This repository contains all the code you'll need to train your own version of MARS.

You will first need to follow the installation instructions for the end-user version of MARS.

Once you have the end-user version installed, the only other thing you'll need to do to use MARS-Developer is install the conda environment: conda env create -f MARS_dev.yml

You can run jupyter notebooks from within the MARS_dev environment by installing ipykernel:

conda install -c anaconda ipykernel

and then calling

python -m ipykernel install --user --name=mars_dev

MARS processes your videos in three steps:

1. Detection - detects the location of animals in each video frame.
2. Pose estimation - estimates the posture of each animal in terms of a set of anatomically defined "keypoints".
3. Behavior classification - detects social behaviors of interest based on the poses of each animal.

Each of these steps can be fine-tuned to your own data using the code in this repository.

Training MARS to run on your own experiments includes the following steps. We'll assume you have already settled on a recording setup, and have a set of videos on hand to be analyzed.

Important Note: the steps below only show the training commands themselves. Open the MARS_tutorial jupyter notebook to see the complete training process, including scripts to evaluate model performance at each step!

MARS uses a set file strcture to keep track of data and models associated with your project. The script create_new_project.py will create one for you. It takes the arguments:

• location: where we're going to save all associated files for this project
• name: a name for the project

As well as optional arguments:

• Set download_MARS_checkpoints=True to download pre-trained MARS detection and pose models. If your dataset looks similar to MARS, initializing training from the pre-trained models should help decrease your training data requirements. TODO: finish unploading these
• Set download_demo_data=True to download a sample dataset, consisting of 2000 frames from videos in the CRIM13 dataset, with accompanying manual annotations of animal pose collected via Amazon SageMaker. The sample dataset can also be previewed here

Call it from terminal with: python create_new_project.py /path/to/savedir my_project

It creates a folder at /path/to/savedir/my_project that has already been populated with a few files and subdirectories. We'll get to these shortly.

We provide code for crowdsourcing of pose annotation to a public workforce via Amazon SageMaker. Running this code requires an Amazon Web Services (AWS) account and some initial time investment in setting up the custom annotation job. A typical pose annotation job, at high annotation quality + high label confidence (5 repeat annotations/image) costs ~68 cents/image.

• The MARS Pose Annotation Tools module covers the following steps:
  • Extracting video frames that you would like to annotate.
  • Running a labeling job to collect body part annotations from a human workforce.
  • Post-processing the annotations to correct for common annotation errors.
  • Visualizing some annotations to evaluate performance of your workforce.

If you've already collected pose annotations via another interface such as DeepLabCut, you can skip directly to the post-processing step to format your data for training.

After you've collected annotations, the script pose_annotation_tools.annotation_postprocessing.py will consolidate annotations across human workers, correct for left/right flips of body parts, and package your data for model training. Call it from the terminal as

python pose_annotation_tools.annotation_postprocessing.py /path/to/savedir/my_project

Next, we need to teach MARS what your animals look like. The Multibox Detection module covers training, validating, and testing your mouse detector.

To train your detection model(s) from the command line, simply call python multibox_detection/train.py /path/to/savedir/my_project This call also takes optional arguments:

• models takes names of detectors to train, in case you only want to work on one detector at a time (MARS trains all detectors sequentially by default.)
• max_training_steps overrides NUM_TRAIN_ITERATIONS in train_config.yaml (300,000 by default). You can set this to a small number to confirm everything is working before launching your full training job.

Now that you can detect your mice, we want to estimate their poses. In this step we'll train and evaluate a mouse pose estimator for your videos. The Hourglass Pose module covers training, validating, and testing a stacked hourglass model for animal pose estimation.

As for detection, training the pose model can be launched from the command line by calling python hourglass_pose/train.py /path/to/savedir/my_project With optional arguments:

• models takes names of pose models to train, in case you only want to work on one model at a time.
• max_training_steps overrides NUM_TRAIN_ITERATIONS in train_config.yaml (300,000 by default).

The module hourglass_pose/evaluation includes several scripts for evaluating performance of your pose model using MARS's fork of the COCO API. These include hourglass_pose.evaluation.plot_frame to view pose predictions for a single frame in your test set, and hourglass_pose.evaluation.plot_model_PCK to evaluate performance in terms of Percent Correct Keypoints (PCK).

Now that you have a working detector and pose estimator, we'll add them to your end-user version of MARS so you can run them on new videos!

• Step-by-step: coming soon

Once you've applied your trained pose estimator on some new behavior videos, you can annotate behaviors of interest in those videos and train MARS to detect those behaviors automatically.

• Step-by-step: coming soon