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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A deep learning framework for multi-animal pose tracking.
| Date | Stars |
|---|---|
| 2026-07-31 | 606 |
| 2026-08-05 | 606 |
| 2026-08-06 | 606 |
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35.0
growth rate 0.00%/day
[](https://github.com/talmolab/sleap/actions/workflows/ci.yml) [](https://codecov.io/gh/talmolab/sleap) [](https://docs.sleap.ai) [](https://pepy.tech/project/sleap) [](https://github.com/talmolab/sleap/releases/) [](https://github.com/talmolab/sleap/releases/) # Social LEAP Estimates Animal Poses (SLEAP)  **SLEAP** is an open-source deep-learning based framework for multi-animal pose tracking [(Pereira et al., Nature Methods, 2022)](https://www.nature.com/articles/s41592-022-01426-1). It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading. ## Features * Easy, one-line installation with support for all OSes * Purpose-built GUI and human-in-the-loop workflow for rapidly labeling large datasets * Single- and multi-animal pose estimation with *top-down* and *bottom-up* training strategies * Customizable neural network architectures that deliver *accurate predictions* with *very few* labels * Fast training: 15 to 60 mins on a single GPU for a typical dataset * Fast inference: up to 600+ FPS for batch, <10ms latency for realtime * Support for remote training/inference workflow (for using SLEAP without GPUs) * Flexible developer API for building integrated apps and customization * Two independent backends— [`sleap-nn`](https://nn.sleap.ai) and [`sleap-io`](https://io.sleap.ai) for training/inference pipelines & handling SLEAP files respectively ## Get some SLEAP SLEAP is installed as a Python package. We strongly recommend using [uv](https://docs.astral.sh/uv/) to install SLEAP in its own environment. You can find the latest version of SLEAP in the [Releases](https://github.com/talmolab/sleap/releases) page. ### Quick install > **Python 3.14 is not yet supported** > > SLEAP currently supports **Python 3.11, 3.12, and 3.13**. > **Python 3.14 is not yet tested or supported.** > By default, `uv` will use your system-installed Python. > If you have Python 3.14 installed, you must specify the Python version (≤3.13) in the install command. > > For example: > > ```bash > uv tool install --python 3.13 "sleap[nn]" ... > ``` > Replace `...` with the rest of your install command as needed. **`uv tool install` (any OS):** First, install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) if you haven't already: ```bash # macOS/Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Windows powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" ``` Then install SLEAP: ```bash # Windows/Linux CUDA 12.8 uv tool install "sleap[nn]" --index https://download.pytorch.org/whl/cu128 --index https://pypi.org/simple # macOS / CPU-only uv tool install "sleap[nn]" --index https://download.pytorch.org/whl/cpu --index https://pypi.org/simple ``` Run the SLEAP GUI after installation: ```bash sleap ``` See the docs for [full installation instructions](https://docs.sleap.ai/latest/installation). ## Learn to SLEAP - **Learn step-by-step:** [Tutorial](https://docs.sleap.ai/latest/tutorial/overview) - **Learn more advanced usage:** [Guides](https://docs.sleap.ai/latest/guides/guides-overview/) and [Notebooks](https://docs.sleap.ai/latest/notebooks/notebooks-overview/) - **Learn by watching:** [COSYNE 2024 Tut
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Lili Karashchuk · Janelia Research Campus
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2cc6497a9a9445a4, llm:Topics: animal-pose-estimation, animal-tracking, behavior-analysis, deep-learning, pose-estimation, sleap. Description/readme: 'A deep learning framework for multi-animal pose tracking', GUI for labeling, training/inference, top-down and bottom-up pose estimation, multi-animal tracking.
matched fp:2cc6497a9a9445a4, llm:Topics: animal-pose-estimation, animal-tracking, behavior-analysis, deep-learning, pose-estimation, sleap. Description/readme: 'A deep learning framework for multi-animal pose tracking', GUI for labeling, training/inference, top-down and bottom-up pose estimation, multi-animal tracking.
matched fp:2cc6497a9a9445a4, llm:Topics: animal-pose-estimation, animal-tracking, behavior-analysis, deep-learning, pose-estimation, sleap. Description/readme: 'A deep learning framework for multi-animal pose tracking', GUI for labeling, training/inference, top-down and bottom-up pose estimation, multi-animal tracking.