm 
		Command line: cd ~/darknet 
		Command line: ./darknet detector train data/obj.data cfg/yolo-obj.cfg darknet53.conv.74</li>
		<li>YOLOv3 Evaluation 
		After the training is complete based on a set number of iterations (ITERATIONNUMBER), you can view them by 
		Command line: ./darknet detector test data/obj.data cfg/yolo-obj.cfg backup/yolo-obj_ITERATIONNUMBER.weights &#60;IMAGE&#62;.jpg</li>
	</ol>
	</li>
	<li>Predict on new videos and get coordinates 
	This command can be run to obtain the coordinates of the labels in the new video: 
	Command line: ./darknet detector demo data/obj.data cfg/yolo-obj.cfg backup/yolo-obj_ITERATIONNUMBER.weights VIDEO.avi -ext_output &#60;VIDEO.avi&#62; FILENAME.txt</li>
	<li>YOLOv3 PostProcessing in MATLAB 
	Take the FILENAME.txt file to MATLAB, and run the &#34;Process_socialtest_mini.m&#34; script for two mice social interaction test. See results in Figure 2</li>
</ol>
4. OpenPose
OpenPose is ideal to track multiple body parts in a human subject. The setup and installation processes are very similar to the previous two frameworks. However, there is no training step as the network is already trained on human data.
<ol>
	<li>OpenPose Installation 
	Navigate to <a href="https://github.com/aarac/openpose" target="_blank">https://github.com/aarac/openpose</a> and follow the installation instructions.</li>
	<li>Process Video 
	./build/examples/openpose/openpose.bin --video VIDEONAME.avi --net_resolution &#34;1312x736&#34; --scale_number 4 --scale_gap 0.25 --hand --hand_scale_number 6 --hand_scale_range 0.4 --write_json JSONFOLDERNAME --write_video RESULTINGVIDEONAME.avi 
	Here the --net_resolution, --scale_number, --scale_gap, --hand_scale_number and --hand_scale_range handles can be omitted if a high precision detection is not needed (this would decrease the processing time).</li>
	<li>OpenPose Post-Processing 
	In MATLAB folder, please use &#39;process_files_human3D.m&#39; script to run the code after adding the appropriate folder containing json files from cameras 1 and 2, as well as the calibration file. This will create a &#34;cell&#34; file with all the 3D poses of the joints. It will also make a movie of the 3D skeletal view. For camera calibration, please follow the instructions at this link: <a href="http://www.vision.caltech.edu/bouguetj/calib_doc/" target="_blank">http://www.vision.caltech.edu/bouguetj/calib_doc/</a></li>
</ol>

<ol><li>Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., Poeppel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Neuroscience+Needs+Behavior%3A+Correcting+a+Reductionist+Bias%5BTitle%5D)+AND+%22Neuron%22%5BJournal%5D)">Neuroscience Needs Behavior: Correcting a Reductionist Bias.</a> Neuron. 93 (3), 480-490 (2017).</li>
<li>Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., Golshani, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DeepBehavior%3A+A+Deep+Learning+Toolbox+for+Automated+Analysis+of+Animal+and+Human+Behavior+Imaging+Data%5BTitle%5D)+AND+%22Front+Syst+Neurosci%22%5BJournal%5D)">DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data.</a> Front Syst Neurosci. 13, 20(2019).</li>
<li>Pereira, T. D., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S., Murthy, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fast+animal+pose+estimation+using+deep+neural+networks%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Fast animal pose estimation using deep neural networks.</a> Nat Methods. 16 (1), 117-125 (2019).</li>
<li>Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DeepLabCut%3A+markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning%5BTitle%5D)+AND+%22Nat+Neurosci%22%5BJournal%5D)">DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.</a> Nat Neurosci. 21 (9), 1281-1289 (2018).</li>
<li>Stern, U., He, R., Yang, C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Analyzing+animal+behavior+via+classifying+each+video+frame+using+convolutional+neural+networks%5BTitle%5D)+AND+%22Sci+Rep%22%5BJournal%5D)">Analyzing animal behavior via classifying each video frame using convolutional neural networks.</a> Sci Rep. 5, 14351(2015).</li>
<li>Tinbergen, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((On+aims+and+methods+of+ethology%5BTitle%5D)+AND+%22Zeitschrift+fu%CC%88r+Tierpsychologie%22%5BJournal%5D)">On aims and methods of ethology.</a> Zeitschrift für Tierpsychologie. 20, 410-433 (1963).</li>
<li>LeCun, Y., Bengio, Y., Hinton, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Deep+Learning%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Deep Learning.</a> Nature. 521 (7553), 436-444 (2015).</li>
<li>Zhao, Z., Zheng, P., Xu, S., Wu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Object+Detection+With+Deep+Learning%3A+A+Review%5BTitle%5D)+AND+%22IEEE+Transactions+on+Neural+Networks+and+Learning+Systems%22%5BJournal%5D)">Object Detection With Deep Learning: A Review.</a> IEEE Transactions on Neural Networks and Learning Systems. , 1-21 (2019).</li>
<li>He, K., Zhang, X., Ren, S., Deep Sun, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Residual+Learning+for+Image+Recognition%5BTitle%5D)+AND+%22arXiv%22%5BJournal%5D)">Residual Learning for Image Recognition.</a> arXiv. , eprint (2015).</li>
<li>Krizhevsky, A., Sutskever, I., Hinton, G. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ImageNet+classification+with+deep+convolutional+neural+networks%5BTitle%5D)+AND+%22Proceedings+of+the+25th+International+Conference+on+Neural+Information+Processing+Systems%22%5BJournal%5D)">ImageNet classification with deep convolutional neural networks.</a> Proceedings of the 25th International Conference on Neural Information Processing Systems. 1, Curran Associates Inc. Lake Tahoe, Nevada. 1097-1105 (2012).</li>
<li>Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Going+deeper+with+convolutions%5BTitle%5D)+AND+%222015+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29%22%5BJournal%5D)">Going deeper with convolutions.</a> 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). , 7-12 (2015).</li>
<li>Stewart, R., Andriluka, M., Ng, A. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((End-to-End+People+Detection+in+Crowded+Scenes%5BTitle%5D)+AND+%222016+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29%22%5BJournal%5D)">End-to-End People Detection in Crowded Scenes.</a> 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). , 27-30 (2016).</li>
<li>Redmon, J., Farhadi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((YOLOv3%3A+An+Incremental+Improvement%5BTitle%5D)+AND+%22arXiv%22%5BJournal%5D)">YOLOv3: An Incremental Improvement.</a> arXiv. , eprint (2018).</li>
<li>Cao, Z., Simon, T., Wei, S. E., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Realtime+Multi-Person+2D+Pose+Estimation+using+Part+Affinity+Fields%5BTitle%5D)+AND+%22arXiv%22%5BJournal%5D)">Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields.</a> arXiv. , (2017).</li>
<li>Simon, T., Joo, H., Matthews, I., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hand+Keypoint+Detection+in+Single+Images+using+Multiview+Bootstrapping%5BTitle%5D)+AND+%22arXiv%22%5BJournal%5D)">Hand Keypoint Detection in Single Images using Multiview Bootstrapping.</a> arXiv. , eprint (2017).</li>
<li>Wei, S. E., Ramakrishna, V., Kanade, T., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Convolutional+Pose+Machines%5BTitle%5D)+AND+%22arXiv%22%5BJournal%5D)">Convolutional Pose Machines.</a> arXiv. , eprint (2016).</li>
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The authors have nothing to disclose.

Here, we provide a step-by-step guide for implementation of DeepBehavior, our recently developed deep learning based toolbox for animal and human behavior imaging data analysis2. We provide detailed explanations for each step for installation of the frameworks for each network architecture, and provide links for installation of the open-source requirements to be able to run these frameworks. We demonstrate how to install them, how to create training data, how to train the network, and how to proce...

A detailed analysis of behavior is key to understanding the brain and behavior relationships. There have been many exciting advances in methodologies for recording and manipulating neuronal populations with high temporal resolution, however, behavior analysis methods have not developed at the same rate and are limited to indirect measurements and a reductionist approach1. Recently, deep learning based methods have been developed to perform automated and detailed behavior analysis2,3,4,5. This protocol provides a step-by-step implementation guide for the DeepBehavior toolbox.
Traditional behavioral analysis methods often include manually labeling data by multiple evaluators, leading to variance in how experimenters define a behavior6. Manual labeling of the data requires time and resources that increase disproportionately to the amount of data collected. Moreover, manually labelled data reduce the behavior outcomes into categorical measurements which do not capture the richness of the behavior, and will be more subjective. Thus, the current traditional methods may be limited in capturing the details in the natural behaviors.
The DeepBehavior toolbox presents a precise, detailed, highly temporal, and automated solution using deep learning for behavioral analysis. Deep learning has quickly become accessible to all with open-source tools and packages. Convolutional neural networks (CNNs) are proven to be highly effective in object recognition and tracking tasks7,8. Using modern day CNNs and high-performance graphics-processing-units (GPUs), large image and video datasets can be processed quickly with high precision7,9,10,11. In DeepBehavior, there are three different convolutional neural net architectures, TensorBox, YOLOv3, and OpenPose2.
The first framework, Tensorbox, is a versatile framework that incorporates many different CNN architectures for object detection12. TensorBox is best suited for detecting only one object class per image. The resulting outputs are bounding boxes of the object of interest (Figure 1) and the cartesian coordinates of the bounding box.
The second CNN framework is YOLOv3, which stands for &#34;You Only Look Once&#34;13. YOLOv3 is advantageous when there are multiple objects of interest that must be tracked separately. The output of this network includes the bounding box with the associated object label class as well as the bounding box cartesian coordinates of the object in the video frame (Figure 2).
The previous two frameworks are advantageous for generalized behavioral data collected from standard laboratory experiments in animal subjects. The last CNN framework is OpenPose14,15,16 which is used for human joint pose estimation. OpenPose detects human body, hand, facial, and foot key points on images. The outputs of the framework are labeled images of the human subject as well as the coordinates of all the 25 key points in the body and 21 key points of each hand (Figure 3).
This detailed step-by-step guide for implementation of our recently developed open-source DeepBehavior toolbox employs state-of-the-art convolutional neural nets to track animal behavior (e.g. movement of a paw) or human behavior (e.g. reaching tasks). By tracking the behavior, useful kinematics can be derived from the behavior such as position, velocity, and acceleration. The protocol explains the installation of each CNN architecture, demonstrates how to create training datasets, how to train the networks, how to process new videos on the trained network, how to extract the data from the network on the new videos, and how to post-process the output data to make it useful for further analysis.

<table><tbody><tr><td>CUDA v8.0.61</td><td>NVIDIA</td><td>n/a</td><td>GPU Software</td></tr><tr><td>MATLAB R2016b</td><td>Mathworks</td><td>n/a</td><td>Matlab</td></tr><tr><td>Python 2.7</td><td>Python</td><td>n/a</td><td>Python Version</td></tr><tr><td>Quadro P6000</td><td>NVIDIA</td><td>n/a</td><td>GPU Processor</td></tr><tr><td>Ubuntu v16.04</td><td>Ubuntu</td><td>n/a</td><td>Operating System</td></tr></tbody></table>

a step-by-step implementation of deepbehavior, deep learning toolbox for automated behavior analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods often do not capture the richness inherent to the natural behavior. Here, we provide detailed step-by-step instructions with visualizations of our recent methodology, DeepBehavior. The DeepBehavior toolbox uses deep learning frameworks built with convolutional neural networks to rapidly process and analyze behavioral videos. This protocol demonstrates three different frameworks for single object detection, multiple object detection, and three-dimensional (3D) human joint pose tracking. These frameworks return cartesian coordinates of the object of interest for each frame of the behavior video. Data collected from the DeepBehavior toolbox contain much more detail than traditional behavior analysis methods and provides detailed insights to the behavior dynamics. DeepBehavior quantifies behavior tasks in a robust, automated, and precise way. Following the identification of behavior, post-processing code is provided to extract information and visualizations from the behavioral videos.

When the protocol is followed, the data for each network architecture should be similar to the following. For TensorBox, it outputs a bounding box around the object of interest. In our example, we used videos from a food pellet reaching task, and labeled the right paws to track their movement. As seen in Figure 1, the right paw can be detected in different positions in both the front view and side view cameras. After post-processing with camera calibration, 3...

The purpose of this protocol is to utilize pre-built convolutional neural nets to automate behavior tracking and perform detailed behavior analysis. Behavior tracking can be applied to any video data or sequences of images and is generalizable to track any user-defined object.

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods ...

Performing a detailed behavior analysis is crucial to understanding brain behavior relationship. One of the best ways to evaluate behavior is through careful observations. However, quantifying the observed behavior is time consuming and challenging.Classical methods of behavior analysis are not easily quantifiable and are inherently subjective. Recent developments in deep learning, a branch of machine learning and artificial intelligence fields, provide opportunities for automated and objective quantification of images and videos. Here we present our recently developed methods utilizing deep neural networks to perform detailed behavior analysis in rodents and humans.The main advantage of this technique are its flexibility and applicability to any imaging data for behavior analysis. The DeepBehavior Toolbox supports single object identification, multi object detection, and human pose tracking. We also provide the post processing code in MATLAB for more in-depth kinematic analysis methods.Begin by setting up Tensor Box. Activate the environment, then use GitHub to clone Tensor Box and install it on the machine and on additional dependencies. Next, launch the labeling graphical user i

a-step-step-implementation-deepbehavior-deep-learning-toolbox-for

Research

JoVE Journal

Behavior

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Intra-Operative Behavioral Tasks in Awake Humans Undergoing Deep Brain Stimulation Surgery

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Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station (INBEST)

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A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior

This protocol describes how to build a small and versatile video camera, and how to use videos obtained from it to train a neural network to track the position of an animal inside operant conditioning chambers. This is a valuable complement to standard analyses of data logs obtained from operant conditioning tests.

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

This protocol provides a method for tracking automated eye squint in rodents over time in a manner compatible with time-locking to neurophysiological measures. This protocol is expected to be useful to researchers studying mechanisms of pain disorders such as migraine.

An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies

This protocol gives an integrated framework based on advanced computational neuroethological methods to understand brain coding in naturalistic contexts.

Decoding Natural Behavior from Neuroethological Embedding

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Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Fagot &amp; Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems

학습하세요 딥러닝 기반 자동 행동 분석 방법을 사용하여 영상 데이터에서 객체를 추적하고 정량화합니다. 이 기술은 신경과학 연구에서 주관성을 줄이고 효율성을 높입니다.

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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