The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Paper • 2104.02710 • Published
Datasets:
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 78, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 54, in _get_pipeline_from_tar
current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 316, in npy_loads
return numpy.lib.format.read_array(stream, allow_pickle=False)
~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/numpy/lib/_format_impl.py", line 833, in read_array
raise ValueError("Object arrays cannot be loaded when "
"allow_pickle=False")
ValueError: Object arrays cannot be loaded when allow_pickle=False
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CalMS21 — YOLO pose-model training and TREx tracking example
A 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.
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 |
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, under CC 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
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
@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
More on CalMS21: https://sites.google.com/view/computational-behavior/our-datasets/calms21-dataset
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