Help#

Stuck? Can’t get SLEAP to run? Crashing? Try the recommended tips below.

Installation#

I can’t get SLEAP to install!#

Have you tried all of the steps in the installation instructions?

If so, please feel free to start a discussion or open an issue and tell us how you’re trying to install it, what error messages you’re getting and which operating system you’re on.

Can I install it on a computer without a GPU?#

Yes! You can install SLEAP as you normally would using the conda or pip-based methods and the GPU support will be ignored.

What if I already have CUDA set up on my system?#

You can use the system CUDA installation by simply using the installation method.

Note that you will need to use a version compatible with TensorFlow 2.6+ (CUDA Toolkit v11.3 and cuDNN v8.2).

Usage#

How do I use SLEAP?#

If you’re new to pose tracking in general, check out this talk or our review in Nature Neuroscience.

If you’re just new to SLEAP, we suggest starting with the Overview and then following the Tutorial.

Once you get the hang of it, check out the Guides for more detailed info.

Does my data need to be in a particular format?#

SLEAP supports a large number of formats, including all video formats and imported data from DeepLabCut and others.

Many types of video acquisition software, however, do not save videos in a format suitable for computer vision-based processing. A very common issue is that videos are not reliably seekable, meaning that you may not get the same data when trying to read a particular frame index. This is because many video formats are optimized for realtime and sequential playback and save space by reconstructing the image using data in adjacent frames. The consequence is that you may not get the exact same image depending on what the last frame you read was. Check out this blog post for more details.

If you think you may be affected by this issue, or just want to be safe, re-encode your videos using the following command:

ffmpeg -y -i "input.mp4" -c:v libx264 -pix_fmt yuv420p -preset superfast -crf 23 "output.mp4"

Breaking down what this does:

-i "input.mp4": Specifies the path to the input file. Replace this with your video. Can be .avi or any other video format.
-c:v libx264: Sets the video compression to use H264.
-pix_fmt yuv420p: Necessary for playback on some systems.
-preset superfast: Sets a number of parameters that enable reliable seeking.
-crf 23: Sets the quality of the output video. Lower numbers are less lossy, but result in larger files. A CRF of 15 is nearly lossless, while 30 will be highly compressed.
"output.mp4": The name of the output video file (must end in .mp4).

If you don’t have ffmpeg on your system, you can install it using conda install ffmpeg or by downloading it from the official website.

I get strange results where the poses appear to be correct but shifted relative to the image.#

This is most likely an issue with the video compression format. See above.

How do I get predictions out?#

See Export Data For Analysis and sleap-convert.

What do I do with the output of SLEAP?#

Check out the Analysis examples notebooks.

Getting more help#

I’ve found a bug or have another problem!#

Feel free to start a discussion to get help from the developers and community. Or open an issue and we’ll get back to you as soon as we can!

Can I just talk to someone?#

SLEAP is a complex machine learning system intended for general use, so it’s possible that we failed to consider the specifics of the situation in which you may be interested in using it with.

Feel free to reach out to us at talmo@salk.edu if you have a question that isn’t covered here.

Improving SLEAP#

How can I help improve SLEAP?#

Tell your friends about SLEAP! We also love to hear stories about what worked or didn’t work, or your experience if you came from other software tools (talmo@salk.edu).

Cite our paper! Here’s a BibTeX citation for your reference manager:

 @ARTICLE{Pereira2022sleap,
     title={SLEAP: A deep learning system for multi-animal pose tracking},
     author={Pereira, Talmo D and
         Tabris, Nathaniel and
         Matsliah, Arie and
         Turner, David M and
         Li, Junyu and
         Ravindranath, Shruthi and
         Papadoyannis, Eleni S and
         Normand, Edna and
         Deutsch, David S and
         Wang, Z. Yan and
         McKenzie-Smith, Grace C and
         Mitelut, Catalin C and
         Castro, Marielisa Diez and
         D'Uva, John and
         Kislin, Mikhail and
         Sanes, Dan H and
         Kocher, Sarah D and
         Samuel S-H and
         Falkner, Annegret L and
         Shaevitz, Joshua W and
         Murthy, Mala},
     journal={Nature Methods},
     volume={19},
     number={4},
     year={2022},
     publisher={Nature Publishing Group}
     }
 }

Share new ideas for new features or improvements in the Discussion forum.
Contribute some code! See our contribution guidelines for more info.

What is usage data?#

To help us improve SLEAP, you may allow us to collect basic and anonymous usage data. If enabled from the Help menu, the SLEAP GUI will transmit information such as which version of Python and operating system you are running SLEAP on.

This helps us understand on which types of computers SLEAP is being used so we can ensure that our software is maximally accessible to the broadest userbase possible, for example, by telling us whether it’s safe to update our dependencies without breaking SLEAP for most users. Collecting usage data also helps us get a sense for how often SLEAP is being used so that we can report its impact to external grant funding agencies.

You can opt out at any time from the menu (this preference will be stored). If you want to prevent these data from being shared with us, you can launch the GUI with sleap-label --no-usage-data. Usage data is only shared when the GUI is used, not the API or CLIs. You can check out the source code to see exactly what data is collected.

Help

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