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  • Home - LILA BC
    Home LILA BC is a repository for data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for biology and conservation Machine learning depends on labeled data, but accessing such data in biology and conservation is a challenge
  • Data Sets - LILA BC
    There are lots of labeled data sets relevant to conservation that are not on LILA, of course, and rather than copying them all to LILA, this pagetracks other data sets we know about
  • Accessing images outside of giant zipfiles - LILA BC
    Table of contents What’s wrong with giant zipfiles? Downloading a whole data set (without the giant zipfiles) Downloading a folder from a data set Downloading a list of files from a data set Downloading with a GUI (using Azure Storage Explorer) Mounting a container with rclone What’s wrong with giant zipfiles? Many of our data
  • List of other conservation data sets - LILA BC
    There are lots of labeled data sets relevant to conservation that are not on LILA, of course, and rather than copying them all to LILA, this pagetracks other data sets we know about This page complements the list of LILA datasets; the union of these two list is every conservation-related labeled dataset we’re aware of A few boundaries we draw around this list: This page is intended to
  • Caltech Camera Traps - LILA BC
    This data set contains 244,497 images from 140 camera locations in the Southwestern United States, with species-level labels for 22 species, and approximately 66,000 bounding box annotations
  • Frequently Asked Questions - LILA BC
    On AWS, LILA data is stored in the us-west-2 AWS region If you are going to set up an AWS compute environment to work with this data, we recommend placing it in the us-west-2 region On Azure, LILA data is stored in the South Central US Azure region
  • Snapshot Serengeti - LILA BC
    Overview This data set contains approximately 2 65M sequences of camera trap images, totaling 7 1M images, from seasons one through eleven of the Snapshot Serengeti project, the flagship project of the Snapshot Safari network Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa,
  • ENA24-detection - LILA BC
    Overview This data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image The most common classes are “American Crow”, “American Black Bear”, and “Dog” Citation, license, and contact information This data set is released under the Community Data License Agreement (permissive variant) The data
  • WCS Camera Traps - LILA BC
    Overview This data set contains approximately 1 4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly Data were provided by the Wildlife Conservation Society The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle) A
  • Community Fish Detection Dataset - LILA BC - lila. science
    Overview This dataset is a curated compilation of fish imagery and annotations from a variety of publicly available datasets, with a focus on object detection It includes >1 9M images frames and >935,000 bounding boxes from around 17 individual datasets, spanning freshwater, marine, and lab-based captures The original datasets include bounding box annotations and metadata in a





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