--- deeplabcut: last_content_updated: '2025-07-01' last_metadata_updated: '2026-03-06' ignore: false --- (dlc3-user-guide)= # What is new in DeepLabCut 3.0 ## Using DeepLabCut 3.0 **DeepLabCut 3.0 keeps the same high-level API that you know, but has a full new PyTorch backend. Moreover, it is a rewrite that is more developer friendly, more powerful, and built for modern deep learning-based computer vision applications.** **NOTE**🔥: We suggest that if you're just starting with DeepLabCut you start with the PyTorch backend. You will easily know which "engine" you are using by looking at the main `config.yaml` file, or top right corner in the GUI. If you have DeepLabCut projects in TensorFlow, we've got you covered too: you can seamlessly switch to train your already labeled data by simply switching the engine (and thereby also compare performance). In short, expect a boost 🔥. In short, PyTorch models can be trained in any DeepLabCut project. If you have a project already made, simply add a new key to your project `config.yaml` file specifying `engine: pytorch`. Then any new training dataset that will be created will be a PyTorch model (see [Creating Shuffles and Model Configuration](#Creating-Shuffles-and-Model-Configuration)) to learn more about training PyTorch models. To train Tensorflow models again, you can set `engine: tensorflow`. ### Installation To see the DeepLabCut 3.0 installation guide, check the [installation docs](how-to-install). ### Using the GUI You can use the GUI to train DeepLabCut projects. You can switch between the PyTorch and TensorFlow engine through the drop-down menu in the top right corner. ### Quick guide (standard API) The standard use of DLC does not change (via the high-level API), as you can see in the standard guide: for [single](https://deeplabcut.github.io/DeepLabCut/docs/standardDeepLabCut_UserGuide) and [multiple individuals](https://deeplabcut.github.io/DeepLabCut/docs/maDLC_UserGuide). Also check out several COLAB notebooks on how you can use the code. For the ## Major changes ### From iterations to epochs Pytorch models in DeepLabCut 3.0 are trained for a set number of `epochs`, instead of a maximum number of `iterations`. An epoch is a single pass through the training dataset, which means your model has seen each training image exactly once. - So if you have 64 training images for your network, an epoch is 64 iterations with batch size 1 (or 32 iterations with batch size 2, 16 with batch size 4, etc.). ## API ### Creating Shuffles and Model Configuration You can configure models using the `pytorch_config.yaml` file, as described [here](dlc3-pytorch-config). You can use the same methods to create new shuffles in DeepLabCut 3.0 as you did for Tensorflow models (`deeplabcut.create_training_dataset` and `deeplabcut.create_training_model_comparison`). More information about the different PyTorch model architectures available in DeepLabCut is available [here](architectures). You can see a list of supported architectures/variants by using: ```python from deeplabcut.pose_estimation_pytorch import available_models print(available_models()) ```