# The iWildCam 2018 Challenge Dataset

arXiv: https://arxiv.org/abs/1904.05986 · HTML: https://mlsanity.com/paper/1904.05986/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [The iWildCam 2021 Competition Dataset](https://mlsanity.com/paper/2105.03494/) (2021)
- [The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting](https://mlsanity.com/paper/2207.09295/) (2022)
- [Bugs in the Data: How ImageNet Misrepresents Biodiversity](https://mlsanity.com/paper/2208.11695/) (2022)
- [SealID: Saimaa ringed seal re-identification dataset](https://mlsanity.com/paper/2206.02260/) (2022)
- [Species Distribution Modeling for Machine Learning Practitioners: A Review](https://mlsanity.com/paper/2107.10400/) (2021)
- [Domain Generalization -- A Causal Perspective](https://mlsanity.com/paper/2209.15177/) (2022)
- [Low-power, Continuous Remote Behavioral Localization with Event Cameras](https://mlsanity.com/paper/2312.03799/) (2023)
- [ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification](https://mlsanity.com/paper/2106.15083/) (2021)
- [Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2401.00608/) (2023)
- [Domain Adaptation for Rare Classes Augmented with Synthetic Samples](https://mlsanity.com/paper/2110.12216/) (2021)
- [Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time](https://mlsanity.com/paper/2603.20509/) (2026)
- [Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras](https://mlsanity.com/paper/2410.06698/) (2024)
- [Image-to-Image Translation of Synthetic Samples for Rare Classes](https://mlsanity.com/paper/2106.12212/) (2021)
- [Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers](https://mlsanity.com/paper/2507.21364/) (2025)

## Abstract (arXiv)

Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it is imperative that we develop automatic solutions for annotating camera trap data in order to allow this research to scale. A promising approach is based on deep networks trained on human-annotated images. We provide a challenge dataset to explore whether such solutions generalize to novel locations, since systems that are trained once and may be deployed to operate automatically in new locations would be most useful.