EcodylicScience/mosaic-example-calms21

CalMS21 subset: tracks and behaviour labels

The CalMS21 task-1 arrays trimmed to four sequences per split, packaged as a mosaic dataset. Resident-intruder assays: a black resident and a white intruder in a home cage, filmed from above at 30 fps, with seven MARS keypoints per mouse and a per-frame behaviour annotation.

Worked example: notebooks/calms21-template.ipynb. Set SOURCE = "download" (the default) and it fetches the file below.

What is in it

File	Size	Contents
calms21-tracks-and-labels.tar.gz	12.6 MB	dataset.yaml, tracks_raw/calms21_task1_{train,test}.npy, and the two scan sources already declared

Four sequences: two from the train split and two from the test split, about 82,700 frames of two mice. That is enough for the template's whole path.

Note what four entries mean for the supervised section. ExtractLabeledTemplates splits by sequence, holding out max(1, min(n - 1, round(test_fraction * n))) whole recordings -- so at the template's default test_fraction=0.2 the test set is one recording. The notebook prints that split rather than assuming it, and says to read the classifier scores as a demonstration that the path runs rather than as a benchmark.

No video. Every feature in the template reads the track table; media is only needed for overlays and crops, and the CalMS21 clips are roughly 500 MB each. The mosaic-example-calms21-pose dataset ships three of them if you want them.

One file, two roots

calms21_npy is registered in mosaic as both a track converter and a label converter, so the same .npy feeds tracks/ and labels/ with no copy. The manifest declares both sources already; source overlap is only checked within a kind.

Note multi_sequences_per_file: true on each. One CalMS21 file holds many sequences, and only that flag leaves the raw index's sequence cell blank, which is what triggers the converter's enumerate_sequences. Without it the whole file collapses into one entry named after the stem, and nothing fails.

Use

from mosaic.core.dataset import open_dataset

ds = open_dataset("calms21")
ds.scan_tracks()
ds.convert_all_tracks(group_from="filename")
ds.scan_labels()
ds.convert_all_labels(kind="behavior", source_format="calms21_npy")

table = ds.load_tracks(*ds.list_sequences()[0])

Provenance and licence

Derived from the Caltech Mouse Social Interactions (CalMS21) Dataset v1.0 at CaltechDATA, doi:10.22002/D1.1991, under CC BY 4.0. The trajectories come from task1_classic_classification.zip, converted with that record's own calms21_convert_to_npy.py and then trimmed to eight sequences. This derivative carries the same licence, and attribution travels with it: if you use this, or anything built from it, cite the work below.

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

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

The keypoints are MARS (Segalin et al. 2021, eLife 10:e63720, doi:10.7554/eLife.63720).

Downloading

The notebook uses huggingface_hub >= 1.2.0. The floor matters: older clients retry a rate-limit response with a 25-second backoff against a 5-minute window, so they fail rather than wait.