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 asBoxMode.XYXY_ABSfor 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/ |
| light_dark_nest | 104 | light_dark_nest/ |
| perfect_setup | 102 | perfect_setup/ |
| colorful_multi_mice | 102 | colorful_multi_mice/ |
| black_multi_mice | 101 | black_multi_mice/ |
Web Sourced (Publicly Available) Datasets
| Dataset Name | Number of Examples | Folder Location |
|---|---|---|
| golden_open_field | 101 | golden_open_field/ |
| mars_multi | 104 | mars_multi/ |
| mcdannald_rat_operant_chamber | 106 | mcdannald_rat_operant_chamber/ |
| trimice_dlc | 101 | trimice_dlc/ |
| pennington_maze | 102 | pennington_maze/ |
| golden_home_cage_color | 100 | golden_home_cage_color/ |
| golden_home_cage_greyscale | 101 | golden_home_cage_greyscale/ |
Challenge Datasets (Located within the challenge_setups/ folder)
| Dataset Name | Number of Examples | Folder Location |
|---|---|---|
| top_black_DSLR | 71 | top_black_DSLR/ |
| ratcage_brown_DSLR | 73 | ratcage_brown_DSLR/ |
| ratcage_white_actioncam | 74 | ratcage_white_actioncam/ |
| ratcage_black_actioncam | 74 | ratcage_black_actioncam/ |
| grate_brown_actioncam | 73 | grate_brown_actioncam/ |
| grate_white_actioncam | 71 | grate_white_actioncam/ |
| top_brown_DSLR | 71 | top_brown_DSLR/ |
| ratcage_black_DSLR | 75 | ratcage_black_DSLR/ |
| grate_white_DSLR | 71 | grate_white_DSLR/ |
| ratcage_brown_actioncam | 75 | ratcage_brown_actioncam/ |
| grate_black_actioncam | 71 | grate_black_actioncam/ |
| grate_brown_DSLR | 75 | grate_brown_DSLR/ |
| top_white_DSLR | 100 | top_white_DSLR/ |
| top_brown_actioncam | 72 | top_brown_actioncam/ |
| grate_black_DSLR | 71 | grate_black_DSLR/ |
| top_white_actioncam | 70 | top_white_actioncam/ |
| top_black_actioncam | 71 | top_black_actioncam/ |
| ratcage_white_DSLR | 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:
@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