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gauravkaul/DAMM_mouse_detection

Datasets used to create the Detect Any Mouse Model (or DAMM for short ) DAMM is a project focused on the detection and tracking of multiple animals within complex social and environmental settings. The goal of this project was to create mouse instance segmentation systems that generalize across cage and experimental setups. This README provides an overview of the datasets used in DAMM and the annotation structure. Annotation Structure Annotations in this project… See the full description on the dataset page: https://huggingface.co/datasets/gauravkaul/DAMM_mouse_detection.

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Datasets used to create the Detect Any Mouse Model (or DAMM for short )

DAMM is a project focused on the detection and tracking of multiple animals within complex social and environmental settings. The goal of this project was to create mouse instance segmentation systems that generalize across cage and experimental setups. This README provides an overview of the datasets used in DAMM and the annotation structure.

Annotation Structure

Annotations in this project are structured using a JSON format compatible with COCO-style annotations. Each annotation corresponds to an object in an image and includes the following fields:

—file_name: The name of the image file, including its path.
—height: The height of the image in pixels.
—width: The width of the image in pixels.
—image_id: An identifier for the image.
—annotations: A list of annotations, where each annotation (dictionary) contains the following fields:
—bbox: A bounding box representing the object's location in the image. Expressed as [[x1, y1], [x2, y2]], where (x1, y1) is the top-left corner and (x2, y2) is the bottom-right corner.
—bbox_mode: The format of the bounding box coordinates, specified as BoxMode.XYXY_ABS for absolute coordinates in XYXY format.
—category_id: The category identifier for the object.
—segmentation: A list of closed polygon regions representing the object's mask. Each region is represented as [x1, y1, x2, y2, ..., xn, yn].

Detection Datasets

Detection datasets used in DAMM are stored in the detection_datasets/ directory (unzip detection_datasets.zip). The following tables list the number of examples for each split and their associated folder location. Each folder contains a metadata.json file containing the annotations.

AER Pretraining Dataset

Dataset Name	Number of Examples	Folder Location
AER pretraining	2209	`AER_pretraining_set/`

AER Lab Sourced (In-house) Datasets

Dataset Name	Number of Examples	Folder Location
playground	103	`playground/`
lightdarknest	104	`light_dark_nest/`
perfect_setup	102	`perfect_setup/`
colorfulmultimice	102	`colorful_multi_mice/`
blackmultimice	101	`black_multi_mice/`

Web Sourced (Publicly Available) Datasets

Dataset Name	Number of Examples	Folder Location
goldenopenfield	101	`golden_open_field/`
mars_multi	104	`mars_multi/`
mcdannaldratoperant_chamber	106	`mcdannald_rat_operant_chamber/`
trimice_dlc	101	`trimice_dlc/`
pennington_maze	102	`pennington_maze/`
goldenhomecage_color	100	`golden_home_cage_color/`
goldenhomecage_greyscale	101	`golden_home_cage_greyscale/`

Challenge Datasets (Located within the `challenge_setups/` folder)

Dataset Name	Number of Examples	Folder Location
topblackDSLR	71	`top_black_DSLR/`
ratcagebrownDSLR	73	`ratcage_brown_DSLR/`
ratcagewhiteactioncam	74	`ratcage_white_actioncam/`
ratcageblackactioncam	74	`ratcage_black_actioncam/`
gratebrownactioncam	73	`grate_brown_actioncam/`
gratewhiteactioncam	71	`grate_white_actioncam/`
topbrownDSLR	71	`top_brown_DSLR/`
ratcageblackDSLR	75	`ratcage_black_DSLR/`
gratewhiteDSLR	71	`grate_white_DSLR/`
ratcagebrownactioncam	75	`ratcage_brown_actioncam/`
grateblackactioncam	71	`grate_black_actioncam/`
gratebrownDSLR	75	`grate_brown_DSLR/`
topwhiteDSLR	100	`top_white_DSLR/`
topbrownactioncam	72	`top_brown_actioncam/`
grateblackDSLR	71	`grate_black_DSLR/`
topwhiteactioncam	70	`top_white_actioncam/`
topblackactioncam	71	`top_black_actioncam/`
ratcagewhiteDSLR	71	`ratcage_white_DSLR/`

Tracking Datasets

Tracking datasets used in DAMM are stored in the tracking_datasets/ directory (unzip tracking_datasets.zip). Each folder contains tracking data similarly structured to detection data, with the additional key of frame_id for each annotation mapping to the video frame number.

Single Animal Datasets (Located within the `single_animal_tracking/` folder)

Dataset Name	Frames Annotated	FPS	Duration (seconds)
`oft_12/oft_12.mp4`	1084	25.0	43.36
`mcdannald_rat_operant_chamber_conditioning_02/mcdannald_rat_operant_chamber_conditioning_02.mp4`	828	14.80694563265613	56.730133333333335
`golden_mouse_open_field_social_familiarity_cd1_absent_01/golden_mouse_open_field_social_familiarity_cd1_absent_01.mp4`	1387	30.0	46.233333333333334
`epm_1/epm_1.mp4`	2410	24.99810498811716	157.691953125
`single_spinach/single_spinach.mp4`	1531	30.0	51.03333333333333
`smear_mouse_operant_chamber_olfactory_search_raw_01/smear_mouse_operant_chamber_olfactory_search_raw_01.mp4`	145	10.0	14.5
`pennington_mouse_operant_chamber_exploration_02/pennington_mouse_operant_chamber_exploration_02.mp4`	1514	25.0	60.56

Multi-Animal Datasets (Located within the `multi_animal_tracking/` folder)

Dataset Name	Frames Annotated	FPS	Duration (seconds)
`multi_ir_lighting/multi_ir_lighting.mp4`	365	9.912283083942102	36.823
`golden_mouse_operant_chamber_social_self_admin_09/golden_mouse_operant_chamber_social_self_admin_09.mp4`	437	30.0	52.166666666666664
`mouse024_task1_annotator1/mouse024_task1_annotator1.mp4`	1965	30.0	65.5
`golden_mouse_home_cage_social_72/golden_mouse_home_cage_social_72.mp4`	1470	30.0	49.0
`top_white_iphone_3/top_white_iphone_3.mp4`	174	29.97	75.00834167500834

This format maintains clarity while indicating the folder locations for each dataset category.

Usage and Citation

If you use this dataset, please make sure to cite the following article:

bibtex

@article{kaul2024damm,
  author    = {Gaurav Kaul and Jonathan McDevitt and Justin Johnson and Ada Eban-Rothschild},
  title     = {DAMM for the detection and tracking of multiple animals within complex social and environmental settings},
  journal   = {bioRxiv},
  year      = {2024}
}

license: cc-by-4.0