WCS Camera Traps

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 complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.

Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files.

Contact information

This data set is released under the Community Data License Agreement (permissive variant).

Data format

Annotations are provided in the COCO Camera Traps .json format used for most data sets on lila.science.

For information about mapping this dataset’s categories to a common taxonomy, see this page.

Accessing the data

Class-level annotations are available here:

Bounding box annotations are available here:

Recommended train/val/test splits are available here:

Images are available in the following Azure blob container:

So, for example, the image referred to in the metadata file as:

…is available at:

Images are also available in the following Google Cloud Storage folder:

The full data set is several hundred GB, so you may not want to download all the data. You can download images programmatically, or you can download a list of images with AzCopy (for Azure) and gsutil (for GCP), as documented on our download FAQ. If you are working on Azure, use the South Central US Azure region, and consider mounting the blob container using rclone or BlobFuse. Mounting instructions are also available on our download FAQ.

Posted by Dan Morris.