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[Open Data]

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CameraTrapDetectoR Family Model

This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR family model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts animals from 33 North American taxonomic families in camera trap images, including humans vehicles and a background class. Additional information about the R package and the training data can be found in the package's Github repository: https://github.com/CameraTrapDetectoR/CameraTrapDetectoR

Resources in this dataset:

  • family_v1.zip is a folder containing the model architecture, model weights, and class label dictionary required to deploy the first generation family model in CameraTrapDetectoR

  • family_v2.zip is a folder containing the model architecture, model weights, and class label dictionary required to deploy the family model version 2 in CameraTrapDetectoR

  • family_v2_cl.zip is a folder containing the all information to deploy the family v2 model via Python script from the command line. Full instructions for set up and use may be found at https://github.com/CameraTrapDetectoR/model_training

See all >>
FieldValue
Tags
computer vision
animal behavior
deep learning
camera trapping
machine learning
classification
CameraTrapDetectoR
NP215
Modified
2023-10-12
Release Date
2023-05-09
Frequency
Not Planned
Identifier
c0e2890f-bdc2-43a9-bdd2-c11328147a39
Spatial / Geographical Coverage Area
POLYGON ((-161.89453125 70.470124401839, -161.89453125 57.474889007664, -144.66796875 60.549536115658, -103.53515625 10.758479494301, -55.01953125 46.747889039741, -87.36328125 69.009872311641))
Publisher
Ag Data Commons
Spatial / Geographical Coverage Location
North America
Temporal Coverage
January 1, 2022
License
GNU General Public License
Contact Name
Burns, Amira
Contact Email
Amira.Burns@usda.gov
Public Access Level
Public
Program Code
005:037 - Department of Agriculture - Research and Education
Bureau Code
005:32 - Animal and Plant Health Inspection Service
Extended Metadata: 
FieldValue
Authors
Burns, Amira L. (ORCID)
Tabak, Michael (ORCID)
Falbel, Daniel
Hamzeh, Tess
Brook, Ryan K. (ORCID)
Goolsby, John A. (Scopus Author ID)
Zoromski, Lisa D. (ORCID)
Boughton, Raoul (ORCID)
Snow, Nathan (ORCID)
VerCauteren, Kurt (ORCID)
Miller, Ryan (ORCID)
Wilmer, Hailey (ORCID)
Peer Reviewed
No
Intended Use
The dataset supports the R package CameraTrapDetectoR with model architecture, model weights, and class label dictionary for the taxonomic family model version 1, launched January 2022, and version 2, launched July 2023.
Equipment or Software Used
PyTorch v1.9.1
Funding Sources
Animal and Plant Health Inspection Service
Center for Epidemiology and Animal Health
Primary Article

Tabak, M. A., Falbel, D., Hamzeh, T., Brook, R. K., Goolsby, J. A., Zoromski, L. D., Boughton, R. K., Snow, N. P., VerCauteren, K. C., & Miller, R. S. (2022). CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.02.07.479461
Dataset DOI (digital object identifier)
10.15482/USDA.ADC/1528979
Product Type
Model
Related Content
CameraTrapDetectoR GitHub repository
FieldValue
ISO Topic(s)
biota
environment
geoscientificInformation
health
State or Territory
North America
National Agricultural Library Thesaurus Term
data collection
models
cameras
species identification
ecology
animals
automation
taxonomy
wildlife management
environmental monitoring
computer vision
humans
birds
reptiles
Ursidae
prediction
user interface
Ag Data Commons Keywords
  • Agroecosystems & Environment
  • Agroecosystems & Environment
  • Plant and animal
  • Agroecosystems & Environment
  • Plant and animal
  • Species composition
  • Maps & Multimedia
  • Maps & Multimedia
  • Images (non-GIS)
  • Maps & Multimedia
  • Images (non-GIS)
  • Organisms

Suggested Citations:

(original article) Tabak, M. A., Falbel, D., Hamzeh, T., Brook, R. K., Goolsby, J. A., Zoromski, L. D., Boughton, R. K., Snow, N. P., VerCauteren, K. C., & Miller, R. S. (2022). CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.02.07.479461

(dataset) Burns, Amira L.; Tabak, Michael; Falbel, Daniel; Hamzeh, Tess; Brook, Ryan K.; Goolsby, John A.; Zoromski, Lisa D.; Boughton, Raoul; Snow, Nathan; VerCauteren, Kurt; Miller, Ryan; Wilmer, Hailey (2023). CameraTrapDetectoR Family Model. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1528979. Accessed 2023-12-07.