From 51a6af4ef24e6625eca5f321d7a803ef0807b577 Mon Sep 17 00:00:00 2001 From: Talmo Pereira Date: Sun, 3 Apr 2022 19:29:45 -0700 Subject: [PATCH] Add more notebooks to docs (#699) * Add more notebooks to docs (#698) * Fix missing links and update content * Add new notebooks --- docs/conf.py | 8 +- docs/guides/gui.rst | 4 + docs/help.md | 4 +- docs/notebooks/Data_structures.ipynb | 954 ++++++++++ .../Interactive_and_realtime_inference.ipynb | 1572 +++++++++++++++++ .../Interactive_and_resumable_training.ipynb | 998 +++++++++++ docs/notebooks/Post_inference_tracking.ipynb | 578 ++++++ docs/notebooks/index.rst | 53 +- docs/tutorials/new-project.md | 81 + docs/tutorials/new-project.rst | 46 - 10 files changed, 4239 insertions(+), 59 deletions(-) create mode 100644 docs/notebooks/Data_structures.ipynb create mode 100644 docs/notebooks/Interactive_and_realtime_inference.ipynb create mode 100644 docs/notebooks/Interactive_and_resumable_training.ipynb create mode 100644 docs/notebooks/Post_inference_tracking.ipynb create mode 100644 docs/tutorials/new-project.md delete mode 100644 docs/tutorials/new-project.rst diff --git a/docs/conf.py b/docs/conf.py index c5e8d390a..2c2869ab8 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,6 +16,7 @@ import sys import shutil import docs.utils +from datetime import date sys.path.insert(0, os.path.abspath("..")) @@ -23,8 +24,8 @@ # -- Project information ----------------------------------------------------- project = "SLEAP" -author = "Talmo D. Pereira" -copyright = "2019–2022, Talmo Lab" +author = "SLEAP Developers" +copyright = f"2019–{date.today().year}, Talmo Lab" # The short X.Y version version = "1.2.2" @@ -162,6 +163,9 @@ def linkcode_resolve(domain, info): "tasklist", ] +# https://myst-parser.readthedocs.io/en/latest/syntax/optional.html#auto-generated-header-anchors +myst_heading_anchors = 3 + html_logo = "_static/logo.png" # Add any paths that contain custom static files (such as style sheets) here, diff --git a/docs/guides/gui.rst b/docs/guides/gui.rst index 7246a00df..bb5b9e8dd 100644 --- a/docs/guides/gui.rst +++ b/docs/guides/gui.rst @@ -64,6 +64,10 @@ View "**Apply Distinct Colors To**" allows you to determine whether distinct colors are used for distinct tracks (instance identities), nodes, or edges. Try it! +"**Show Instances**" toggles the visibility of all instances in the frame. Useful for quickly hiding overlapping predictions. + +"**Show Non-Visible Nodes**" toggles the visibility of "non-visible" nodes. Non-visible here means they are landmarks that were manually marked as occluded or not present. Hiding them is useful when inspecting manual labels with many missing nodes. + "**Show Node Names**" allows you to toggle the visibility of the node names. This is useful if you have lots of nearby instances or very dense skeletons and the node names make it hard to see where the nodes are located. "**Show Edges**" allows you to toggle the visibility of the edges which connect the nodes. This can be useful when you have lots of edges which make it hard to see the features of animals in your video. diff --git a/docs/help.md b/docs/help.md index 34cf40eab..afc880750 100644 --- a/docs/help.md +++ b/docs/help.md @@ -7,7 +7,7 @@ Stuck? Can't get SLEAP to run? Crashing? Try the recommended tips below. ### I can't get SLEAP to install! -Have you tried all of the steps in the {ref}`installation instructions `? +Have you tried all of the steps in the [installation instructions](installation)? If so, please feel free to [open an issue](https://github.com/murthylab/sleap/issues) and tell us how you're trying to install it, what error messages you're getting and which operating system you're on. @@ -17,7 +17,7 @@ Yes! You can install SLEAP as you normally would using the `conda` or `pip`-base ### What if I already have CUDA set up on my system? -You can use the system CUDA installation by simply using the {ref}`pip ` installation method. +You can use the system CUDA installation by simply using the [](./installation.md#pip-package) installation method. Note that you will need to use a version compatible with **TensorFlow 2.6+** (**CUDA Toolkit v11.3** and **cuDNN v8.2**). diff --git a/docs/notebooks/Data_structures.ipynb b/docs/notebooks/Data_structures.ipynb new file mode 100644 index 000000000..67be045a1 --- /dev/null +++ b/docs/notebooks/Data_structures.ipynb @@ -0,0 +1,954 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "SLEAP - Data structures.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Data structures\n", + "\n", + "In this notebook, we will explore some of the major data structures used in SLEAP and how they can be manipulated when generating predictions from trained models.\n", + "\n", + "A quick overview of the data structures before we start:\n", + "\n", + "- `Point`/`PredictedPoint` → Contains the `x` and `y` coordinates (and `score` for predictions) of a landmark.\n", + "- `Instance`/`PredictedInstance` → Contains a set of `Point`/`PredictedPoint`s. This represent a single individual within a frame and may also contain an associated `Track`.\n", + "- `Skeleton` → Defines the nodes and edges that define the set of unique landmark types that each point represents, e.g., \"head\", \"tail\", etc. This *does not contain positions* -- those are stored in individual `Point`s.\n", + "- `LabeledFrame` → Contains a set of `Instance`/`PredictedInstance`s for a single frame.\n", + "- `Labels` → Contains a set of `LabeledFrame`s and the associated metadata for the videos and other information related to the project or predictions." + ], + "metadata": { + "id": "NqgGonrTRLg9" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8BOjXv09U2iK" + }, + "source": [ + "## 1. Setup SLEAP and data\n", + "\n", + "We'll start by installing SLEAP and downloading some data and models to play around with.\n", + "\n", + "If you get a dependency error in subsequent cells, just click **Runtime** → **Restart runtime** to reload the packages." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3GTiapGASisF", + "outputId": "c7ce8c05-a473-4995-8cab-0f20d04a52b1" + }, + "source": [ + "# This should take care of all the dependencies on colab:\n", + "!pip uninstall -y opencv-python opencv-contrib-python && pip install sleap\n", + "\n", + "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n", + "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found existing installation: opencv-python 4.1.2.30\n", + "Uninstalling opencv-python-4.1.2.30:\n", + " 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"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.4.8)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.0.4)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.24.3)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (2.10)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.2.0)\n", + "Building wheels for collected packages: pykalman, jsmin\n", + " Building wheel for pykalman (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for pykalman: filename=pykalman-0.9.5-py3-none-any.whl size=48462 sha256=a06494160ef192a795ebcc248474d9c759e93594f237a46d572d71045302de71\n", + " Stored in directory: /root/.cache/pip/wheels/6a/04/02/2dda6ea59c66d9e685affc8af3a31ad3a5d87b7311689efce6\n", + " Building wheel for jsmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for jsmin: filename=jsmin-3.0.1-py3-none-any.whl size=13782 sha256=11175f12c4cdb3583f65125aa1f875e232ab437f5d9bdf1a6a73fbdb3d9ba69a\n", + " Stored in directory: /root/.cache/pip/wheels/a4/0b/64/fb4f87526ecbdf7921769a39d91dcfe4860e621cf15b8250d6\n", + "Successfully built pykalman jsmin\n", + "Installing collected packages: keras-applications, tf-estimator-nightly, shiboken2, opencv-python, image-classifiers, efficientnet, commonmark, colorama, attrs, segmentation-models, scikit-video, rich, qimage2ndarray, python-rapidjson, PySide2, pykalman, opencv-python-headless, jsonpickle, jsmin, imgstore, imgaug, cattrs, sleap\n", + " Attempting uninstall: attrs\n", + " Found existing installation: attrs 21.4.0\n", + " Uninstalling attrs-21.4.0:\n", + " Successfully uninstalled attrs-21.4.0\n", + " Attempting uninstall: imgaug\n", + " Found existing installation: imgaug 0.2.9\n", + " Uninstalling imgaug-0.2.9:\n", + " Successfully uninstalled imgaug-0.2.9\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", + "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n", + "Successfully installed PySide2-5.14.1 attrs-21.2.0 cattrs-1.1.1 colorama-0.4.4 commonmark-0.9.1 efficientnet-1.0.0 image-classifiers-1.0.0 imgaug-0.4.0 imgstore-0.2.9 jsmin-3.0.1 jsonpickle-1.2 keras-applications-1.0.8 opencv-python-4.5.5.64 opencv-python-headless-4.5.5.62 pykalman-0.9.5 python-rapidjson-1.6 qimage2ndarray-1.8.3 rich-10.16.1 scikit-video-1.1.11 segmentation-models-1.0.1 shiboken2-5.14.1 sleap-1.2.2 tf-estimator-nightly-2.8.0.dev2021122109\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0n8oqLWBU0v7", + "outputId": "f9cdcfe1-d152-4a0a-b769-6f9f7d8c0cf0" + }, + "source": [ + "# Test video:\n", + "!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n", + "\n", + "# Test video labels (from predictions/not necessary for inference benchmarking):\n", + "!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp\n", + "\n", + "# Bottom-up model:\n", + "# !wget https://storage.googleapis.com/sleap-data/reference/flies13/bu.210506_230852.multi_instance.n%3D1800.zip\n", + "\n", + "# Top-down model (two-stage):\n", + "!wget https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n", + "!wget https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2022-04-04 00:19:01-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.97.128, 142.251.107.128, 173.194.214.128, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.97.128|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 85343812 (81M) [video/mp4]\n", + "Saving to: ‘190719_090330_wt_18159206_rig1.2@15000-17560.mp4’\n", + "\n", + "190719_090330_wt_18 100%[===================>] 81.39M 142MB/s in 0.6s \n", + "\n", + "2022-04-04 00:19:02 (142 MB/s) - ‘190719_090330_wt_18159206_rig1.2@15000-17560.mp4’ saved [85343812/85343812]\n", + "\n", + "--2022-04-04 00:19:02-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.214.128, 173.194.215.128, 173.194.216.128, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.214.128|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1581400 (1.5M) [application/octet-stream]\n", + "Saving to: ‘190719_090330_wt_18159206_rig1.2@15000-17560.slp’\n", + "\n", + "190719_090330_wt_18 100%[===================>] 1.51M --.-KB/s in 0.01s \n", + "\n", + "2022-04-04 00:19:02 (151 MB/s) - ‘190719_090330_wt_18159206_rig1.2@15000-17560.slp’ saved [1581400/1581400]\n", + "\n", + "--2022-04-04 00:19:02-- https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.214.128, 173.194.215.128, 173.194.216.128, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.214.128|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6372537 (6.1M) [application/zip]\n", + "Saving to: ‘centroid.fast.210504_182918.centroid.n=1800.zip’\n", + "\n", + "centroid.fast.21050 100%[===================>] 6.08M --.-KB/s in 0.05s \n", + "\n", + "2022-04-04 00:19:02 (134 MB/s) - ‘centroid.fast.210504_182918.centroid.n=1800.zip’ saved [6372537/6372537]\n", + "\n", + "--2022-04-04 00:19:02-- https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.216.128, 173.194.217.128, 173.194.218.128, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.216.128|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 30775963 (29M) [application/zip]\n", + "Saving to: ‘td_fast.210505_012601.centered_instance.n=1800.zip’\n", + "\n", + "td_fast.210505_0126 100%[===================>] 29.35M 190MB/s in 0.2s \n", + "\n", + "2022-04-04 00:19:03 (190 MB/s) - ‘td_fast.210505_012601.centered_instance.n=1800.zip’ saved [30775963/30775963]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "F-zzLnAoWrC5", + "outputId": "b0ae7571-3ac0-42c7-d50f-982e4d9a459f" + }, + "source": [ + "!ls -lah" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "total 119M\n", + "drwxr-xr-x 1 root root 4.0K Apr 4 00:19 .\n", + "drwxr-xr-x 1 root root 4.0K Apr 4 00:15 ..\n", + "-rw-r--r-- 1 root root 82M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.mp4\n", + "-rw-r--r-- 1 root root 1.6M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.slp\n", + "-rw-r--r-- 1 root root 6.1M May 20 2021 'centroid.fast.210504_182918.centroid.n=1800.zip'\n", + "drwxr-xr-x 4 root root 4.0K Mar 23 14:21 .config\n", + "drwxr-xr-x 1 root root 4.0K Mar 23 14:22 sample_data\n", + "-rw-r--r-- 1 root root 30M May 20 2021 'td_fast.210505_012601.centered_instance.n=1800.zip'\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "w6xCj73QXM0t", + "outputId": "47d181ba-9272-4b9d-ab2a-0fcae34f38d1" + }, + "source": [ + "import sleap\n", + "\n", + "# This prevents TensorFlow from allocating all the GPU memory, which leads to issues on\n", + "# some GPUs/platforms:\n", + "sleap.disable_preallocation()\n", + "\n", + "# This would hide GPUs from the TensorFlow altogether:\n", + "# sleap.use_cpu_only()\n", + "\n", + "# Print some info:\n", + "sleap.versions()\n", + "sleap.system_summary()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n", + "SLEAP: 1.2.2\n", + "TensorFlow: 2.8.0\n", + "Numpy: 1.21.5\n", + "Python: 3.7.13\n", + "OS: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n", + "GPUs: 1/1 available\n", + " Device: /physical_device:GPU:0\n", + " Available: True\n", + " Initalized: False\n", + " Memory growth: True\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B-y49i6sWu45" + }, + "source": [ + "## 2. Data structures and inference" + ] + }, + { + "cell_type": "markdown", + "source": [ + "SLEAP can read videos in a variety of different formats through the `sleap.load_video` high level API. Once loaded, the `sleap.Video` object allows you to access individual frames as if the it were a standard numpy array.\n", + "\n", + "**Note:** The actual frames are not loaded until you access them so we don't blow up our memory when using long videos." + ], + "metadata": { + "id": "0Fyey-smRjXx" + } + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cH_qfme2We7k", + "outputId": "cb6aaf9c-ab38-4b3b-ffac-8acd78bf13c1" + }, + "source": [ + "# Videos can be represented agnostic to the backend format\n", + "video = sleap.load_video(\"190719_090330_wt_18159206_rig1.2@15000-17560.mp4\")\n", + "\n", + "# sleap.Video objects have a numpy-like interface:\n", + "print(video.shape)\n", + "\n", + "# And we can load images in the video using array indexing:\n", + "imgs = video[:4]\n", + "print(imgs.shape, imgs.dtype)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(2560, 1024, 1024, 1)\n", + "(4, 1024, 1024, 1) uint8\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oPiNRLWlZKZS" + }, + "source": [ + "The high level interface for loading models (`sleap.load_model()`) takes model folders or zipped folders as input. These are outputs from our training procedure and need to contain a `\"best_model.h5\"` and `\"training_config.json\"`.\n", + " \n", + "`best_model.h5` is an HDF5-serialized tf.keras.Model that was checkpointed during\n", + "training. It includes the architecture as well as the weights, so they're standalone\n", + "and don't need SLEAP -- BUT they do not contain the inference methods.\n", + "\n", + "`training_config.json` is a serialized `sleap.TrainingJobConfig` that contains metadata\n", + "like what channels of the model correspond to what landmarks and etc.\n", + "\n", + "Top-down models have two stages: centroid and centered instance confidence maps, which we train and save out separately, so loading them together links them up into a single inference model." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wnIgeeivXiln" + }, + "source": [ + "# Top-down\n", + "predictor = sleap.load_model([\n", + " \"centroid.fast.210504_182918.centroid.n=1800.zip\",\n", + " \"td_fast.210505_012601.centered_instance.n=1800.zip\"\n", + " ])\n", + "\n", + "# Bottom-up\n", + "# predictor = sleap.load_model(\"bu.210506_230852.multi_instance.n=1800.zip\")" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ymlKl4uuZmk8" + }, + "source": [ + "The high level predictor creates all the SLEAP data structures after doing inference. For example:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51, + "referenced_widgets": [ + "581b3a9402bc4837bde932e98fa475a7", + "81f1dfa2788c4ec883a2135ff27f4626" + ] + }, + "id": "4RWl4PwTZkuN", + "outputId": "82141aed-1fa1-4d44-8bad-d8d78a642cd7" + }, + "source": [ + "labels = predictor.predict(video)\n", + "labels" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Output()" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "581b3a9402bc4837bde932e98fa475a7" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Labels(labeled_frames=2560, videos=1, skeletons=1, tracks=0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3iiyg26z-rZt"
+      },
+      "source": [
+        "Labels contain not just the predicted data, but all the other associated data structures and metadata:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EgL-bqRj-l6R",
+        "outputId": "3fd8f355-92b1-4bbb-b7e9-d564b007d97b"
+      },
+      "source": [
+        "labels.videos"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Video(backend=MediaVideo(filename='190719_090330_wt_18159206_rig1.2@15000-17560.mp4', grayscale=True, bgr=True, dataset='', input_format='channels_last'))]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EOu9c9ly-nkN",
+        "outputId": "3e66210c-12f6-48e4-c829-41aa3768b140"
+      },
+      "source": [
+        "labels.skeletons"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Skeleton(name='Skeleton-0', nodes=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], edges=[('thorax', 'head'), ('thorax', 'abdomen'), ('thorax', 'wingL'), ('thorax', 'wingR'), ('thorax', 'forelegL4'), ('thorax', 'forelegR4'), ('thorax', 'midlegL4'), ('thorax', 'midlegR4'), ('thorax', 'hindlegL4'), ('thorax', 'hindlegR4'), ('head', 'eyeL'), ('head', 'eyeR')], symmetries=[('wingL', 'wingR'), ('forelegL4', 'forelegR4'), ('hindlegL4', 'hindlegR4'), ('eyeL', 'eyeR'), ('midlegL4', 'midlegR4')])]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8bVug0aH8sOA"
+      },
+      "source": [
+        "Individual labeled frames are accessible through a list-like interface:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pGcyrjKf8hp4",
+        "outputId": "1ff0ab5a-5a67-4d35-c09f-21adbcec655e"
+      },
+      "source": [
+        "labeled_frame = labels[0]  # shortcut for labels.labeled_frames[0]\n",
+        "labeled_frame"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LabeledFrame(video=MediaVideo('190719_090330_wt_18159206_rig1.2@15000-17560.mp4'), frame_idx=0, instances=2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5DrFjiOk9Vbt"
+      },
+      "source": [
+        "Convenient methods allow for easy inspection:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 543
+        },
+        "id": "s2YiRWSa7f6D",
+        "outputId": "3f76ae98-dd72-4c2e-ac06-9bfe3b2c2637"
+      },
+      "source": [
+        "labels[0].plot(scale=0.5)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gU8q0OCb9b9m"
+      },
+      "source": [
+        "The labeled frame is itself a container for instances:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZP9Z0etc9e0c",
+        "outputId": "00986c80-23d0-43fa-f4f9-c60482e5293e"
+      },
+      "source": [
+        "labeled_frame.instances"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (234.2, 430.5, 0.98), thorax: (271.6, 436.1, 0.94), abdomen: (308.0, 438.6, 0.59), wingL: (321.8, 440.1, 0.39), wingR: (322.0, 436.8, 0.49), forelegL4: (246.1, 450.6, 0.92), forelegR4: (242.3, 413.9, 0.78), midlegL4: (285.8, 459.9, 0.47), midlegR4: (272.3, 406.7, 0.77), hindlegR4: (317.6, 430.6, 0.30), eyeL: (242.1, 441.9, 0.89), eyeR: (245.3, 420.9, 0.92)], score=0.95, track=None, tracking_score=0.00),\n",
+              " PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (319.4, 435.9, 0.83), thorax: (354.4, 435.2, 0.80), abdomen: (368.3, 433.8, 0.71), wingL: (393.9, 480.3, 0.83), wingR: (398.4, 430.0, 0.81), forelegL4: (307.8, 445.7, 0.26), forelegR4: (305.6, 421.4, 0.69), midlegL4: (325.7, 475.0, 0.94), midlegR4: (331.8, 385.1, 0.88), hindlegL4: (363.7, 474.1, 0.88), hindlegR4: (376.0, 398.4, 0.52), eyeL: (329.3, 445.6, 0.90), eyeR: (327.9, 425.1, 0.84)], score=0.84, track=None, tracking_score=0.00)]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Y-stVhiw9uIr",
+        "outputId": "4cd7dbdf-bd91-4037-b971-3a17c85193bd"
+      },
+      "source": [
+        "instance = labeled_frame[0]  # shortcut for labeled_frame.instances[0]\n",
+        "instance"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (234.2, 430.5, 0.98), thorax: (271.6, 436.1, 0.94), abdomen: (308.0, 438.6, 0.59), wingL: (321.8, 440.1, 0.39), wingR: (322.0, 436.8, 0.49), forelegL4: (246.1, 450.6, 0.92), forelegR4: (242.3, 413.9, 0.78), midlegL4: (285.8, 459.9, 0.47), midlegR4: (272.3, 406.7, 0.77), hindlegR4: (317.6, 430.6, 0.30), eyeL: (242.1, 441.9, 0.89), eyeR: (245.3, 420.9, 0.92)], score=0.95, track=None, tracking_score=0.00)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MMOUcx6Z94TJ"
+      },
+      "source": [
+        "Finally, instances are containers for points:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7xK-uGJZ905J",
+        "outputId": "102accd0-ba45-44b0-b839-eff15a06245a"
+      },
+      "source": [
+        "instance.points"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(PredictedPoint(x=234.244384765625, y=430.52001953125, visible=True, complete=False, score=0.9790461659431458),\n",
+              " PredictedPoint(x=271.5894470214844, y=436.1461181640625, visible=True, complete=False, score=0.9357967376708984),\n",
+              " PredictedPoint(x=308.02899169921875, y=438.5711975097656, visible=True, complete=False, score=0.5859644412994385),\n",
+              " PredictedPoint(x=321.8167419433594, y=440.0872802734375, visible=True, complete=False, score=0.3912011682987213),\n",
+              " PredictedPoint(x=322.0196533203125, y=436.77008056640625, visible=True, complete=False, score=0.48613619804382324),\n",
+              " PredictedPoint(x=246.1430206298828, y=450.56182861328125, visible=True, complete=False, score=0.9176540970802307),\n",
+              " PredictedPoint(x=242.2632293701172, y=413.94976806640625, visible=True, complete=False, score=0.7807964086532593),\n",
+              " PredictedPoint(x=285.78167724609375, y=459.9156494140625, visible=True, complete=False, score=0.4739593267440796),\n",
+              " PredictedPoint(x=272.27996826171875, y=406.71759033203125, visible=True, complete=False, score=0.7721188068389893),\n",
+              " PredictedPoint(x=317.5997619628906, y=430.6052551269531, visible=True, complete=False, score=0.2960105538368225),\n",
+              " PredictedPoint(x=242.1038055419922, y=441.94561767578125, visible=True, complete=False, score=0.8855815529823303),\n",
+              " PredictedPoint(x=245.3200225830078, y=420.93609619140625, visible=True, complete=False, score=0.9199579954147339))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e2uGWf-R-OAc"
+      },
+      "source": [
+        "These can be converted into concrete arrays:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jEWddPpg93GM",
+        "outputId": "ddd09bae-83e1-48f7-b870-3155a68e6ecb"
+      },
+      "source": [
+        "pts = instance.numpy()\n",
+        "print(pts)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[234.24438477 430.52001953]\n",
+            " [271.58944702 436.14611816]\n",
+            " [308.0289917  438.57119751]\n",
+            " [321.81674194 440.08728027]\n",
+            " [322.01965332 436.77008057]\n",
+            " [246.14302063 450.56182861]\n",
+            " [242.26322937 413.94976807]\n",
+            " [285.78167725 459.91564941]\n",
+            " [272.27996826 406.71759033]\n",
+            " [         nan          nan]\n",
+            " [317.59976196 430.60525513]\n",
+            " [242.10380554 441.94561768]\n",
+            " [245.32002258 420.93609619]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "id5JzQL9_KIS"
+      },
+      "source": [
+        "Images can be embedded together with the predictions in the same format:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Thx9INKJ_JHk"
+      },
+      "source": [
+        "labels = sleap.Labels(labels.labeled_frames[:4])  # crop to the first few labels for this example\n",
+        "labels.save(\"labels_with_images.pkg.slp\", with_images=True, embed_all_labeled=True)"
+      ],
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mcv3CtqA_uXf"
+      },
+      "source": [
+        "Let's delete the source data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fJvcyJDw_Wbz"
+      },
+      "source": [
+        "!rm \"190719_090330_wt_18159206_rig1.2@15000-17560.mp4\""
+      ],
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-nqzHbDT_yXX"
+      },
+      "source": [
+        "And check out what happens when we load in some labels with embedded images:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "enTHiSIY_qg0",
+        "outputId": "96589190-e771-4fd8-bc41-7cd7bf7262d9"
+      },
+      "source": [
+        "labels = sleap.load_file(\"labels_with_images.pkg.slp\")\n",
+        "labels"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Labels(labeled_frames=4, videos=1, skeletons=1, tracks=0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 543
+        },
+        "id": "X8zy1PyP_2cW",
+        "outputId": "757240fe-eb6f-465f-b079-170ef889144d"
+      },
+      "source": [
+        "labels[0].plot(scale=0.5)"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/docs/notebooks/Interactive_and_realtime_inference.ipynb b/docs/notebooks/Interactive_and_realtime_inference.ipynb
new file mode 100644
index 000000000..f95676627
--- /dev/null
+++ b/docs/notebooks/Interactive_and_realtime_inference.ipynb
@@ -0,0 +1,1572 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "SLEAP - Interactive and realtime inference.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "\"Open"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Interactive and realtime inference\n",
+        "\n",
+        "For most workflows, using the [`sleap-track` CLI](https://sleap.ai/guides/cli.html#sleap-track) is probably the most convenient option, but if you're developing a custom application you can take advantage of SLEAP's inference API to use your trained models in your own custom scripts.\n",
+        "\n",
+        "In this notebook we will explore how to predict poses from raw images in pure Python, and do some basic benchmarking on a simulated realtime predictor that could be used to enable closed-loop experiments."
+      ],
+      "metadata": {
+        "id": "DpvQa3M3n7jC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BeeqrLbdupmE"
+      },
+      "source": [
+        "## 1. Setup SLEAP\n",
+        "\n",
+        "Run this cell first to install SLEAP. If you get a dependency error in subsequent cells, just click **Runtime** → **Restart runtime** to reload the packages.\n",
+        "\n",
+        "Don't forget to set **Runtime** → **Change runtime type** → **GPU** as the accelerator."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BYxJ2rJOMW8B",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6ef53f4c-5074-4f41-8523-3d989a0f2844"
+      },
+      "source": [
+        "# This should take care of all the dependencies on colab:\n",
+        "!pip uninstall -y opencv-python opencv-contrib-python && pip install sleap\n",
+        "\n",
+        "\n",
+        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
+        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found existing installation: opencv-python 4.1.2.30\n",
+            "Uninstalling opencv-python-4.1.2.30:\n",
+            "  Successfully uninstalled opencv-python-4.1.2.30\n",
+            "Found existing installation: opencv-contrib-python 4.1.2.30\n",
+            "Uninstalling opencv-contrib-python-4.1.2.30:\n",
+            "  Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
+            "Collecting sleap\n",
+            "  Downloading sleap-1.2.2-py3-none-any.whl (62.0 MB)\n",
+            "\u001b[K     |████████████████████████████████| 62.0 MB 17 kB/s \n",
+            "\u001b[?25hRequirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from sleap) (2.6.3)\n",
+            "Collecting rich==10.16.1\n",
+            "  Downloading rich-10.16.1-py3-none-any.whl (214 kB)\n",
+            "\u001b[K     |████████████████████████████████| 214 kB 51.1 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from sleap) (5.4.8)\n",
+            "Collecting segmentation-models==1.0.1\n",
+            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
+            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from sleap) (0.11.2)\n",
+            "Collecting jsmin\n",
+            "  Downloading jsmin-3.0.1.tar.gz (13 kB)\n",
+            "Collecting attrs==21.2.0\n",
+            "  Downloading attrs-21.2.0-py2.py3-none-any.whl (53 kB)\n",
+            "\u001b[K     |████████████████████████████████| 53 kB 1.9 MB/s \n",
+            "\u001b[?25hCollecting opencv-python-headless<=4.5.5.62,>=4.2.0.34\n",
+            "  Downloading opencv_python_headless-4.5.5.62-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (47.7 MB)\n",
+            "\u001b[K     |████████████████████████████████| 47.7 MB 92 kB/s \n",
+            "\u001b[?25hCollecting pykalman==0.9.5\n",
+            "  Downloading pykalman-0.9.5.tar.gz (228 kB)\n",
+            "\u001b[K     |████████████████████████████████| 228 kB 67.2 MB/s \n",
+            "\u001b[?25hCollecting cattrs==1.1.1\n",
+            "  Downloading cattrs-1.1.1-py3-none-any.whl (16 kB)\n",
+            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from sleap) (0.18.3)\n",
+            "Requirement already satisfied: numpy<=1.21.5,>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from sleap) (1.21.5)\n",
+            "Requirement already satisfied: scipy<=1.7.3,>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from sleap) (1.4.1)\n",
+            "Collecting jsonpickle==1.2\n",
+            "  Downloading jsonpickle-1.2-py2.py3-none-any.whl (32 kB)\n",
+            "Requirement already satisfied: pyzmq in /usr/local/lib/python3.7/dist-packages (from sleap) (22.3.0)\n",
+            "Collecting scikit-video\n",
+            "  Downloading scikit_video-1.1.11-py2.py3-none-any.whl (2.3 MB)\n",
+            "\u001b[K     |████████████████████████████████| 2.3 MB 54.6 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from sleap) (3.13)\n",
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+            "Requirement already satisfied: h5py<=3.6.0,>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from sleap) (3.1.0)\n",
+            "Collecting PySide2<=5.14.1,>=5.13.2\n",
+            "  Downloading PySide2-5.14.1-5.14.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (165.5 MB)\n",
+            "\u001b[K     |████████████████████████████████| 165.5 MB 64 kB/s \n",
+            "\u001b[?25hRequirement already satisfied: imageio<=2.15.0 in /usr/local/lib/python3.7/dist-packages (from sleap) (2.4.1)\n",
+            "Collecting python-rapidjson\n",
+            "  Downloading python_rapidjson-1.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
+            "\u001b[K     |████████████████████████████████| 1.6 MB 42.0 MB/s \n",
+            "\u001b[?25hCollecting qimage2ndarray<=1.8.3,>=1.8.2\n",
+            "  Downloading qimage2ndarray-1.8.3-py3-none-any.whl (11 kB)\n",
+            "Requirement already satisfied: scikit-learn==1.0.* in /usr/local/lib/python3.7/dist-packages (from sleap) (1.0.2)\n",
+            "Collecting imgstore==0.2.9\n",
+            "  Downloading imgstore-0.2.9-py2.py3-none-any.whl (904 kB)\n",
+            "\u001b[K     |████████████████████████████████| 904 kB 70.2 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from sleap) (1.3.5)\n",
+            "Collecting imgaug==0.4.0\n",
+            "  Downloading imgaug-0.4.0-py2.py3-none-any.whl (948 kB)\n",
+            "\u001b[K     |████████████████████████████████| 948 kB 72.4 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug==0.4.0->sleap) (3.2.2)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug==0.4.0->sleap) (1.15.0)\n",
+            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug==0.4.0->sleap) (7.1.2)\n",
+            "Collecting opencv-python\n",
+            "  Downloading opencv_python-4.5.5.64-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (60.5 MB)\n",
+            "\u001b[K     |████████████████████████████████| 60.5 MB 1.3 MB/s \n",
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+            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from imgstore==0.2.9->sleap) (2018.9)\n",
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+            "Requirement already satisfied: typing-extensions<5.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from rich==10.16.1->sleap) (3.10.0.2)\n",
+            "\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))': /simple/colorama/\u001b[0m\n",
+            "Collecting colorama<0.5.0,>=0.4.0\n",
+            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich==10.16.1->sleap) (2.6.1)\n",
+            "Collecting commonmark<0.10.0,>=0.9.0\n",
+            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
+            "\u001b[K     |████████████████████████████████| 51 kB 8.9 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==1.0.*->sleap) (1.1.0)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==1.0.*->sleap) (3.1.0)\n",
+            "Collecting keras-applications<=1.0.8,>=1.0.7\n",
+            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
+            "\u001b[K     |████████████████████████████████| 50 kB 8.7 MB/s \n",
+            "\u001b[?25hCollecting image-classifiers==1.0.0\n",
+            "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
+            "Collecting efficientnet==1.0.0\n",
+            "  Downloading efficientnet-1.0.0-py3-none-any.whl (17 kB)\n",
+            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py<=3.6.0,>=3.1.0->sleap) (1.5.2)\n",
+            "Collecting shiboken2==5.14.1\n",
+            "  Downloading shiboken2-5.14.1-5.14.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (847 kB)\n",
+            "\u001b[K     |████████████████████████████████| 847 kB 56.7 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->sleap) (1.3.0)\n",
+            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image->sleap) (2021.11.2)\n",
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+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug==0.4.0->sleap) (1.4.0)\n",
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+            "Collecting tf-estimator-nightly==2.8.0.dev2021122109\n",
+            "  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
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+            "Building wheels for collected packages: pykalman, jsmin\n",
+            "  Building wheel for pykalman (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for pykalman: filename=pykalman-0.9.5-py3-none-any.whl size=48462 sha256=b43fd016511642d3238f564a820ccced9855d44660a169c46474533d3cf57390\n",
+            "  Stored in directory: /root/.cache/pip/wheels/6a/04/02/2dda6ea59c66d9e685affc8af3a31ad3a5d87b7311689efce6\n",
+            "  Building wheel for jsmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for jsmin: filename=jsmin-3.0.1-py3-none-any.whl size=13782 sha256=fd47efc594f3416388e6e074d4602a5b5559ce66e69e621778a182409f5a004c\n",
+            "  Stored in directory: /root/.cache/pip/wheels/a4/0b/64/fb4f87526ecbdf7921769a39d91dcfe4860e621cf15b8250d6\n",
+            "Successfully built pykalman jsmin\n",
+            "Installing collected packages: keras-applications, tf-estimator-nightly, shiboken2, opencv-python, image-classifiers, efficientnet, commonmark, colorama, attrs, segmentation-models, scikit-video, rich, qimage2ndarray, python-rapidjson, PySide2, pykalman, opencv-python-headless, jsonpickle, jsmin, imgstore, imgaug, cattrs, sleap\n",
+            "  Attempting uninstall: attrs\n",
+            "    Found existing installation: attrs 21.4.0\n",
+            "    Uninstalling attrs-21.4.0:\n",
+            "      Successfully uninstalled attrs-21.4.0\n",
+            "  Attempting uninstall: imgaug\n",
+            "    Found existing installation: imgaug 0.2.9\n",
+            "    Uninstalling imgaug-0.2.9:\n",
+            "      Successfully uninstalled imgaug-0.2.9\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
+            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
+            "Successfully installed PySide2-5.14.1 attrs-21.2.0 cattrs-1.1.1 colorama-0.4.4 commonmark-0.9.1 efficientnet-1.0.0 image-classifiers-1.0.0 imgaug-0.4.0 imgstore-0.2.9 jsmin-3.0.1 jsonpickle-1.2 keras-applications-1.0.8 opencv-python-4.5.5.64 opencv-python-headless-4.5.5.62 pykalman-0.9.5 python-rapidjson-1.6 qimage2ndarray-1.8.3 rich-10.16.1 scikit-video-1.1.11 segmentation-models-1.0.1 shiboken2-5.14.1 sleap-1.2.2 tf-estimator-nightly-2.8.0.dev2021122109\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Import SLEAP to make sure it installed correctly and print out some information about the system:"
+      ],
+      "metadata": {
+        "id": "qjfoeOZvpV8o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jftAOyvvuQeh",
+        "outputId": "5c415dbc-7ecf-46db-8271-c17cc89552a4"
+      },
+      "source": [
+        "import sleap\n",
+        "sleap.disable_preallocation()  # This initializes the GPU and prevents TensorFlow from filling the entire GPU memory\n",
+        "sleap.versions()\n",
+        "sleap.system_summary()"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
+            "SLEAP: 1.2.2\n",
+            "TensorFlow: 2.8.0\n",
+            "Numpy: 1.21.5\n",
+            "Python: 3.7.13\n",
+            "OS: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n",
+            "GPUs: 1/1 available\n",
+            "  Device: /physical_device:GPU:0\n",
+            "         Available: True\n",
+            "        Initalized: False\n",
+            "     Memory growth: True\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wSdTJYOdu4L6"
+      },
+      "source": [
+        "## 2. Setup data\n",
+        "\n",
+        "Before we start, let's download a raw video and a set of trained top-down ID models that we'll use to build our application around."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sDIF3RKdM86u",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5d435b70-d296-4e19-b1b1-0cd9d509e9f3"
+      },
+      "source": [
+        "!curl -L --output video.mp4 https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\n",
+        "!curl -L --output centroid_model.zip https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\n",
+        "!curl -L --output centered_instance_id_model.zip https://storage.googleapis.com/sleap-data/reference/flies13/td_id.fast.v2.210519_111253.multi_class_topdown.n%3D1800.zip\n",
+        "!ls -lah"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100 81.3M  100 81.3M    0     0   119M      0 --:--:-- --:--:-- --:--:--  119M\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100 6223k  100 6223k    0     0  23.2M      0 --:--:-- --:--:-- --:--:-- 23.2M\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100 32.2M  100 32.2M    0     0  62.4M      0 --:--:-- --:--:-- --:--:-- 62.4M\n",
+            "total 120M\n",
+            "drwxr-xr-x 1 root root 4.0K Apr  3 23:33 .\n",
+            "drwxr-xr-x 1 root root 4.0K Apr  3 23:31 ..\n",
+            "-rw-r--r-- 1 root root  33M Apr  3 23:33 centered_instance_id_model.zip\n",
+            "-rw-r--r-- 1 root root 6.1M Apr  3 23:33 centroid_model.zip\n",
+            "drwxr-xr-x 4 root root 4.0K Mar 23 14:21 .config\n",
+            "drwxr-xr-x 1 root root 4.0K Mar 23 14:22 sample_data\n",
+            "-rw-r--r-- 1 root root  82M Apr  3 23:33 video.mp4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Note:** These zip files just have the contents of standard SLEAP model folders that are generated during training."
+      ],
+      "metadata": {
+        "id": "0edP4yp7PMJy"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-vYsPusvviiu"
+      },
+      "source": [
+        "## 3. Interactive inference\n",
+        "\n",
+        "SLEAP provides a high-level API for performing inference in the form of `Predictor` classes specific to each approach/model type.\n",
+        "\n",
+        "To create one from a set of trained models, we can use the high-level `sleap.load_model()` function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictor = sleap.load_model([\"centroid_model.zip\", \"centered_instance_id_model.zip\"], batch_size=16)"
+      ],
+      "metadata": {
+        "id": "cC7IKtPDOktW"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This function handles all the logic of loading trained models, reading the configurations used to train them, and constructs inference models that also include non-trainable operations like peak finding and instance grouping.\n",
+        "\n",
+        "Next, we'll load a video that we want to use for inference. SLEAP `Video` objects don't actually load the whole video into memory, they just provide a common numpy-like interface for reading from different file formats:"
+      ],
+      "metadata": {
+        "id": "w7xGANT7PfmL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "video = sleap.load_video(\"video.mp4\")\n",
+        "video.shape, video.dtype"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CJ9-vuddPelx",
+        "outputId": "9f09d46d-6808-471e-9aed-92a408b97b06"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((2560, 1024, 1024, 1), dtype('uint8'))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Our predictor is pretty flexible. It can handle a variety of different input formats, all of which will return a `Labels` object that contains all of our predictions:"
+      ],
+      "metadata": {
+        "id": "O3xA6cuTQ6sG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load frames to a numpy array.\n",
+        "imgs = video[:100]\n",
+        "print(f\"imgs.shape: {imgs.shape}\")\n",
+        "\n",
+        "# Predict on numpy array.\n",
+        "predictions = predictor.predict(imgs)\n",
+        "predictions"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68,
+          "referenced_widgets": [
+            "d6ca46c1a214448098ad47270939d0c2",
+            "64f2d6a13449451190f6a01f3312235b"
+          ]
+        },
+        "id": "IdhwFe1dRG2K",
+        "outputId": "f5b7d30c-4fad-48b6-9652-c83933c9adf8"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d6ca46c1a214448098ad47270939d0c2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "imgs.shape: (100, 1024, 1024, 1)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Labels(labeled_frames=100, videos=1, skeletons=1, tracks=2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predict on the entire video with parallelizable loading/preprocessing:\n",
+        "predictions = predictor.predict(video)\n",
+        "predictions"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51,
+          "referenced_widgets": [
+            "0e9d4c257a4d4c45b02337a0e038e45e",
+            "fb2df858b0a444edb4b0f429743abd9f"
+          ]
+        },
+        "id": "McsFHqx0Q6F0",
+        "outputId": "a648dac3-6e78-4fbd-e4b1-91389ead143d"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0e9d4c257a4d4c45b02337a0e038e45e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Labels(labeled_frames=2560, videos=1, skeletons=1, tracks=2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can then inspect the results of our predictor:"
+      ],
+      "metadata": {
+        "id": "E8Qm3Y8ERrFb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualize a frame.\n",
+        "predictions[100].plot(scale=0.25)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 287
+        },
+        "id": "MhPh8uwaRFfT",
+        "outputId": "29e5ae1f-bf9d-44ea-a2fe-573b51faaf67"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Inspect the contents of a single frame.\n",
+        "labeled_frame = predictions[100]\n",
+        "labeled_frame.instances"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xyz5qfrFR3Cd",
+        "outputId": "203d483f-6e1b-4e1e-ff89-0dc62488edad"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[PredictedInstance(video=Video(filename=video.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=100, points=[head: (212.5, 427.0, 0.94), thorax: (252.0, 433.1, 0.95), abdomen: (288.6, 439.3, 0.68), wingL: (304.5, 443.3, 0.88), wingR: (306.2, 435.8, 0.68), forelegL4: (216.2, 445.5, 0.88), forelegR4: (216.1, 410.0, 0.90), midlegL4: (244.4, 471.3, 0.90), midlegR4: (256.6, 408.9, 0.86), hindlegL4: (275.0, 459.2, 0.89), hindlegR4: (292.3, 412.0, 0.81), eyeL: (220.0, 438.0, 0.84), eyeR: (223.8, 417.5, 0.91)], score=0.99, track=Track(spawned_on=0, name='female'), tracking_score=0.00),\n",
+              " PredictedInstance(video=Video(filename=video.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=100, points=[head: (313.7, 432.6, 0.87), thorax: (348.9, 427.9, 1.00), abdomen: (378.9, 425.8, 0.83), wingL: (397.0, 428.7, 0.89), wingR: (394.9, 420.7, 0.74), forelegL4: (307.4, 446.4, 0.88), forelegR4: (306.5, 422.5, 0.89), midlegL4: (341.6, 474.2, 0.97), midlegR4: (332.6, 386.3, 0.97), hindlegL4: (378.9, 458.8, 0.92), hindlegR4: (387.7, 394.8, 0.88), eyeL: (323.7, 442.1, 0.96), eyeR: (320.7, 420.8, 0.88)], score=0.99, track=Track(spawned_on=0, name='male'), tracking_score=0.00)]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Convert an instance to a numpy array:\n",
+        "labeled_frame[0].numpy()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FDMcaIwtR7he",
+        "outputId": "df3ead74-4505-4680-de86-2dbd531145e1"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "rec.array([[212.51400757, 426.97024536],\n",
+              "           [251.97747803, 433.08648682],\n",
+              "           [288.64355469, 439.3086853 ],\n",
+              "           [304.53396606, 443.33477783],\n",
+              "           [306.20336914, 435.77227783],\n",
+              "           [216.24688721, 445.4755249 ],\n",
+              "           [216.14550781, 409.98342896],\n",
+              "           [244.39497375, 471.31561279],\n",
+              "           [256.61740112, 408.89056396],\n",
+              "           [274.97470093, 459.1831665 ],\n",
+              "           [292.2600708 , 411.95904541],\n",
+              "           [219.98565674, 437.97906494],\n",
+              "           [223.75566101, 417.5496521 ]],\n",
+              "          dtype=float64)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "What if we don't want or need the inference results wrapped in the SLEAP structures?\n",
+        "\n",
+        "By using the low-level inference model, we can actually go directly from image to numpy arrays of our results:"
+      ],
+      "metadata": {
+        "id": "c6kRMZDYSKIp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "imgs = video[:16]  # batch of 16 images\n",
+        "\n",
+        "predictions = predictor.inference_model.predict(imgs, numpy=True)\n",
+        "predictions"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pWo_bG1HSJaJ",
+        "outputId": "d22e30e9-13ae-466b-d94c-ce787c96a818"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
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+              " \n",
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+              " \n",
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+              " \n",
+              " \n",
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+              " \n",
+              " \n",
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+              " \n",
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+              "          [330.72357, 419.30713]]],\n",
+              " \n",
+              " \n",
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+              " \n",
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+              "          [331.94138, 439.76627],\n",
+              "          [330.20728, 418.03998]]],\n",
+              " \n",
+              " \n",
+              "        [[[232.59995, 427.9426 ],\n",
+              "          [271.68756, 435.92496],\n",
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+              "          [322.3493 , 441.9495 ],\n",
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+              "          [244.36139, 419.95685]],\n",
+              " \n",
+              "         [[322.50397, 428.86414],\n",
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+              "          [332.3299 , 438.7801 ],\n",
+              "          [330.43085, 417.03174]]],\n",
+              " \n",
+              " \n",
+              "        [[[232.25208, 427.7414 ],\n",
+              "          [271.57523, 436.99503],\n",
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+              " \n",
+              "         [[324.36966, 429.4342 ],\n",
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+              "          [334.3139 , 438.40005],\n",
+              "          [331.89133, 417.64728]]],\n",
+              " \n",
+              " \n",
+              "        [[[232.70679, 428.36255],\n",
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+              "          [244.31602, 420.40244]],\n",
+              " \n",
+              "         [[325.2043 , 429.92737],\n",
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+              "          [332.62274, 417.9344 ]]],\n",
+              " \n",
+              " \n",
+              "        [[[232.79385, 428.12885],\n",
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+              "          [310.3099 , 437.81348],\n",
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+              " \n",
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+              "          [388.92133, 394.11078],\n",
+              "          [336.72043, 439.8229 ],\n",
+              "          [332.6642 , 419.31372]]],\n",
+              " \n",
+              " \n",
+              "        [[[232.83435, 428.2637 ],\n",
+              "          [272.11572, 435.61078],\n",
+              "          [312.17938, 439.66312],\n",
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+              "          [242.64131, 413.59937],\n",
+              "          [285.06653, 460.35504],\n",
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+              "          [      nan,       nan],\n",
+              "          [322.4148 , 422.6127 ],\n",
+              "          [240.42722, 440.2208 ],\n",
+              "          [244.4097 , 419.95215]],\n",
+              " \n",
+              "         [[327.3499 , 431.52005],\n",
+              "          [361.313  , 425.36264],\n",
+              "          [389.47607, 423.60114],\n",
+              "          [411.6601 , 435.50894],\n",
+              "          [409.51843, 419.6943 ],\n",
+              "          [319.90283, 445.82428],\n",
+              "          [313.70898, 423.5036 ],\n",
+              "          [345.66882, 473.1757 ],\n",
+              "          [331.79486, 385.46274],\n",
+              "          [399.46533, 457.10553],\n",
+              "          [388.24854, 394.009  ],\n",
+              "          [337.8076 , 440.06436],\n",
+              "          [333.29004, 419.49707]]],\n",
+              " \n",
+              " \n",
+              "        [[[232.41422, 429.4673 ],\n",
+              "          [271.8141 , 435.7682 ],\n",
+              "          [310.01324, 439.98956],\n",
+              "          [322.19714, 443.71683],\n",
+              "          [324.71207, 434.39133],\n",
+              "          [224.85786, 451.4593 ],\n",
+              "          [242.5914 , 413.65204],\n",
+              "          [285.67142, 461.77646],\n",
+              "          [273.7307 , 406.5118 ],\n",
+              "          [      nan,       nan],\n",
+              "          [322.71594, 420.21155],\n",
+              "          [239.99216, 440.57278],\n",
+              "          [243.82819, 420.339  ]],\n",
+              " \n",
+              "         [[328.47983, 431.74188],\n",
+              "          [363.9317 , 425.2397 ],\n",
+              "          [390.49423, 423.05255],\n",
+              "          [413.68115, 433.6671 ],\n",
+              "          [410.5454 , 419.09042],\n",
+              "          [320.30078, 446.80396],\n",
+              "          [313.82977, 421.7456 ],\n",
+              "          [356.64886, 473.89554],\n",
+              "          [331.84995, 385.1559 ],\n",
+              "          [399.78146, 457.11206],\n",
+              "          [388.5744 , 393.94125],\n",
+              "          [339.9305 , 440.99496],\n",
+              "          [334.5468 , 419.42017]]],\n",
+              " \n",
+              " \n",
+              "        [[[232.05379, 430.01157],\n",
+              "          [271.71146, 436.17175],\n",
+              "          [310.08688, 438.66077],\n",
+              "          [322.65015, 442.097  ],\n",
+              "          [324.3269 , 434.45065],\n",
+              "          [224.67744, 450.92798],\n",
+              "          [242.56874, 413.94662],\n",
+              "          [285.72803, 462.40347],\n",
+              "          [273.67886, 406.66385],\n",
+              "          [313.6862 , 456.8137 ],\n",
+              "          [318.559  , 416.42374],\n",
+              "          [239.62582, 441.11035],\n",
+              "          [242.73026, 420.8417 ]],\n",
+              " \n",
+              "         [[329.30188, 431.77295],\n",
+              "          [364.57666, 425.20844],\n",
+              "          [391.32507, 421.96838],\n",
+              "          [414.35016, 433.2262 ],\n",
+              "          [411.04324, 418.17578],\n",
+              "          [320.63538, 445.82654],\n",
+              "          [315.36795, 420.21204],\n",
+              "          [360.9988 , 471.7216 ],\n",
+              "          [332.20065, 385.24988],\n",
+              "          [399.20847, 456.9794 ],\n",
+              "          [388.68896, 394.04962],\n",
+              "          [340.75934, 441.0198 ],\n",
+              "          [335.4428 , 419.33124]]]], dtype=float32),\n",
+              " 'instance_scores': array([[0.9953146 , 0.99476504],\n",
+              "        [0.9959341 , 0.99526805],\n",
+              "        [0.9959078 , 0.99451363],\n",
+              "        [0.99573493, 0.993386  ],\n",
+              "        [0.99603134, 0.99172956],\n",
+              "        [0.99564207, 0.9916197 ],\n",
+              "        [0.9947187 , 0.9915406 ],\n",
+              "        [0.9940315 , 0.98916876],\n",
+              "        [0.99394447, 0.98962784],\n",
+              "        [0.99446183, 0.9910501 ],\n",
+              "        [0.99155337, 0.9933716 ],\n",
+              "        [0.9916019 , 0.9933977 ],\n",
+              "        [0.9932473 , 0.9932013 ],\n",
+              "        [0.99207497, 0.9946308 ],\n",
+              "        [0.991653  , 0.99465877],\n",
+              "        [0.99162734, 0.99486005]], dtype=float32),\n",
+              " 'n_valid': array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=int32)}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for key, value in predictions.items():\n",
+        "    print(f\"'{key}': {value.shape} ({value.dtype})\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k4ms3mUAX_ww",
+        "outputId": "4ea4fc9f-bdbc-4c2d-da9e-68cfc734f22c"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "'instance_peaks': (16, 2, 13, 2) (float32)\n",
+            "'instance_peak_vals': (16, 2, 13) (float32)\n",
+            "'instance_scores': (16, 2) (float32)\n",
+            "'centroids': (16, 2, 2) (float32)\n",
+            "'centroid_vals': (16, 2) (float32)\n",
+            "'n_valid': (16,) (int32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Realtime performance\n",
+        "\n",
+        "Now that we know how to do inference with different types of outputs, let's try to use that to build a simulated \"realtime\" application with timing.\n",
+        "\n",
+        "First, we'll create a class that simulates a camera grabber API that provides a sequence of pre-loaded frames."
+      ],
+      "metadata": {
+        "id": "sDKsqAEVOogD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from time import perf_counter\n",
+        "import numpy as np\n",
+        "\n",
+        "\n",
+        "class SimulatedCamera:\n",
+        "    \"\"\"Simulated camera class that serves frames from memory continuously.\n",
+        "\n",
+        "    Attributes:\n",
+        "        frames: Numpy array with pre-loaded frames.\n",
+        "        frame_counter: Count of frames that have been grabbed.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    frames: np.ndarray\n",
+        "    frame_counter: int\n",
+        "\n",
+        "    def __init__(self, frames):\n",
+        "        self.frames = frames\n",
+        "        self.frame_counter = 0\n",
+        "    \n",
+        "    def grab_frame(self):\n",
+        "        idx = self.frame_counter % len(self.frames)\n",
+        "        self.frame_counter += 1\n",
+        "        return self.frames[idx]\n"
+      ],
+      "metadata": {
+        "id": "_vKMoT_oYcgZ"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Then, we'll define a simply acquisition loop, in which we repeatedly grab a frame and perform inference to time how long it takes."
+      ],
+      "metadata": {
+        "id": "3-ctjg4wkxit"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "recording_duration = 100  # session length in frames\n",
+        "\n",
+        "# Pre-load images onto \"camera\"\n",
+        "camera = SimulatedCamera(video[:512])\n",
+        "\n",
+        "# Camera capture loop\n",
+        "inference_times = []\n",
+        "frames_recorded = 0\n",
+        "while frames_recorded < recording_duration:\n",
+        "    # Get the next frame.\n",
+        "    frame = camera.grab_frame()\n",
+        "    frames_recorded += 1\n",
+        "\n",
+        "    # Get inference results for the frame and time how long it took.\n",
+        "    t0 = perf_counter()\n",
+        "    frame_predictions = predictor.inference_model.predict_on_batch(np.expand_dims(frame, axis=0))\n",
+        "    dt = perf_counter() - t0\n",
+        "    inference_times.append(dt)\n",
+        "\n",
+        "# Convert to milliseconds.\n",
+        "inference_times = np.array(inference_times) * 1000\n",
+        "\n",
+        "# Separate out first timing from the rest. The first inference call is much slower as it builds the compute graph.\n",
+        "first_inference_time, inference_times = inference_times[0], inference_times[1:]\n",
+        "print(f\"First inference time: {first_inference_time:.1f} ms\")\n",
+        "print(f\"Inference times: {inference_times.mean():.1f} +- {inference_times.std():.1f} ms\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ExhVDw_AaOJq",
+        "outputId": "3531b16e-4c0b-4e9f-a09c-9004105b469b"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "First inference time: 2181.9 ms\n",
+            "Inference times: 28.8 +- 2.6 ms\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "After the first batch, our inference latencies go way down and we can see how they vary over time:"
+      ],
+      "metadata": {
+        "id": "WtbC0_3ek8I-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(10, 4), dpi=120, facecolor=\"w\")\n",
+        "plt.plot(inference_times, \".\")\n",
+        "plt.xlabel(\"Time (frames)\")\n",
+        "plt.ylabel(\"Inference latency (ms)\")\n",
+        "plt.grid(True);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "R1uQIpjma5nJ",
+        "outputId": "92a06b58-9250-482a-e645-86bb4cc5647a"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(6, 4), dpi=120, facecolor=\"w\")\n",
+        "plt.hist(inference_times, bins=30)\n",
+        "plt.xlabel(\"Inference latency (ms)\")\n",
+        "plt.ylabel(\"PDF\");"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "ubgokqC4ct5m",
+        "outputId": "03fea67b-5c92-413f-f841-5c9464be08a6"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/docs/notebooks/Interactive_and_resumable_training.ipynb b/docs/notebooks/Interactive_and_resumable_training.ipynb
new file mode 100644
index 000000000..b4ebbd1c3
--- /dev/null
+++ b/docs/notebooks/Interactive_and_resumable_training.ipynb
@@ -0,0 +1,998 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "SLEAP - Interactive and resumable training.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "machine_shape": "hm"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "\"Open"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Interactive and resumable training\n",
+        "\n",
+        "Most of the time, you will be training models through the GUI or using the [`sleap-train` CLI](https://sleap.ai/guides/cli.html#sleap-train).\n",
+        "\n",
+        "If you'd like to customize the training process, however, you can use SLEAP's low-level training functionality interactively. This allows you to define scripts that train models according to your own workflow, for example, to **resume training** on an already trained model. Another possible application would be to train a model using **transfer learning**, where a pretrained model can be used to initialize the weights of the new model.\n",
+        "\n",
+        "In this notebook we will explore how to set up a training job and train a model for multiple rounds without the GUI or CLI."
+      ],
+      "metadata": {
+        "id": "DpvQa3M3n7jC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BeeqrLbdupmE"
+      },
+      "source": [
+        "## 1. Setup SLEAP\n",
+        "\n",
+        "Run this cell first to install SLEAP. If you get a dependency error in subsequent cells, just click **Runtime** → **Restart runtime** to reload the packages.\n",
+        "\n",
+        "Don't forget to set **Runtime** → **Change runtime type** → **GPU** as the accelerator."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BYxJ2rJOMW8B",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d2230650-4e45-46f3-ff8f-dbe271bb9eb9"
+      },
+      "source": [
+        "# This should take care of all the dependencies on colab:\n",
+        "!pip uninstall -y opencv-python opencv-contrib-python && pip install sleap\n",
+        "\n",
+        "\n",
+        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
+        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found existing installation: opencv-python 4.1.2.30\n",
+            "Uninstalling opencv-python-4.1.2.30:\n",
+            "  Successfully uninstalled opencv-python-4.1.2.30\n",
+            "Found existing installation: opencv-contrib-python 4.1.2.30\n",
+            "Uninstalling opencv-contrib-python-4.1.2.30:\n",
+            "  Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
+            "Collecting sleap\n",
+            "  Downloading sleap-1.2.2-py3-none-any.whl (62.0 MB)\n",
+            "\u001b[K     |████████████████████████████████| 62.0 MB 1.3 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: certifi<=2021.10.8,>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from sleap) (2021.10.8)\n",
+            "Requirement already satisfied: tensorflow<2.9.0,>=2.6.3 in /usr/local/lib/python3.7/dist-packages (from sleap) (2.8.0)\n",
+            "Requirement already satisfied: pyzmq in /usr/local/lib/python3.7/dist-packages (from sleap) (22.3.0)\n",
+            "Collecting jsonpickle==1.2\n",
+            "  Downloading jsonpickle-1.2-py2.py3-none-any.whl (32 kB)\n",
+            "Requirement already satisfied: scikit-learn==1.0.* in /usr/local/lib/python3.7/dist-packages (from sleap) (1.0.2)\n",
+            "Collecting opencv-python-headless<=4.5.5.62,>=4.2.0.34\n",
+            "  Downloading opencv_python_headless-4.5.5.62-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (47.7 MB)\n",
+            "\u001b[K     |████████████████████████████████| 47.7 MB 85 kB/s \n",
+            "\u001b[?25hCollecting rich==10.16.1\n",
+            "  Downloading rich-10.16.1-py3-none-any.whl (214 kB)\n",
+            "\u001b[K     |████████████████████████████████| 214 kB 49.1 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: numpy<=1.21.5,>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from sleap) (1.21.5)\n",
+            "Requirement already satisfied: imageio<=2.15.0 in /usr/local/lib/python3.7/dist-packages (from sleap) (2.4.1)\n",
+            "Collecting scikit-video\n",
+            "  Downloading scikit_video-1.1.11-py2.py3-none-any.whl (2.3 MB)\n",
+            "\u001b[K     |████████████████████████████████| 2.3 MB 38.3 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: h5py<=3.6.0,>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from sleap) (3.1.0)\n",
+            "Collecting cattrs==1.1.1\n",
+            "  Downloading cattrs-1.1.1-py3-none-any.whl (16 kB)\n",
+            "Collecting python-rapidjson\n",
+            "  Downloading python_rapidjson-1.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
+            "\u001b[K     |████████████████████████████████| 1.6 MB 39.3 MB/s \n",
+            "\u001b[?25hCollecting imgaug==0.4.0\n",
+            "  Downloading imgaug-0.4.0-py2.py3-none-any.whl (948 kB)\n",
+            "\u001b[K     |████████████████████████████████| 948 kB 40.4 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from sleap) (2.6.3)\n",
+            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from sleap) (0.11.2)\n",
+            "Collecting pykalman==0.9.5\n",
+            "  Downloading pykalman-0.9.5.tar.gz (228 kB)\n",
+            "\u001b[K     |████████████████████████████████| 228 kB 52.8 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from sleap) (3.13)\n",
+            "Collecting PySide2<=5.14.1,>=5.13.2\n",
+            "  Downloading PySide2-5.14.1-5.14.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (165.5 MB)\n",
+            "\u001b[K     |████████████████████████████████| 165.5 MB 76 kB/s \n",
+            "\u001b[?25hCollecting attrs==21.2.0\n",
+            "  Downloading attrs-21.2.0-py2.py3-none-any.whl (53 kB)\n",
+            "\u001b[K     |████████████████████████████████| 53 kB 2.3 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from sleap) (5.4.8)\n",
+            "Collecting imgstore==0.2.9\n",
+            "  Downloading imgstore-0.2.9-py2.py3-none-any.whl (904 kB)\n",
+            "\u001b[K     |████████████████████████████████| 904 kB 39.4 MB/s \n",
+            "\u001b[?25hCollecting jsmin\n",
+            "  Downloading jsmin-3.0.1.tar.gz (13 kB)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from sleap) (1.3.5)\n",
+            "Collecting segmentation-models==1.0.1\n",
+            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
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+            "  Downloading qimage2ndarray-1.8.3-py3-none-any.whl (11 kB)\n",
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+            "Collecting opencv-python\n",
+            "  Downloading opencv_python-4.5.5.64-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (60.5 MB)\n",
+            "\u001b[K     |████████████████████████████████| 60.5 MB 1.4 MB/s \n",
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+            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
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+            "Collecting commonmark<0.10.0,>=0.9.0\n",
+            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
+            "\u001b[K     |████████████████████████████████| 51 kB 6.5 MB/s \n",
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+            "Collecting keras-applications<=1.0.8,>=1.0.7\n",
+            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
+            "\u001b[K     |████████████████████████████████| 50 kB 6.0 MB/s \n",
+            "\u001b[?25hCollecting image-classifiers==1.0.0\n",
+            "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
+            "Collecting efficientnet==1.0.0\n",
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+            "Collecting shiboken2==5.14.1\n",
+            "  Downloading shiboken2-5.14.1-5.14.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (847 kB)\n",
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+            "Collecting tf-estimator-nightly==2.8.0.dev2021122109\n",
+            "  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
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+            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.0.1)\n",
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+            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.6.1)\n",
+            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.4.6)\n",
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+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.8)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.3.1)\n",
+            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.11.3)\n",
+            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.7.0)\n",
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+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.0.4)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.24.3)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (2.10)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.2.0)\n",
+            "Building wheels for collected packages: pykalman, jsmin\n",
+            "  Building wheel for pykalman (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for pykalman: filename=pykalman-0.9.5-py3-none-any.whl size=48462 sha256=dde739150408cee5e4cb98680575a79e9cf2574d606fea22d81dac69689e1b5f\n",
+            "  Stored in directory: /root/.cache/pip/wheels/6a/04/02/2dda6ea59c66d9e685affc8af3a31ad3a5d87b7311689efce6\n",
+            "  Building wheel for jsmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for jsmin: filename=jsmin-3.0.1-py3-none-any.whl size=13782 sha256=28e30a78deeb41cb8a5a2a452ecd4209438e26a6f74af8de2e29a7da35b6fe93\n",
+            "  Stored in directory: /root/.cache/pip/wheels/a4/0b/64/fb4f87526ecbdf7921769a39d91dcfe4860e621cf15b8250d6\n",
+            "Successfully built pykalman jsmin\n",
+            "Installing collected packages: keras-applications, tf-estimator-nightly, shiboken2, opencv-python, image-classifiers, efficientnet, commonmark, colorama, attrs, segmentation-models, scikit-video, rich, qimage2ndarray, python-rapidjson, PySide2, pykalman, opencv-python-headless, jsonpickle, jsmin, imgstore, imgaug, cattrs, sleap\n",
+            "  Attempting uninstall: attrs\n",
+            "    Found existing installation: attrs 21.4.0\n",
+            "    Uninstalling attrs-21.4.0:\n",
+            "      Successfully uninstalled attrs-21.4.0\n",
+            "  Attempting uninstall: imgaug\n",
+            "    Found existing installation: imgaug 0.2.9\n",
+            "    Uninstalling imgaug-0.2.9:\n",
+            "      Successfully uninstalled imgaug-0.2.9\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
+            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
+            "Successfully installed PySide2-5.14.1 attrs-21.2.0 cattrs-1.1.1 colorama-0.4.4 commonmark-0.9.1 efficientnet-1.0.0 image-classifiers-1.0.0 imgaug-0.4.0 imgstore-0.2.9 jsmin-3.0.1 jsonpickle-1.2 keras-applications-1.0.8 opencv-python-4.5.5.64 opencv-python-headless-4.5.5.62 pykalman-0.9.5 python-rapidjson-1.6 qimage2ndarray-1.8.3 rich-10.16.1 scikit-video-1.1.11 segmentation-models-1.0.1 shiboken2-5.14.1 sleap-1.2.2 tf-estimator-nightly-2.8.0.dev2021122109\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Import SLEAP to make sure it installed correctly and print out some information about the system:"
+      ],
+      "metadata": {
+        "id": "qjfoeOZvpV8o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jftAOyvvuQeh",
+        "outputId": "f62974d2-51e7-47d8-defb-ab6f970c995f"
+      },
+      "source": [
+        "import sleap\n",
+        "sleap.versions()\n",
+        "sleap.system_summary()"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
+            "SLEAP: 1.2.2\n",
+            "TensorFlow: 2.8.0\n",
+            "Numpy: 1.21.5\n",
+            "Python: 3.7.13\n",
+            "OS: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n",
+            "GPUs: 1/1 available\n",
+            "  Device: /physical_device:GPU:0\n",
+            "         Available: True\n",
+            "        Initalized: False\n",
+            "     Memory growth: None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wSdTJYOdu4L6"
+      },
+      "source": [
+        "## 2. Setup training data\n",
+        "\n",
+        "Here we will download an existing training dataset package. This is an `.slp` file that contains both the labeled poses, as well as the image data for labeled frames.\n",
+        "\n",
+        "If running on Google Colab, you'll want to replace this with mounting your Google Drive folder containing your own data, or if running locally, simply change the path to your labels below in `TRAINING_SLP_FILE`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sDIF3RKdM86u",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9c267834-935c-4f90-bb77-c0f15814ba2a"
+      },
+      "source": [
+        "# !curl -L --output labels.pkg.slp https://www.dropbox.com/s/b990gxjt3d3j3jh/210205.sleap_wt_gold.13pt.pkg.slp?dl=1\n",
+        "!curl -L --output labels.pkg.slp https://storage.googleapis.com/sleap-data/datasets/wt_gold.13pt/tracking_split2/train.pkg.slp\n",
+        "!ls -lah"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  619M  100  619M    0     0   106M      0  0:00:05  0:00:05 --:--:--  110M\n",
+            "total 620M\n",
+            "drwxr-xr-x 1 root root 4.0K Apr  3 23:48 .\n",
+            "drwxr-xr-x 1 root root 4.0K Apr  3 23:40 ..\n",
+            "drwxr-xr-x 4 root root 4.0K Mar 23 14:21 .config\n",
+            "-rw-r--r-- 1 root root 620M Apr  3 23:48 labels.pkg.slp\n",
+            "drwxr-xr-x 1 root root 4.0K Mar 23 14:22 sample_data\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "TRAINING_SLP_FILE = \"labels.pkg.slp\""
+      ],
+      "metadata": {
+        "id": "vbpBugZRp_S7"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-vYsPusvviiu"
+      },
+      "source": [
+        "## 3. Setup training job\n",
+        "\n",
+        "A SLEAP `TrainingJobConfig` is a structure that contains all of the hyperparameters needed to train a SLEAP model. This is typically saved out to `initial_config.json` and `training_config.json` in the model folder so that training runs can be reproduced if needed, as well as to store metadata necessary for inference.\n",
+        "\n",
+        "Normally, these are generated interactively by the GUI, or manually by editing an existing JSON file in a text editor. Here, we will define a configuration interactively entirely in Python."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Cqt1Bhp-OIsi"
+      },
+      "source": [
+        "from sleap.nn.config import *\n",
+        "\n",
+        "# Initialize the default training job configuration.\n",
+        "cfg = TrainingJobConfig()\n",
+        "\n",
+        "# Update path to training data we just downloaded.\n",
+        "cfg.data.labels.training_labels = TRAINING_SLP_FILE\n",
+        "cfg.data.labels.validation_fraction = 0.1\n",
+        "\n",
+        "# Preprocesssing and training parameters.\n",
+        "cfg.data.instance_cropping.center_on_part = \"thorax\"\n",
+        "cfg.optimization.augmentation_config.rotate = True\n",
+        "cfg.optimization.epochs = 10  # This is the maximum number of training rounds.\n",
+        "\n",
+        "# These configures the actual neural network and the model type:\n",
+        "cfg.model.backbone.unet = UNetConfig(\n",
+        "    filters=16,\n",
+        "    output_stride=4\n",
+        ")\n",
+        "cfg.model.heads.centered_instance = CenteredInstanceConfmapsHeadConfig(\n",
+        "    anchor_part=\"thorax\",\n",
+        "    sigma=1.5,\n",
+        "    output_stride=4\n",
+        ")\n",
+        "\n",
+        "# Setup how we want to save the trained model.\n",
+        "cfg.outputs.run_name = \"baseline_model.topdown\""
+      ],
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9qSU7BcKv4Gw"
+      },
+      "source": [
+        "Existing configs can also be loaded from a `.json` file with:\n",
+        "\n",
+        "```python\n",
+        "cfg = sleap.load_config(\"training_config.json\")\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Noq-XINMv8nz"
+      },
+      "source": [
+        "## 4. Training\n",
+        "Next we will create a SLEAP `Trainer` from the configuration we just specified. This handles all the nitty gritty mechanics necessary to setup training in the backend."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "enbK9O5Dv8Pd",
+        "outputId": "0e36a6e2-a7e8-4d0f-e1d3-0d1b7abaf490"
+      },
+      "source": [
+        "trainer = sleap.nn.training.Trainer.from_config(cfg)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.training:Loading training labels from: labels.pkg.slp\n",
+            "INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1\n",
+            "INFO:sleap.nn.training:  Splits: Training = 1440 / Validation = 160.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JwMTtOrmwUM9"
+      },
+      "source": [
+        "Great, now we're ready to do the first round of training. This is when the model will actually start to improve over time:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "6b2a262ed72e4c659969f996ac889aa7",
+            "b30cb8d1b5bc4e12b554794098bd2c46",
+            "973660ab9cb2472786b368a18db11c63",
+            "cdb03dbf1b804f0b8b9bfd738c5eb2ad"
+          ]
+        },
+        "id": "L8jNydTEwNA1",
+        "outputId": "51828b8c-6d8b-4743-e9d2-9153f5b571c3"
+      },
+      "source": [
+        "trainer.train()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.training:Setting up for training...\n",
+            "INFO:sleap.nn.training:Setting up pipeline builders...\n",
+            "INFO:sleap.nn.training:Setting up model...\n",
+            "INFO:sleap.nn.training:Building test pipeline...\n",
+            "INFO:sleap.nn.training:Loaded test example. [6.047s]\n",
+            "INFO:sleap.nn.training:  Input shape: (160, 160, 1)\n",
+            "INFO:sleap.nn.training:Created Keras model.\n",
+            "INFO:sleap.nn.training:  Backbone: UNet(stacks=1, filters=16, filters_rate=2, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=2, up_interpolate=False, block_contraction=False)\n",
+            "INFO:sleap.nn.training:  Max stride: 16\n",
+            "INFO:sleap.nn.training:  Parameters: 2,101,501\n",
+            "INFO:sleap.nn.training:  Heads: \n",
+            "INFO:sleap.nn.training:    [0] = CenteredInstanceConfmapsHead(part_names=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], anchor_part='thorax', sigma=1.5, output_stride=4, loss_weight=1.0)\n",
+            "INFO:sleap.nn.training:  Outputs: \n",
+            "INFO:sleap.nn.training:    [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 40, 40, 13), dtype=tf.float32, name=None), name='CenteredInstanceConfmapsHead/BiasAdd:0', description=\"created by layer 'CenteredInstanceConfmapsHead'\")\n",
+            "INFO:sleap.nn.training:Setting up data pipelines...\n",
+            "INFO:sleap.nn.training:Training set: n = 1440\n",
+            "INFO:sleap.nn.training:Validation set: n = 160\n",
+            "INFO:sleap.nn.training:Setting up optimization...\n",
+            "INFO:sleap.nn.training:  Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-06, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08)\n",
+            "INFO:sleap.nn.training:  Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-06, plateau_patience=10)\n",
+            "INFO:sleap.nn.training:Setting up outputs...\n",
+            "INFO:sleap.nn.training:Created run path: models/baseline_model.topdown\n",
+            "INFO:sleap.nn.training:Setting up visualization...\n",
+            "Unable to use Qt backend for matplotlib. This probably means Qt is running headless.\n",
+            "INFO:sleap.nn.training:Finished trainer set up. [10.4s]\n",
+            "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n",
+            "INFO:sleap.nn.training:Finished creating training datasets. [29.5s]\n",
+            "INFO:sleap.nn.training:Starting training loop...\n",
+            "Epoch 1/10\n",
+            "360/360 - 70s - loss: 0.0037 - head: 0.0029 - thorax: 0.0030 - abdomen: 0.0037 - wingL: 0.0041 - wingR: 0.0041 - forelegL4: 0.0037 - forelegR4: 0.0038 - midlegL4: 0.0041 - midlegR4: 0.0041 - hindlegL4: 0.0039 - hindlegR4: 0.0040 - eyeL: 0.0033 - eyeR: 0.0034 - val_loss: 0.0033 - val_head: 0.0017 - val_thorax: 0.0025 - val_abdomen: 0.0035 - val_wingL: 0.0039 - val_wingR: 0.0039 - val_forelegL4: 0.0033 - val_forelegR4: 0.0036 - val_midlegL4: 0.0040 - val_midlegR4: 0.0040 - val_hindlegL4: 0.0040 - val_hindlegR4: 0.0040 - val_eyeL: 0.0022 - val_eyeR: 0.0023 - lr: 1.0000e-04 - 70s/epoch - 194ms/step\n",
+            "Epoch 2/10\n",
+            "360/360 - 53s - loss: 0.0028 - head: 0.0013 - thorax: 0.0020 - abdomen: 0.0028 - wingL: 0.0031 - wingR: 0.0031 - forelegL4: 0.0032 - forelegR4: 0.0033 - midlegL4: 0.0039 - midlegR4: 0.0039 - hindlegL4: 0.0037 - hindlegR4: 0.0038 - eyeL: 0.0013 - eyeR: 0.0014 - val_loss: 0.0025 - val_head: 9.5906e-04 - val_thorax: 0.0013 - val_abdomen: 0.0023 - val_wingL: 0.0025 - val_wingR: 0.0025 - val_forelegL4: 0.0029 - val_forelegR4: 0.0030 - val_midlegL4: 0.0037 - val_midlegR4: 0.0038 - val_hindlegL4: 0.0037 - val_hindlegR4: 0.0038 - val_eyeL: 8.8668e-04 - val_eyeR: 9.7728e-04 - lr: 1.0000e-04 - 53s/epoch - 148ms/step\n",
+            "Epoch 3/10\n",
+            "360/360 - 55s - loss: 0.0023 - head: 8.0222e-04 - thorax: 9.4507e-04 - abdomen: 0.0022 - wingL: 0.0022 - wingR: 0.0022 - forelegL4: 0.0027 - forelegR4: 0.0028 - midlegL4: 0.0035 - midlegR4: 0.0036 - hindlegL4: 0.0034 - hindlegR4: 0.0036 - eyeL: 8.5909e-04 - eyeR: 8.8003e-04 - val_loss: 0.0021 - val_head: 7.4704e-04 - val_thorax: 6.8354e-04 - val_abdomen: 0.0020 - val_wingL: 0.0018 - val_wingR: 0.0019 - val_forelegL4: 0.0024 - val_forelegR4: 0.0025 - val_midlegL4: 0.0031 - val_midlegR4: 0.0034 - val_hindlegL4: 0.0032 - val_hindlegR4: 0.0035 - val_eyeL: 7.6220e-04 - val_eyeR: 7.1808e-04 - lr: 1.0000e-04 - 55s/epoch - 154ms/step\n",
+            "Epoch 4/10\n",
+            "360/360 - 61s - loss: 0.0019 - head: 6.5537e-04 - thorax: 5.3996e-04 - abdomen: 0.0019 - wingL: 0.0018 - wingR: 0.0018 - forelegL4: 0.0023 - forelegR4: 0.0024 - midlegL4: 0.0027 - midlegR4: 0.0029 - hindlegL4: 0.0029 - hindlegR4: 0.0032 - eyeL: 7.4337e-04 - eyeR: 7.2396e-04 - val_loss: 0.0017 - val_head: 5.5193e-04 - val_thorax: 3.6303e-04 - val_abdomen: 0.0018 - val_wingL: 0.0016 - val_wingR: 0.0016 - val_forelegL4: 0.0020 - val_forelegR4: 0.0020 - val_midlegL4: 0.0023 - val_midlegR4: 0.0026 - val_hindlegL4: 0.0027 - val_hindlegR4: 0.0031 - val_eyeL: 6.5068e-04 - val_eyeR: 6.0169e-04 - lr: 1.0000e-04 - 61s/epoch - 169ms/step\n",
+            "Epoch 5/10\n",
+            "360/360 - 57s - loss: 0.0016 - head: 5.6982e-04 - thorax: 4.1064e-04 - abdomen: 0.0017 - wingL: 0.0016 - wingR: 0.0016 - forelegL4: 0.0020 - forelegR4: 0.0020 - midlegL4: 0.0021 - midlegR4: 0.0022 - hindlegL4: 0.0024 - hindlegR4: 0.0028 - eyeL: 6.5447e-04 - eyeR: 6.3768e-04 - val_loss: 0.0014 - val_head: 4.9811e-04 - val_thorax: 3.0411e-04 - val_abdomen: 0.0015 - val_wingL: 0.0014 - val_wingR: 0.0014 - val_forelegL4: 0.0017 - val_forelegR4: 0.0019 - val_midlegL4: 0.0018 - val_midlegR4: 0.0020 - val_hindlegL4: 0.0023 - val_hindlegR4: 0.0026 - val_eyeL: 5.9634e-04 - val_eyeR: 5.8405e-04 - lr: 1.0000e-04 - 57s/epoch - 157ms/step\n",
+            "Epoch 6/10\n",
+            "360/360 - 54s - loss: 0.0014 - head: 5.1206e-04 - thorax: 3.4952e-04 - abdomen: 0.0015 - wingL: 0.0014 - wingR: 0.0014 - forelegL4: 0.0017 - forelegR4: 0.0018 - midlegL4: 0.0017 - midlegR4: 0.0018 - hindlegL4: 0.0020 - hindlegR4: 0.0023 - eyeL: 6.0045e-04 - eyeR: 5.7847e-04 - val_loss: 0.0012 - val_head: 4.3860e-04 - val_thorax: 2.5352e-04 - val_abdomen: 0.0014 - val_wingL: 0.0013 - val_wingR: 0.0012 - val_forelegL4: 0.0015 - val_forelegR4: 0.0016 - val_midlegL4: 0.0014 - val_midlegR4: 0.0017 - val_hindlegL4: 0.0020 - val_hindlegR4: 0.0022 - val_eyeL: 5.1261e-04 - val_eyeR: 5.5203e-04 - lr: 1.0000e-04 - 54s/epoch - 151ms/step\n",
+            "Epoch 7/10\n",
+            "360/360 - 54s - loss: 0.0012 - head: 4.7131e-04 - thorax: 3.1231e-04 - abdomen: 0.0014 - wingL: 0.0012 - wingR: 0.0012 - forelegL4: 0.0016 - forelegR4: 0.0016 - midlegL4: 0.0015 - midlegR4: 0.0016 - hindlegL4: 0.0018 - hindlegR4: 0.0020 - eyeL: 5.7016e-04 - eyeR: 5.4539e-04 - val_loss: 0.0011 - val_head: 4.3133e-04 - val_thorax: 2.2694e-04 - val_abdomen: 0.0013 - val_wingL: 0.0011 - val_wingR: 0.0011 - val_forelegL4: 0.0014 - val_forelegR4: 0.0015 - val_midlegL4: 0.0013 - val_midlegR4: 0.0015 - val_hindlegL4: 0.0018 - val_hindlegR4: 0.0020 - val_eyeL: 5.5373e-04 - val_eyeR: 5.0355e-04 - lr: 1.0000e-04 - 54s/epoch - 149ms/step\n",
+            "Epoch 8/10\n",
+            "360/360 - 53s - loss: 0.0011 - head: 4.3369e-04 - thorax: 2.6750e-04 - abdomen: 0.0013 - wingL: 0.0011 - wingR: 0.0011 - forelegL4: 0.0015 - forelegR4: 0.0015 - midlegL4: 0.0014 - midlegR4: 0.0014 - hindlegL4: 0.0017 - hindlegR4: 0.0018 - eyeL: 5.2745e-04 - eyeR: 5.0480e-04 - val_loss: 0.0011 - val_head: 4.1774e-04 - val_thorax: 2.4407e-04 - val_abdomen: 0.0013 - val_wingL: 0.0011 - val_wingR: 0.0010 - val_forelegL4: 0.0013 - val_forelegR4: 0.0014 - val_midlegL4: 0.0012 - val_midlegR4: 0.0014 - val_hindlegL4: 0.0017 - val_hindlegR4: 0.0018 - val_eyeL: 6.2877e-04 - val_eyeR: 5.7243e-04 - lr: 1.0000e-04 - 53s/epoch - 148ms/step\n",
+            "Epoch 9/10\n",
+            "360/360 - 53s - loss: 0.0010 - head: 4.0425e-04 - thorax: 2.3597e-04 - abdomen: 0.0012 - wingL: 0.0010 - wingR: 0.0011 - forelegL4: 0.0014 - forelegR4: 0.0014 - midlegL4: 0.0013 - midlegR4: 0.0013 - hindlegL4: 0.0016 - hindlegR4: 0.0017 - eyeL: 5.0906e-04 - eyeR: 4.9227e-04 - val_loss: 0.0010 - val_head: 3.9088e-04 - val_thorax: 2.1458e-04 - val_abdomen: 0.0012 - val_wingL: 0.0010 - val_wingR: 9.4879e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0013 - val_midlegL4: 0.0011 - val_midlegR4: 0.0014 - val_hindlegL4: 0.0016 - val_hindlegR4: 0.0017 - val_eyeL: 4.6829e-04 - val_eyeR: 4.7323e-04 - lr: 1.0000e-04 - 53s/epoch - 147ms/step\n",
+            "Epoch 10/10\n",
+            "360/360 - 55s - loss: 9.7632e-04 - head: 3.7896e-04 - thorax: 2.1828e-04 - abdomen: 0.0011 - wingL: 9.9185e-04 - wingR: 9.9033e-04 - forelegL4: 0.0014 - forelegR4: 0.0013 - midlegL4: 0.0012 - midlegR4: 0.0012 - hindlegL4: 0.0015 - hindlegR4: 0.0016 - eyeL: 4.7323e-04 - eyeR: 4.5868e-04 - val_loss: 9.2870e-04 - val_head: 3.3704e-04 - val_thorax: 1.5806e-04 - val_abdomen: 0.0010 - val_wingL: 9.5121e-04 - val_wingR: 9.2122e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0014 - val_midlegL4: 0.0010 - val_midlegR4: 0.0012 - val_hindlegL4: 0.0015 - val_hindlegR4: 0.0016 - val_eyeL: 4.2130e-04 - val_eyeR: 4.1479e-04 - lr: 1.0000e-04 - 55s/epoch - 154ms/step\n",
+            "INFO:sleap.nn.training:Finished training loop. [9.4 min]\n",
+            "INFO:sleap.nn.training:Deleting visualization directory: models/baseline_model.topdown/viz\n",
+            "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6b2a262ed72e4c659969f996ac889aa7"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
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+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.train.slp\n",
+            "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.train.npz\n",
+            "INFO:sleap.nn.evals:OKS mAP: 0.518988\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "973660ab9cb2472786b368a18db11c63"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
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+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.val.slp\n",
+            "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.val.npz\n",
+            "INFO:sleap.nn.evals:OKS mAP: 0.520377\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0RHAJkqowaF5"
+      },
+      "source": [
+        "## 5. Continuing training\n",
+        "\n",
+        "If we still have the trainer in memory, we can continue training by simply calling `trainer.train()` again with a potentially different number of epochs:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 381,
+          "referenced_widgets": [
+            "d49529f91f6d4090a7820b081094823d",
+            "f5a6112df0964befa6704030d2995ecd",
+            "8291326df0b9435b8ba2298c8977778b",
+            "3e68443331b44baa9cb8f8e077e2075b"
+          ]
+        },
+        "id": "ENOiptvQwrtI",
+        "outputId": "ccdec444-17ae-4040-9aa3-509086e3dc37"
+      },
+      "source": [
+        "trainer.config.optimization.epochs = 3\n",
+        "trainer.train()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n",
+            "INFO:sleap.nn.training:Finished creating training datasets. [29.4s]\n",
+            "INFO:sleap.nn.training:Starting training loop...\n",
+            "Epoch 1/3\n",
+            "360/360 - 57s - loss: 9.1732e-04 - head: 3.5629e-04 - thorax: 1.9609e-04 - abdomen: 0.0010 - wingL: 9.1318e-04 - wingR: 9.1330e-04 - forelegL4: 0.0013 - forelegR4: 0.0013 - midlegL4: 0.0011 - midlegR4: 0.0011 - hindlegL4: 0.0014 - hindlegR4: 0.0015 - eyeL: 4.4475e-04 - eyeR: 4.3944e-04 - val_loss: 9.2727e-04 - val_head: 3.8719e-04 - val_thorax: 1.5200e-04 - val_abdomen: 0.0011 - val_wingL: 9.3115e-04 - val_wingR: 8.9376e-04 - val_forelegL4: 0.0012 - val_forelegR4: 0.0012 - val_midlegL4: 9.9703e-04 - val_midlegR4: 0.0012 - val_hindlegL4: 0.0015 - val_hindlegR4: 0.0016 - val_eyeL: 4.5374e-04 - val_eyeR: 5.1839e-04 - lr: 1.0000e-04 - 57s/epoch - 158ms/step\n",
+            "Epoch 2/3\n",
+            "360/360 - 56s - loss: 8.7900e-04 - head: 3.4532e-04 - thorax: 1.7895e-04 - abdomen: 0.0010 - wingL: 8.7539e-04 - wingR: 8.8524e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 0.0010 - midlegR4: 0.0010 - hindlegL4: 0.0014 - hindlegR4: 0.0014 - eyeL: 4.3484e-04 - eyeR: 4.2888e-04 - val_loss: 8.5310e-04 - val_head: 3.0429e-04 - val_thorax: 1.4837e-04 - val_abdomen: 0.0010 - val_wingL: 8.2237e-04 - val_wingR: 8.3093e-04 - val_forelegL4: 0.0011 - val_forelegR4: 0.0012 - val_midlegL4: 8.5634e-04 - val_midlegR4: 0.0011 - val_hindlegL4: 0.0014 - val_hindlegR4: 0.0015 - val_eyeL: 4.0362e-04 - val_eyeR: 3.8104e-04 - lr: 1.0000e-04 - 56s/epoch - 156ms/step\n",
+            "Epoch 3/3\n",
+            "360/360 - 56s - loss: 8.4466e-04 - head: 3.4540e-04 - thorax: 1.6180e-04 - abdomen: 9.6890e-04 - wingL: 8.4974e-04 - wingR: 8.5187e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 9.5015e-04 - midlegR4: 9.8870e-04 - hindlegL4: 0.0013 - hindlegR4: 0.0014 - eyeL: 4.2245e-04 - eyeR: 4.0856e-04 - val_loss: 8.2153e-04 - val_head: 3.1832e-04 - val_thorax: 1.4803e-04 - val_abdomen: 9.4013e-04 - val_wingL: 8.4738e-04 - val_wingR: 8.4686e-04 - val_forelegL4: 0.0010 - val_forelegR4: 0.0011 - val_midlegL4: 8.5740e-04 - val_midlegR4: 0.0010 - val_hindlegL4: 0.0014 - val_hindlegR4: 0.0015 - val_eyeL: 3.7928e-04 - val_eyeR: 3.8285e-04 - lr: 1.0000e-04 - 56s/epoch - 156ms/step\n",
+            "INFO:sleap.nn.training:Finished training loop. [2.8 min]\n",
+            "INFO:sleap.nn.training:Deleting visualization directory: models/baseline_model.topdown/viz\n",
+            "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d49529f91f6d4090a7820b081094823d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
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+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.train.slp\n",
+            "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.train.npz\n",
+            "INFO:sleap.nn.evals:OKS mAP: 0.551905\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8291326df0b9435b8ba2298c8977778b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.val.slp\n",
+            "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.val.npz\n",
+            "INFO:sleap.nn.evals:OKS mAP: 0.551469\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bUjjGVWGyYZu"
+      },
+      "source": [
+        "As you can see, the loss and accuracy pick up from where it left off in the previous training.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GSGW14-Px09E"
+      },
+      "source": [
+        "Usually, however, if you're continuing training it's likely because you're starting off from an already trained model.\n",
+        "\n",
+        "In this case, all you need to do to continue training is to create a new `Trainer` from the existing model configuration and load up the weights before continuing training:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NDL6ScTDxrso",
+        "outputId": "f63c3ef8-97d0-4484-e951-b120dcbbffac"
+      },
+      "source": [
+        "# Load config.\n",
+        "cfg = sleap.load_config(\"models/baseline_model.topdown\")\n",
+        "# cfg.outputs.run_name = \"new_folder\"  # Set the run_name to a new value if you want the model to be saved to a different folder.\n",
+        "\n",
+        "# Create and initialize the trainer.\n",
+        "trainer = sleap.nn.training.Trainer.from_config(cfg)\n",
+        "trainer.setup()\n",
+        "\n",
+        "# Replace the randomly initialized weights with the saved weights.\n",
+        "trainer.keras_model.load_weights(\"models/baseline_model.topdown/best_model.h5\")"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.training:Loading training labels from: labels.pkg.slp\n",
+            "INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1\n",
+            "INFO:sleap.nn.training:  Splits: Training = 1440 / Validation = 160.\n",
+            "INFO:sleap.nn.training:Setting up for training...\n",
+            "INFO:sleap.nn.training:Setting up pipeline builders...\n",
+            "INFO:sleap.nn.training:Setting up model...\n",
+            "INFO:sleap.nn.training:Building test pipeline...\n",
+            "INFO:sleap.nn.training:Loaded test example. [1.909s]\n",
+            "INFO:sleap.nn.training:  Input shape: (160, 160, 1)\n",
+            "INFO:sleap.nn.training:Created Keras model.\n",
+            "INFO:sleap.nn.training:  Backbone: UNet(stacks=1, filters=16, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=2, up_interpolate=False, block_contraction=False)\n",
+            "INFO:sleap.nn.training:  Max stride: 16\n",
+            "INFO:sleap.nn.training:  Parameters: 2,101,501\n",
+            "INFO:sleap.nn.training:  Heads: \n",
+            "INFO:sleap.nn.training:    [0] = CenteredInstanceConfmapsHead(part_names=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], anchor_part='thorax', sigma=1.5, output_stride=4, loss_weight=1.0)\n",
+            "INFO:sleap.nn.training:  Outputs: \n",
+            "INFO:sleap.nn.training:    [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 40, 40, 13), dtype=tf.float32, name=None), name='CenteredInstanceConfmapsHead/BiasAdd:0', description=\"created by layer 'CenteredInstanceConfmapsHead'\")\n",
+            "INFO:sleap.nn.training:Setting up data pipelines...\n",
+            "INFO:sleap.nn.training:Training set: n = 1440\n",
+            "INFO:sleap.nn.training:Validation set: n = 160\n",
+            "INFO:sleap.nn.training:Setting up optimization...\n",
+            "INFO:sleap.nn.training:  Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-06, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08)\n",
+            "INFO:sleap.nn.training:  Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-06, plateau_patience=10)\n",
+            "INFO:sleap.nn.training:Setting up outputs...\n",
+            "INFO:sleap.nn.training:Created run path: models/baseline_model.topdown\n",
+            "INFO:sleap.nn.training:Setting up visualization...\n",
+            "INFO:sleap.nn.training:Finished trainer set up. [6.0s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 381,
+          "referenced_widgets": [
+            "c74d0a9e497146acaf8da36faf5f496a",
+            "7558a9bb888840e19b42a0bf0faac822",
+            "bf6a847899a24fcea5f14409a7ee1c33",
+            "53b56611dfec45b49e92fa0716fa2f97"
+          ]
+        },
+        "id": "HlGP3dYMy2NG",
+        "outputId": "c32a4240-1abd-401b-caab-4d64bec8348d"
+      },
+      "source": [
+        "trainer.config.optimization.epochs = 3\n",
+        "trainer.train()"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...\n",
+            "INFO:sleap.nn.training:Finished creating training datasets. [28.9s]\n",
+            "INFO:sleap.nn.training:Starting training loop...\n",
+            "Epoch 1/3\n",
+            "360/360 - 63s - loss: 8.2769e-04 - head: 3.4427e-04 - thorax: 1.6900e-04 - abdomen: 9.4941e-04 - wingL: 8.1514e-04 - wingR: 8.1826e-04 - forelegL4: 0.0012 - forelegR4: 0.0012 - midlegL4: 9.2980e-04 - midlegR4: 9.6439e-04 - hindlegL4: 0.0013 - hindlegR4: 0.0013 - eyeL: 4.2129e-04 - eyeR: 4.0767e-04 - val_loss: 7.8855e-04 - val_head: 3.2701e-04 - val_thorax: 1.8405e-04 - val_abdomen: 0.0010 - val_wingL: 7.3709e-04 - val_wingR: 7.1027e-04 - val_forelegL4: 0.0010 - val_forelegR4: 0.0011 - val_midlegL4: 9.3918e-04 - val_midlegR4: 9.0288e-04 - val_hindlegL4: 0.0012 - val_hindlegR4: 0.0013 - val_eyeL: 3.8746e-04 - val_eyeR: 3.3939e-04 - lr: 1.0000e-04 - 63s/epoch - 174ms/step\n",
+            "Epoch 2/3\n",
+            "360/360 - 58s - loss: 7.9662e-04 - head: 3.2407e-04 - thorax: 1.5127e-04 - abdomen: 9.1911e-04 - wingL: 7.6866e-04 - wingR: 7.8884e-04 - forelegL4: 0.0011 - forelegR4: 0.0011 - midlegL4: 8.8560e-04 - midlegR4: 9.3151e-04 - hindlegL4: 0.0012 - hindlegR4: 0.0013 - eyeL: 4.1677e-04 - eyeR: 3.9983e-04 - val_loss: 7.3673e-04 - val_head: 2.8314e-04 - val_thorax: 1.1026e-04 - val_abdomen: 9.4263e-04 - val_wingL: 6.7871e-04 - val_wingR: 6.4992e-04 - val_forelegL4: 0.0011 - val_forelegR4: 0.0011 - val_midlegL4: 8.0315e-04 - val_midlegR4: 8.3331e-04 - val_hindlegL4: 0.0012 - val_hindlegR4: 0.0012 - val_eyeL: 3.4531e-04 - val_eyeR: 3.5707e-04 - lr: 1.0000e-04 - 58s/epoch - 162ms/step\n",
+            "Epoch 3/3\n",
+            "360/360 - 58s - loss: 7.6463e-04 - head: 3.0854e-04 - thorax: 1.3497e-04 - abdomen: 8.9188e-04 - wingL: 7.4921e-04 - wingR: 7.5430e-04 - forelegL4: 0.0011 - forelegR4: 0.0011 - midlegL4: 8.3320e-04 - midlegR4: 8.7736e-04 - hindlegL4: 0.0012 - hindlegR4: 0.0013 - eyeL: 3.9640e-04 - eyeR: 3.7940e-04 - val_loss: 7.0126e-04 - val_head: 2.8905e-04 - val_thorax: 1.1305e-04 - val_abdomen: 9.0676e-04 - val_wingL: 6.4827e-04 - val_wingR: 6.2576e-04 - val_forelegL4: 0.0010 - val_forelegR4: 9.8253e-04 - val_midlegL4: 8.0471e-04 - val_midlegR4: 7.3788e-04 - val_hindlegL4: 0.0011 - val_hindlegR4: 0.0012 - val_eyeL: 3.1543e-04 - val_eyeR: 3.4044e-04 - lr: 1.0000e-04 - 58s/epoch - 161ms/step\n",
+            "INFO:sleap.nn.training:Finished training loop. [3.0 min]\n",
+            "INFO:sleap.nn.training:Deleting visualization directory: models/baseline_model.topdown/viz\n",
+            "INFO:sleap.nn.training:Saving evaluation metrics to model folder...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c74d0a9e497146acaf8da36faf5f496a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "
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+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
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+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.train.slp\n",
+            "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.train.npz\n",
+            "INFO:sleap.nn.evals:OKS mAP: 0.597609\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bf6a847899a24fcea5f14409a7ee1c33"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              ""
+            ],
+            "text/html": [
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "
\n",
+              "
\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:sleap.nn.evals:Saved predictions: models/baseline_model.topdown/labels_pr.val.slp\n",
+            "INFO:sleap.nn.evals:Saved metrics: models/baseline_model.topdown/metrics.val.npz\n",
+            "INFO:sleap.nn.evals:OKS mAP: 0.621393\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kb4YzP7AzYZB"
+      },
+      "source": [
+        "Again, the loss and accuracy pick up from where they left off prior to this round of training.\n",
+        "\n",
+        "The resulting model can be used as usual for inference on new data."
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/docs/notebooks/Post_inference_tracking.ipynb b/docs/notebooks/Post_inference_tracking.ipynb
new file mode 100644
index 000000000..ead7d2747
--- /dev/null
+++ b/docs/notebooks/Post_inference_tracking.ipynb
@@ -0,0 +1,578 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "SLEAP - Post-inference tracking.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "\"Open"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Post-inference tracking\n",
+        "\n",
+        "In tracking, we associate the poses (that were detected within individual frames) across time such that they belong to the same individual.\n",
+        "\n",
+        "Since we typically do this separately, it is sometimes desirable to tweak the tracking parameters to optimize accuracy without having to re-run inference (e.g., `sleap-track`).\n",
+        "\n",
+        "In this notebook, we will explore how to re-run the tracking given an existing predictions SLP file.\n",
+        "\n",
+        "**Note:** Tracking does not run on the GPU, so this notebook can be run locally on your computer without the hassle of uploading your data if desired."
+      ],
+      "metadata": {
+        "id": "gQXmUCj9ljP3"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Setup SLEAP\n",
+        "\n",
+        "Run this cell first to install SLEAP. If you get a dependency error in subsequent cells, just click **Runtime** → **Restart runtime** to reload the packages.\n"
+      ],
+      "metadata": {
+        "id": "WL67LNf10hev"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Install"
+      ],
+      "metadata": {
+        "id": "UtfcHSZCDnvS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# This should take care of all the dependencies on colab:\n",
+        "!pip uninstall -y opencv-python opencv-contrib-python && pip install sleap\n",
+        "\n",
+        "# But to do it locally, we'd recommend the conda package (available on Windows + Linux):\n",
+        "# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HH0weH9f-T1N",
+        "outputId": "d6f69d8d-9aed-4793-c346-2ab60f110316"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found existing installation: opencv-python 4.1.2.30\n",
+            "Uninstalling opencv-python-4.1.2.30:\n",
+            "  Successfully uninstalled opencv-python-4.1.2.30\n",
+            "Found existing installation: opencv-contrib-python 4.1.2.30\n",
+            "Uninstalling opencv-contrib-python-4.1.2.30:\n",
+            "  Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
+            "Collecting sleap\n",
+            "  Downloading sleap-1.2.2-py3-none-any.whl (62.0 MB)\n",
+            "\u001b[K     |████████████████████████████████| 62.0 MB 19 kB/s \n",
+            "\u001b[?25hCollecting pykalman==0.9.5\n",
+            "  Downloading pykalman-0.9.5.tar.gz (228 kB)\n",
+            "\u001b[K     |████████████████████████████████| 228 kB 21.7 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: certifi<=2021.10.8,>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from sleap) (2021.10.8)\n",
+            "Requirement already satisfied: h5py<=3.6.0,>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from sleap) (3.1.0)\n",
+            "Collecting opencv-python-headless<=4.5.5.62,>=4.2.0.34\n",
+            "  Downloading opencv_python_headless-4.5.5.62-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (47.7 MB)\n",
+            "\u001b[K     |████████████████████████████████| 47.7 MB 1.4 MB/s \n",
+            "\u001b[?25hCollecting jsonpickle==1.2\n",
+            "  Downloading jsonpickle-1.2-py2.py3-none-any.whl (32 kB)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from sleap) (3.13)\n",
+            "Requirement already satisfied: scikit-learn==1.0.* in /usr/local/lib/python3.7/dist-packages (from sleap) (1.0.2)\n",
+            "Collecting imgstore==0.2.9\n",
+            "  Downloading imgstore-0.2.9-py2.py3-none-any.whl (904 kB)\n",
+            "\u001b[K     |████████████████████████████████| 904 kB 44.2 MB/s \n",
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+            "Requirement already satisfied: scipy<=1.7.3,>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from sleap) (1.4.1)\n",
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+            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from sleap) (1.3.5)\n",
+            "Collecting segmentation-models==1.0.1\n",
+            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
+            "Collecting rich==10.16.1\n",
+            "  Downloading rich-10.16.1-py3-none-any.whl (214 kB)\n",
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+            "\u001b[?25hRequirement already satisfied: numpy<=1.21.5,>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from sleap) (1.21.5)\n",
+            "Collecting qimage2ndarray<=1.8.3,>=1.8.2\n",
+            "  Downloading qimage2ndarray-1.8.3-py3-none-any.whl (11 kB)\n",
+            "Collecting python-rapidjson\n",
+            "  Downloading python_rapidjson-1.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
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+            "\u001b[?25hCollecting attrs==21.2.0\n",
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+            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from sleap) (0.18.3)\n",
+            "Collecting cattrs==1.1.1\n",
+            "  Downloading cattrs-1.1.1-py3-none-any.whl (16 kB)\n",
+            "Collecting jsmin\n",
+            "  Downloading jsmin-3.0.1.tar.gz (13 kB)\n",
+            "Collecting scikit-video\n",
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+            "Collecting PySide2<=5.14.1,>=5.13.2\n",
+            "  Downloading PySide2-5.14.1-5.14.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (165.5 MB)\n",
+            "\u001b[K     |████████████████████████████████| 165.5 MB 69 kB/s \n",
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+            "  Downloading opencv_python-4.5.5.64-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (60.5 MB)\n",
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+            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
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+            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
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+            "  Downloading efficientnet-1.0.0-py3-none-any.whl (17 kB)\n",
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+            "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
+            "Collecting keras-applications<=1.0.8,>=1.0.7\n",
+            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
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+            "  Downloading shiboken2-5.14.1-5.14.1-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl (847 kB)\n",
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+            "Collecting tf-estimator-nightly==2.8.0.dev2021122109\n",
+            "  Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
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+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.3.6)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.6.1)\n",
+            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.0.1)\n",
+            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (2.23.0)\n",
+            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.35.0)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.8)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (0.2.8)\n",
+            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.2.4)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.3.1)\n",
+            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (4.11.3)\n",
+            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.7.0)\n",
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+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (2.10)\n",
+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.0.4)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (1.24.3)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9.0,>=2.6.3->sleap) (3.2.0)\n",
+            "Building wheels for collected packages: pykalman, jsmin\n",
+            "  Building wheel for pykalman (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for pykalman: filename=pykalman-0.9.5-py3-none-any.whl size=48462 sha256=5de7d8c6487261ac5359426edf6b9d6ff977786a758424aaa6462a743fae77e4\n",
+            "  Stored in directory: /root/.cache/pip/wheels/6a/04/02/2dda6ea59c66d9e685affc8af3a31ad3a5d87b7311689efce6\n",
+            "  Building wheel for jsmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for jsmin: filename=jsmin-3.0.1-py3-none-any.whl size=13782 sha256=353b91b543700f74d4c7801c636ff32de6e99c9578162db575ea8d5e0b29d64e\n",
+            "  Stored in directory: /root/.cache/pip/wheels/a4/0b/64/fb4f87526ecbdf7921769a39d91dcfe4860e621cf15b8250d6\n",
+            "Successfully built pykalman jsmin\n",
+            "Installing collected packages: keras-applications, tf-estimator-nightly, shiboken2, opencv-python, image-classifiers, efficientnet, commonmark, colorama, attrs, segmentation-models, scikit-video, rich, qimage2ndarray, python-rapidjson, PySide2, pykalman, opencv-python-headless, jsonpickle, jsmin, imgstore, imgaug, cattrs, sleap\n",
+            "  Attempting uninstall: attrs\n",
+            "    Found existing installation: attrs 21.4.0\n",
+            "    Uninstalling attrs-21.4.0:\n",
+            "      Successfully uninstalled attrs-21.4.0\n",
+            "  Attempting uninstall: imgaug\n",
+            "    Found existing installation: imgaug 0.2.9\n",
+            "    Uninstalling imgaug-0.2.9:\n",
+            "      Successfully uninstalled imgaug-0.2.9\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
+            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
+            "Successfully installed PySide2-5.14.1 attrs-21.2.0 cattrs-1.1.1 colorama-0.4.4 commonmark-0.9.1 efficientnet-1.0.0 image-classifiers-1.0.0 imgaug-0.4.0 imgstore-0.2.9 jsmin-3.0.1 jsonpickle-1.2 keras-applications-1.0.8 opencv-python-4.5.5.64 opencv-python-headless-4.5.5.62 pykalman-0.9.5 python-rapidjson-1.6 qimage2ndarray-1.8.3 rich-10.16.1 scikit-video-1.1.11 segmentation-models-1.0.1 shiboken2-5.14.1 sleap-1.2.2 tf-estimator-nightly-2.8.0.dev2021122109\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Test"
+      ],
+      "metadata": {
+        "id": "d10pcIu70oLb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title SLEAP and system versions: { display-mode: \"form\" }\n",
+        "import sleap\n",
+        "sleap.versions()\n",
+        "sleap.system_summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WBGKYmLj9Zc2",
+        "outputId": "8f044c67-3abe-4b8b-8552-db2b5c756c7c"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
+            "SLEAP: 1.2.2\n",
+            "TensorFlow: 2.8.0\n",
+            "Numpy: 1.21.5\n",
+            "Python: 3.7.13\n",
+            "OS: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n",
+            "GPUs: None detected.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 2. Setup data\n",
+        "Here we're downloading an existing `.slp` file with predictions and the corresponding `.mp4` video.\n",
+        "\n",
+        "You should replace this with Google Drive mounting if running this on Google Colab, or simply skip it altogether and just set the paths below if running locally."
+      ],
+      "metadata": {
+        "id": "hYBojEjY9qyr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "akfAyAo-9cAd",
+        "outputId": "456bd33c-c1f6-4d57-dc37-a58ef8717472"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2022-04-04 00:10:34--  https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/fly_clip.mp4?raw=true\n",
+            "Resolving github.com (github.com)... 13.114.40.48\n",
+            "Connecting to github.com (github.com)|13.114.40.48|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://github.com/talmolab/sleap-tutorial-uo/raw/main/data/fly_clip.mp4 [following]\n",
+            "--2022-04-04 00:10:34--  https://github.com/talmolab/sleap-tutorial-uo/raw/main/data/fly_clip.mp4\n",
+            "Reusing existing connection to github.com:443.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://raw.githubusercontent.com/talmolab/sleap-tutorial-uo/main/data/fly_clip.mp4 [following]\n",
+            "--2022-04-04 00:10:34--  https://raw.githubusercontent.com/talmolab/sleap-tutorial-uo/main/data/fly_clip.mp4\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 676194 (660K) [application/octet-stream]\n",
+            "Saving to: ‘fly_clip.mp4’\n",
+            "\n",
+            "fly_clip.mp4        100%[===================>] 660.35K  --.-KB/s    in 0.05s   \n",
+            "\n",
+            "2022-04-04 00:10:36 (12.1 MB/s) - ‘fly_clip.mp4’ saved [676194/676194]\n",
+            "\n",
+            "--2022-04-04 00:10:36--  https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/predictions.slp?raw=true\n",
+            "Resolving github.com (github.com)... 52.69.186.44\n",
+            "Connecting to github.com (github.com)|52.69.186.44|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://github.com/talmolab/sleap-tutorial-uo/raw/main/data/predictions.slp [following]\n",
+            "--2022-04-04 00:10:37--  https://github.com/talmolab/sleap-tutorial-uo/raw/main/data/predictions.slp\n",
+            "Reusing existing connection to github.com:443.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://raw.githubusercontent.com/talmolab/sleap-tutorial-uo/main/data/predictions.slp [following]\n",
+            "--2022-04-04 00:10:37--  https://raw.githubusercontent.com/talmolab/sleap-tutorial-uo/main/data/predictions.slp\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 420976 (411K) [application/octet-stream]\n",
+            "Saving to: ‘predictions.slp’\n",
+            "\n",
+            "predictions.slp     100%[===================>] 411.11K  --.-KB/s    in 0.04s   \n",
+            "\n",
+            "2022-04-04 00:10:38 (9.66 MB/s) - ‘predictions.slp’ saved [420976/420976]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget -O fly_clip.mp4 https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/fly_clip.mp4?raw=true\n",
+        "!wget -O predictions.slp https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/predictions.slp?raw=true"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "PREDICTIONS_FILE = \"predictions.slp\""
+      ],
+      "metadata": {
+        "id": "gQSc_ZjFnHl9"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 3. Track"
+      ],
+      "metadata": {
+        "id": "9z5rbej_-_Ea"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load predictions\n",
+        "labels = sleap.load_file(PREDICTIONS_FILE)\n",
+        "\n",
+        "# Here I'm removing the tracks so we just have instances without any tracking applied.\n",
+        "for instance in labels.instances():\n",
+        "    instance.track = None\n",
+        "labels.tracks = []\n",
+        "labels"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MhHCTkdr-wTz",
+        "outputId": "2e286994-eb4c-4648-c6b9-ab3e7d0cc605"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Labels(labeled_frames=1350, videos=1, skeletons=1, tracks=0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Here we create a tracker with the options we want to experiment with. You can [read more about tracking in the documentation](https://sleap.ai/guides/proofreading.html#tracking-methods) or the parameters in the [`sleap-track` CLI help](https://sleap.ai/guides/cli.html#sleap-track)."
+      ],
+      "metadata": {
+        "id": "hwFC2WYWBQXe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create tracker\n",
+        "tracker = sleap.nn.tracking.Tracker.make_tracker_by_name(\n",
+        "    # General tracking options\n",
+        "    tracker=\"flow\",\n",
+        "    track_window=3,\n",
+        "\n",
+        "    # Matching options\n",
+        "    similarity=\"instance\",\n",
+        "    match=\"greedy\",\n",
+        "    min_new_track_points=1,\n",
+        "    min_match_points=1,\n",
+        "\n",
+        "    # Optical flow options (only applies to \"flow\" tracker)\n",
+        "    img_scale=0.5,\n",
+        "    of_window_size=21,\n",
+        "    of_max_levels=3,\n",
+        "\n",
+        "    # Pre-tracking filtering options\n",
+        "    target_instance_count=2,\n",
+        "    pre_cull_to_target=True,\n",
+        "    pre_cull_iou_threshold=0.8,\n",
+        "\n",
+        "    # Post-tracking filtering options\n",
+        "    post_connect_single_breaks=True,\n",
+        "    clean_instance_count=0,\n",
+        "    clean_iou_threshold=None,\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "AgDVuL-u9_iv"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Next we'll actually run the tracking on each frame. This might take a bit longer when using the `\"flow\"` method."
+      ],
+      "metadata": {
+        "id": "EfMhLxWcBqBg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tracked_lfs = []\n",
+        "for lf in labels:\n",
+        "    lf.instances = tracker.track(lf.instances, img=lf.image)\n",
+        "    tracked_lfs.append(lf)\n",
+        "tracked_labels = sleap.Labels(tracked_lfs)\n",
+        "tracked_labels"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "q-EE7r0pBpfD",
+        "outputId": "eabfe089-b122-494d-c41e-996b0243ab71"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Labels(labeled_frames=1350, videos=1, skeletons=1, tracks=2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 4. Inspect and save\n",
+        "\n",
+        "Let's see the results and save out the tracked predictions."
+      ],
+      "metadata": {
+        "id": "OjUvwRzWCJ_G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tracked_labels[0].plot(scale=0.25)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 287
+        },
+        "id": "g-ia6hYGCXZX",
+        "outputId": "2652a6e2-6f63-4b81-dd54-d8a01c6c25a4"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tracked_labels[100].plot(scale=0.25)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 287
+        },
+        "id": "nDMnJFmFCszY",
+        "outputId": "90b984e6-b6bb-468b-eb66-2b0537758c44"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "
"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tracked_labels.save(\"retracked.slp\")"
+      ],
+      "metadata": {
+        "id": "D3YMi3C0C0uh"
+      },
+      "execution_count": 8,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/docs/notebooks/index.rst b/docs/notebooks/index.rst
index bb8cfa11c..1e9784d18 100644
--- a/docs/notebooks/index.rst
+++ b/docs/notebooks/index.rst
@@ -5,29 +5,60 @@ Notebooks
 
 Here are Jupyter notebooks you can run to try SLEAP on `Google Colaboratory `_ (Colab). Colab is great for running training and inference on your data if you don't have access to a local machine with a supported GPU.
 
+Basic usage
+------------
+
 `Training and inference on an example dataset <./Training_and_inference_on_an_example_dataset.html>`_
--------------------------------------------------------------------------------------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In this notebook we'll show you how to install SLEAP on Colab, download a dataset from the `repository of sample datasets `_, run training and inference on that dataset using the SLEAP command-line interface, and then download the predictions.
 This notebook can be a good place to start since you'll be able to see how training and inference work without any of your own data and without having to edit anything in the notebook to get it to run correctly.
 
 
 `Training and inference on your own data using Google Drive <./Training_and_inference_using_Google_Drive.html>`_
------------------------------------------------------------------------------------------------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Once you're ready to run training and inference on your own SLEAP dataset, this notebook walks you through the process of using `Google Drive `_ to copy data to and from Colab (as well as running training and inference on your dataset).
 
 
-`Model evaluation <./Model_evaluation.html>`_
-------------------------------------------------
+`Analysis examples <./Analysis_examples.html>`_
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-After you've trained several models, you may want to compute some metrics for benchmarking and comparisons. This notebook walks through some of the types of metrics that SLEAP can compute for you, as well as how to recompute them.
+Once you've used SLEAP to successfully estimate animal pose and track animals in your videos, you'll want to use the resulting data. This notebook walks you through some analysis examples which illustrate how to read and interpret the data in the analysis HDF5 files which you can export from SLEAP.
 
+Advanced SLEAPing
+------------------
 
-`Analysis examples <./Analysis_examples.html>`_
-------------------------------------------------
+`Data structures <./Data_structures.html>`_
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Once you've used SLEAP to successfully estimate animal pose and track animals in your videos, you'll want to use the resulting data. This notebook walks you through some analysis examples which illustrate how to read and interpret the data in the analysis HDF5 files which you can export from SLEAP.
+SLEAP uses a set of core data structures that contain labels, predictions and other metadata. In this notebook we show you how to use them to develop custom analysis scripts and applications.
+
+We demonstrate how to work with these data structures by interactively generating predictions from a trained model.
+
+
+`Post-inference tracking <./Post_inference_tracking.html>`_
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In this notebook, we show how to use the tracking functionality within SLEAP to re-track existing predictions. This is useful when experimenting with new ID tracking parameters without having to re-run pose estimation.
+
+
+`Interactive and resumable training <./Interactive_and_resumable_training.html>`_
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Training in SLEAP can be done via the GUI, CLI or interactively in Python. Here we show how to use SLEAP's Python API to enable customizable training workflows, including resumable training for initialization from existing models.
+
+
+`Interactive and realtime inference <./Interactive_and_realtime_inference.html>`_
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Once you have trained models, you can run inference via the GUI, CLI or interactively in Python. Here we show how to load trained models, use them to predict on new frames, and implement a basic version of a realtime SLEAP tracker for closed-loop applications.
+
+
+`Model evaluation <./Model_evaluation.html>`_
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+After you've trained several models, you may want to compute some metrics for benchmarking and comparisons. This notebook walks through some of the types of metrics that SLEAP can compute for you, as well as how to recompute them.
 
 
 .. toctree::
@@ -36,4 +67,8 @@ Once you've used SLEAP to successfully estimate animal pose and track animals in
     Training_and_inference_on_an_example_dataset
     Training_and_inference_using_Google_Drive
     Model_evaluation
-    Analysis_examples
\ No newline at end of file
+    Analysis_examples
+    Data_structures
+    Post_inference_tracking
+    Interactive_and_resumable_training
+    Interactive_and_realtime_inference
\ No newline at end of file
diff --git a/docs/tutorials/new-project.md b/docs/tutorials/new-project.md
new file mode 100644
index 000000000..5e145fb70
--- /dev/null
+++ b/docs/tutorials/new-project.md
@@ -0,0 +1,81 @@
+---
+substitutions:
+  image0: |-
+    ```{image} ../_static/add-video.gif
+    ```
+  image1: |-
+    ```{image} ../_static/video-options.gif
+    ```
+  image2: |-
+    ```{image} ../_static/add-skeleton.gif
+    ```
+---
+
+(new-project)=
+
+# Creating a project
+
+## Starting SLEAP
+
+If you haven't installed SLEAP yet, see [](../installation) for instructions.
+
+Once you have SLEAP installed, start by opening a terminal. If you installed via the recommended [](../installation.md#conda-package) method, activate the environment with:
+
+```
+conda activate sleap
+```
+
+````{hint}
+To open a terminal:
+
+**Windows:** Open the *Start menu* and search for the *Anaconda Command Prompt* (if using Miniconda) or the *Command Prompt* if not.
+```{note}
+On Windows, our personal preference is to use alternative terminal apps like [Cmder](https://cmder.net) or [Windows Terminal](https://aka.ms/terminal).
+```
+
+**Linux:** Launch a new terminal by pressing Ctrl + Alt + T.
+
+**Mac:** Launch a new terminal by pressing Cmd + Space and searching for *Terminal*.
+````
+
+To launch the GUI, simply enter in the terminal:
+```
+sleap-label
+```
+
+When you first start SLEAP you’ll see a new, empty project.
+
+## Opening a video
+
+Add a video by clicking the “**Add Video**” button in the “**Videos**” panel
+on the right side of the main window, or by dragging-and-dropping your video file from its
+folder into the SLEAP GUI.
+
+{{ image0 }}
+
+You’ll then be able to select one or more video files and click “**Open**”.
+SLEAP currently supports mp4, avi, and h5 files. For mp4 and avi files,
+you’ll be asked whether to import the video as grayscale. For h5 files,
+you’ll be asked the dataset and whether the video is stored with
+channels first or last.
+
+{{ image1 }}
+
+(new-skeleton)=
+
+## Creating a Skeleton
+
+Create a new **skeleton** using the “Skeleton” panel on the right side
+of the main window.
+
+Use the “**New Node**” button to add a node (i.e., joint or body part).
+Double-click the node name to rename it (hit enter after you type the
+new name). Repeat until you have created all your nodes. You then need
+to connect the nodes with edges. Directly to the left of the “Add edge”
+button you’ll see two drop-down menus. Use these to select a pair of
+nodes, and then click “**Add Edge**”. Repeat until you’ve entered all the
+edges.
+
+{{ image2 }}
+
+Continue to {ref}`initial-labeling`.
diff --git a/docs/tutorials/new-project.rst b/docs/tutorials/new-project.rst
deleted file mode 100644
index 3a2a0f055..000000000
--- a/docs/tutorials/new-project.rst
+++ /dev/null
@@ -1,46 +0,0 @@
-.. _new-project:
-
-Creating a project
----------------------------
-
-When you first start SLEAP you’ll see a new, empty project.
-
-Opening a video
-~~~~~~~~~~~~~~~
-
-Add a **video** by clicking the “**Add Video**” button in the “Videos” panel
-on the right side of the main window.
-
-|image0|
-
-You’ll then be able to select one or more video files and click “**Open**”.
-SLEAP currently supports mp4, avi, and h5 files. For mp4 and avi files,
-you’ll be asked whether to import the video as grayscale. For h5 files,
-you’ll be asked the dataset and whether the video is stored with
-channels first or last.
-
-|image1|
-
-.. _new-skeleton:
-
-Creating a Skeleton
-~~~~~~~~~~~~~~~~~~~~~
-
-Create a new **skeleton** using the “Skeleton” panel on the right side
-of the main window.
-
-Use the “**New Node**” button to add a node (i.e., joint or body part).
-Double-click the node name to rename it (hit enter after you type the
-new name). Repeat until you have created all your nodes. You then need
-to connect the nodes with edges. Directly to the left of the “Add edge”
-button you’ll see two drop-down menus. Use these to select a pair of
-nodes, and then click “**Add Edge**”. Repeat until you’ve entered all the
-edges.
-
-|image2|
-
-Continue to :ref:`initial-labeling`.
-
-.. |image0| image:: ../_static/add-video.gif
-.. |image1| image:: ../_static/video-options.gif
-.. |image2| image:: ../_static/add-skeleton.gif
\ No newline at end of file