Label a few video frames of your animal and train a model to automatically track body parts across hours of footage.
Run markerless pose estimation on any animal species to study movement and behavior for academic research.
Use pretrained models from the model zoo to skip labeling entirely for common animals like mice or flies.
Automate video analysis pipelines using the Python API to process large batches of experimental recordings.
GPU is strongly recommended for training, requires Python 3.10+ and either PyTorch or TensorFlow, with optional Docker setup.
DeepLabCut is a tool built for scientists and researchers who want to track the movement of animals in video without attaching physical markers to them. Instead of gluing dots onto a mouse or fly, a researcher labels a few body parts in a small set of video frames, and the software learns to find those same points in the rest of the video automatically. The result is a dataset of positions over time, which researchers use to study behavior, movement patterns, and neuroscience. The core idea is markerless pose estimation. You define which points matter for your experiment, such as the tip of a nose, a paw, or a joint, label them in sample images using the built-in labeling tool, and then train a neural network on those labeled examples. Once trained, the model can track those points across hours of video at speeds far beyond what a human annotator could manage. The toolbox works on any animal, including humans, and is not limited to specific species or body configurations. The workflow has several steps: create a project, label frames, train a model, evaluate its accuracy, and then analyze new videos. There is a graphical interface for people who prefer not to work in code, as well as a Python API for scripting and automation. Notebooks for Google Colab are provided so you can run training on cloud GPUs if your own computer does not have one. A model zoo offers pretrained models for common animals so you do not always have to label from scratch. The software requires Python 3.10 or later and can run with either PyTorch or TensorFlow as the underlying deep learning engine. GPU support is optional but strongly recommended for training, since it reduces training time from days to hours. Installation is via pip or conda, and Docker images are also provided. DeepLabCut is used in published academic research and is backed by the Mackenzie Mathis Lab. It is released under the LGPL v3 license and is actively maintained with community forums, an online course, and regular updates.
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