| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - biology |
| - animal-behavior |
| - animal-tracking |
| - pose-estimation |
| - mosaic |
| pretty_name: "CalMS21 (subset): pose-model training and TREx tracking" |
| size_categories: |
| - 10K<n<100K |
| --- |
| # CalMS21 — YOLO pose-model training and TREx tracking example |
|
|
| A [mosaic](https://github.com/EcodylicScience/mosaic) dataset for a complete tracking |
| workflow: published keypoints become pose pseudo-annotations, those train a YOLO pose |
| model, and TREx tracks with that model as its detector, with visual identification on. |
|
|
| Worked end to end in |
| [`notebooks/calms21-pose-training-and-tracking.ipynb`](https://github.com/EcodylicScience/mosaic/blob/main/notebooks/calms21-pose-training-and-tracking.ipynb). |
|
|
| ## Contents |
|
|
| | File | Size | |
| | --- | --- | |
| | `calms21-pose.tar.gz` | 1.94 GB | |
|
|
| It unpacks to `calms21-pose/`, a mosaic dataset: |
|
|
| | Root | What | |
| | --- | --- | |
| | `media_raw/` | 3 re-encoded AV1 recordings from CalMS21 task 1 (21,364 / 19,343 / 13,222 frames) | |
| | `media/frames/` | 80 k-means-selected PNG frames per recording (240 total) | |
| | `tracks_raw/` | the CalMS21 task-1 arrays for those three sequences | |
| | `dataset.yaml` | the manifest | |
|
|
| ```bash |
| tar xzf calms21-pose.tar.gz |
| ``` |
|
|
| Then point the notebook's `LOCAL_DATASET` at `calms21-pose/`, or let it download this |
| archive itself. |
|
|
| **`tracks/` is not included, on purpose.** It is regenerable from `tracks_raw/` in one |
| call, and rebuilding it is the notebook's first step. The same goes for the YOLO training |
| tree: it is a rearrangement of the frames and the tables — the same pixels under different |
| filenames — so shipping it would enlarge the download to carry nothing new. |
|
|
| ## No model weights |
|
|
| None are included. The trainer, Ultralytics, is AGPL-3.0; rather than work through what |
| distributing a model produced by it requires, this dataset carries none. The notebook |
| trains one, or takes a `best.pt` you already have. |
|
|
| ## What the tracks are, and are not |
|
|
| The shipped keypoints are MARS-derived and were corrected. Training a *new* pose model on |
| them is bootstrapping, not annotation — the labels are as good as MARS was, no better. |
| That is the point of the example rather than a caveat to it: the training path runs end to |
| end without anyone labelling a frame. |
|
|
| The tracks the notebook then produces with TREx are **not** as good as the ones it started |
| from. With default parameters TREx tends to drop a tracklet when the two mice come close, |
| which is exactly the moment a resident-intruder assay is about. Getting tracks worth |
| analysing needs parameter work and a better model than 240 bootstrapped frames can support. |
| The example demonstrates that the path runs, not that the result is usable. |
|
|
| ## Provenance and licence |
|
|
| Resident-intruder assays: a black resident and a white intruder in a home cage, filmed from |
| above at 30 fps. Seven MARS keypoints per mouse (nose, ears, neck, hips, tail base). |
|
|
| Derived from the **Caltech Mouse Social Interactions (CalMS21) Dataset**, v1.0, task 1, |
| train split — three sequences of the 70 it contains. Source at CaltechDATA, |
| [doi:10.22002/D1.1991](https://doi.org/10.22002/D1.1991), under |
| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Two files of that record are |
| upstream of this one: |
|
|
| | CalMS21 file | What it became here | |
| | --- | --- | |
| | `task1_videos_mp4.zip` (28.3 GB) | `media_raw/` — three clips, re-encoded to AV1 for frame-accurate reading | |
| | `task1_classic_classification.zip` (457.4 MB) | `tracks_raw/` — the trajectories, converted to `.npy` with the record's own `calms21_convert_to_npy.py` and trimmed to these three sequences | |
|
|
| Both are derivatives and carry the same CC BY 4.0 terms, which means **attribution travels |
| with them**: if you use this dataset, or anything built from it, cite the work below. |
|
|
| **Dataset:** |
|
|
| > Sun JJ, Karigo T, Anderson DJ, Perona P, Yue Y, Kennedy A (2021) *Caltech Mouse Social |
| > Interactions (CalMS21) Dataset* (Version 1.0) [Dataset]. CaltechDATA. |
| > [doi:10.22002/D1.1991](https://doi.org/10.22002/D1.1991) |
|
|
| **Paper:** |
|
|
| > Sun JJ, Karigo T, Chakraborty D, Mohanty SP, Wild B, Sun Q, Chen C, Anderson DJ, |
| > Perona P, Yue Y, Kennedy A (2021) The Multi-Agent Behavior Dataset: Mouse Dyadic Social |
| > Interactions. *NeurIPS Datasets and Benchmarks*. |
| > [arXiv:2104.02710](https://arxiv.org/abs/2104.02710) |
|
|
| ```bibtex |
| @article{calms21, |
| title = {The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions}, |
| author = {Sun, Jennifer J and Karigo, Tomomi and Chakraborty, Dipam and |
| Mohanty, Sharada P and Wild, Benjamin and Sun, Quan and Chen, Chen and |
| Anderson, David J and Perona, Pietro and Yue, Yisong and Kennedy, Ann}, |
| journal = {arXiv preprint arXiv:2104.02710}, |
| year = {2021} |
| } |
| ``` |
|
|
| **The keypoints come from MARS**, and this dataset is only as good as that pipeline was: |
|
|
| > Segalin C, Williams J, Karigo T, Hui M, Zelikowsky M, Sun JJ, Perona P, Anderson DJ, |
| > Kennedy A (2021) The Mouse Action Recognition System (MARS) software pipeline for |
| > automated analysis of social behaviors in mice. *eLife* 10: e63720. |
| > [doi:10.7554/eLife.63720](https://doi.org/10.7554/eLife.63720) |
|
|
| More on CalMS21: <https://sites.google.com/view/computational-behavior/our-datasets/calms21-dataset> |
|
|
