# Social LEAP Estimates Animal Poses (SLEAP)
[](https://github.com/talmolab/sleap)
[](https://github.com/talmolab/sleap/releases/)
[](https://pypi.org/project/sleap)
[](https://www.nature.com/articles/s41592-022-01426-1)
**SLEAP** is an open-source deep learning framework for multi-animal pose tracking ([Pereira et al., Nature Methods, 2022](https://www.nature.com/articles/s41592-022-01426-1)). It provides an end-to-end workflow from labeling to trained models, with a purpose-built GUI for active learning and proofreading.
## ✨ Features
- **Easy installation** – One-line install with support for all major OSes
- **Powerful GUI** – Human-in-the-loop workflow for rapidly labeling large datasets
- **Flexible models** – Single and multi-animal pose estimation with top-down and bottom-up strategies
- **Customizable architectures** – Neural networks that deliver accurate predictions with very few labels
- **Fast training** – 15-60 mins on a single GPU for typical datasets
- **Fast inference** – Up to 600+ FPS batch processing, <10ms realtime latency
- **Modern backends** – [`sleap-io`](https://io.sleap.ai) for data handling and PyTorch-based [`sleap-nn`](https://nn.sleap.ai) for training
**Let's Get Some SLEAP!** 🐭🐭
---
## 📚 Explore the Docs
- :material-map:{ .lg .middle } **Workflow Overview**
---
End-to-end workflow with links to all resources.
[:octicons-arrow-right-24: View Workflow](overview.md)
- :material-school:{ .lg .middle } **Tutorial**
---
Step-by-step guide from labeling to tracking.
[:octicons-arrow-right-24: Start Learning](tutorial/overview.md)
- :material-book-open-variant:{ .lg .middle } **Guides**
---
Advanced workflows and best practices.
[:octicons-arrow-right-24: Explore Guides](guides/guides-overview.md)
- :material-notebook:{ .lg .middle } **Notebooks**
---
Jupyter notebooks for training, analysis, and more.
[:octicons-arrow-right-24: Browse Notebooks](notebooks/notebooks-overview.md)
- :material-lightbulb-on:{ .lg .middle } **Learning**
---
GUI, training options, skeleton design, and more.
[:octicons-arrow-right-24: Deep Dive](learnings/system-overview.md)
- :material-bookshelf:{ .lg .middle } **Reference**
---
CLI, datasets, and full API.
[:octicons-arrow-right-24: Reference Docs](api/index.md)
---
## 🚀 Get some SLEAP!
### Quick start
Install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) first:
```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
uv tool install --python 3.13 "sleap[nn]" --torch-backend auto
```
Launch the GUI:
```bash
sleap
```
[:octicons-arrow-right-24: Full installation instructions](installation.md)
### Learn to SLEAP
- **Learn step-by-step:** [Tutorial](tutorial/overview.md)
- **Learn more advanced usage:** [Guides](guides/guides-overview.md) and [Notebooks](notebooks/notebooks-overview.md)
- **Learn by watching:** [ABL:AOC 2023 Workshop](https://www.youtube.com/watch?v=BfW-HgeDfMI) and [MIT CBMM Tutorial](https://cbmm.mit.edu/video/decoding-animal-behavior-through-pose-tracking)
- **Learn by reading:** [Paper (Pereira et al., Nature Methods, 2022)](https://www.nature.com/articles/s41592-022-01426-1) and [Review on behavioral quantification (Pereira et al., Nature Neuroscience, 2020)](https://rdcu.be/caH3H)
- **Learn from others:** [Discussions on Github](https://github.com/talmolab/sleap/discussions)
---
## 🔄 Coming from SLEAP 1.4 or earlier?
!!! tip "New in SLEAP v1.5+"
SLEAP v1.5+ introduced major changes including UV-based installation, PyTorch backend via `sleap-nn`, and modular data workflows with `sleap-io`. Check out [Migration Guide](guides/migrating-to-sleap-1-5.md)!
| SLEAP ≤ 1.4 | SLEAP 1.5+ |
|-------------|------------|
| Conda installation | UV or pip installation |
| TensorFlow backend | PyTorch backend (`sleap-nn`) |
| Monolithic package | Modular: GUI + `sleap-nn` + `sleap-io` |
!!! warning "Legacy Documentation"
If you are using SLEAP version 1.4.1 or earlier, please visit the [legacy documentation](https://legacy.sleap.ai).
---
## Get Help
- :material-help-circle:{ .lg } **Help Page**
Common issues and solutions. [View Help](help.md)
- :fontawesome-brands-github:{ .lg } **Report Issues**
Found a bug? [Create an issue](https://github.com/talmolab/sleap/issues/new?template=bug_report.md)
- :material-forum:{ .lg } **Discussions**
Questions? [Start a discussion](https://github.com/talmolab/sleap/discussions)
---
## References
SLEAP is the successor to the single-animal pose estimation software [LEAP (Pereira et al., Nature Methods, 2019)](https://www.nature.com/articles/s41592-018-0234-5). If you use SLEAP in your research, please cite:
> T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D'Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy. **SLEAP: A deep learning system for multi-animal pose tracking.** *Nature Methods*, 19(4), 2022. [:octicons-link-external-16:](https://www.nature.com/articles/s41592-022-01426-1)
??? note "BibTeX"
```bibtex
@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}
}
```
---
## Contributors
SLEAP was created in the [Murthy](https://murthylab.princeton.edu) and [Shaevitz](https://shaevitzlab.princeton.edu) labs at the [Princeton Neuroscience Institute](https://pni.princeton.edu) at Princeton University.
SLEAP is currently being developed and maintained in the [Talmo Lab](https://talmolab.org) at the [Salk Institute for Biological Studies](https://salk.edu), in collaboration with the Murthy and Shaevitz labs at Princeton University.
??? note "Contributors & Funding"
**Contributors.**
- **Talmo Pereira**, Salk Institute for Biological Studies
- **Liezl Maree**, Salk Institute for Biological Studies
- **Arlo Sheridan**, Salk Institute for Biological Studies
- **Arie Matsliah**, Princeton Neuroscience Institute, Princeton University
- **Nat Tabris**, Princeton Neuroscience Institute, Princeton University
- **David Turner**, Research Computing and Princeton Neuroscience Institute, Princeton University
- **Joshua Shaevitz**, Physics and Lewis-Sigler Institute, Princeton University
- **Mala Murthy**, Princeton Neuroscience Institute, Princeton University
**Funding**
This work was made possible through our funding sources, including:
- NIH BRAIN Initiative R01 NS104899
- Princeton Innovation Accelerator Fund
---
## License
SLEAP is released under a [BSD 3-Clause Clear License](https://github.com/talmolab/sleap/blob/main/LICENSE).