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4 changes: 2 additions & 2 deletions content/tools/community/deeplabcut.md
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title: "DeepLabCut"
description: "An efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data."
category: "behavior-analysis"
image: "/images/tools/deeplabcut/deeplabcut.gif"
image: "/images/tools/deeplabcut/deeplabcut.webp"
source_url: "https://github.com/DeepLabCut/DeepLabCut"
docs_url: "http://www.mackenziemathislab.org/deeplabcut"
weight: 190
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DeepLabCut is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks. It achieves excellent results (matching human labeling accuracy) with minimal training data (typically 50-200 frames). The framework has been proven versatile in tracking various body parts in multiple species across a broad collection of behaviors.

![DeepLabCut Demo](/images/tools/deeplabcut/deeplabcut.gif)
<video autoplay loop muted playsinline width="300" height="224" aria-label="DeepLabCut Demo" style="width: 100%; height: auto;"><source src="/images/tools/deeplabcut/deeplabcut.mp4" type="video/mp4"></video>

## NWB Integration

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2 changes: 1 addition & 1 deletion content/tools/community/nwbexplorer.md
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## Demo

![NWB Explorer Demo](/images/tools/nwbexplorer/nwbexplorer.gif)
<video autoplay loop muted playsinline width="600" height="338" aria-label="NWB Explorer Demo" style="width: 100%; height: auto;"><source src="/images/tools/nwbexplorer/nwbexplorer.mp4" type="video/mp4"></video>

You can try NWB Explorer online at [https://nwbexplorer.v2.opensourcebrain.org](https://nwbexplorer.v2.opensourcebrain.org).

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2 changes: 1 addition & 1 deletion content/tools/community/nwbwidgets.md
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## Demo

![NWB Widgets Demo](/images/tools/nwbwidgets/jupyter_widgets_demo.gif)
<video autoplay loop muted playsinline width="1006" height="872" aria-label="NWB Widgets Demo" style="width: 100%; height: auto;"><source src="/images/tools/nwbwidgets/jupyter_widgets_demo.mp4" type="video/mp4"></video>

## Installation

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4 changes: 2 additions & 2 deletions content/tools/community/sleap.md
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title: "SLEAP"
description: "An open source deep-learning based framework for multi-animal pose tracking, capable of tracking any type or number of animals with an advanced labeling/training GUI for active learning and proofreading."
category: "behavior-analysis"
image: "/images/tools/sleap/sleap.gif"
image: "/images/tools/sleap/sleap.webp"
source_url: "https://github.com/talmolab/sleap"
docs_url: "https://sleap.ai"
weight: 200
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SLEAP is an open source deep-learning based framework for multi-animal pose tracking. It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading.

![SLEAP Demo](/images/tools/sleap/sleap.gif)
<video autoplay loop muted playsinline width="600" height="278" aria-label="SLEAP Demo" style="width: 100%; height: auto;"><source src="/images/tools/sleap/sleap.mp4" type="video/mp4"></video>

## NWB Integration

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2 changes: 1 addition & 1 deletion content/tools/community/suite2p.md
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Suite2p is an imaging processing pipeline written in Python. It provides a comprehensive set of tools for analyzing calcium imaging data.

![Suite2p Demo](/images/tools/suite2p/multiselect.gif)
<video autoplay loop muted playsinline width="1600" height="854" aria-label="Suite2p Demo" style="width: 100%; height: auto;"><source src="/images/tools/suite2p/multiselect.mp4" type="video/mp4"></video>

## Features

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2 changes: 1 addition & 1 deletion content/tools/community/visiomode.md
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Visiomode session data can be exported to the NWB format directly from the web interface. Navigate to the "History" tab, choose the session you wish to export and select "NWB" from the "Download" dropdown menu.

![Visiomode NWB Export](/images/tools/visiomode/visiomode-nwb-export.gif)
<video autoplay loop muted playsinline width="800" height="484" aria-label="Visiomode NWB Export" style="width: 100%; height: auto;"><source src="/images/tools/visiomode/visiomode-nwb-export.mp4" type="video/mp4"></video>

### Data Structure

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