The iWildCam 2021 Competition Dataset

By Sara Beery, Arush Agarwal, Elijah Cole, Vighnesh Birodkar. 2021

105 citations recorded by the source corpus.

Camera traps enable the automatic collection of large quantities of image data. Ecologists use camera traps to monitor animal populations all over the world. In order to estimate the abundance of a species from camera trap data, ecologists need to know not just which species were seen, but also how many individuals of each species were seen. Object detection techniques can be used to find the number of individuals in each image. However, since camera traps collect images in motion-triggered bursts, simply adding up the number of detections over all frames is likely to lead to an incorrect estimate. Overcoming these obstacles may require incorporating spatio-temporal reasoning or individual re-identification in addition to traditional species detection and classification. We have prepared a challenge where the training data and test data are from different cameras spread across the globe. The set of species seen in each camera overlap, but are not identical. The challenge is to classify species and count individual animals across sequences in the test cameras.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time (2022, background): “To further establish the generality of the model soup, we replicate the CLIP hyperparameter sweep experiment on two image classification tasks from WILDS [49], namely FMoW [18] and iWildCam [8]; Figure 6 shows results qualitatively similar to our ImageNet experiment, and Appendix B.”
Robust fine-tuning of zero-shot models (2021, methodology): “In addition to the aforementioned ImageNet distribution shifts, WiSE-FT consistently improves robustness on a diverse set of six further distribution shifts including: (i) geographic shifts in satellite imagery and wildlife recognition (WILDS-FMoW, WILDS-iWildCam) (Koh et al., 2021; Christie et al.,”
Bugs in the Data: How ImageNet Misrepresents Biodiversity (2022, unknown): “…is another notable example of extensive biodiversity datasets, leveraging camera traps to gather hundreds of thousands of images across the world (Beery et al. 2019, 2021); these datasets have been used to host competitions, establish benchmarks, and assess model generalization to novel…”