mlsanityData to 7 Oct 2026

The iWildCam 2020 Competition Dataset

arXiv 2004.10340 ↗First posted 21 Apr 2020cs.CVcited by 86

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Abstract (arXiv)

Camera traps enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor animal populations. We have recently been making strides towards automatic species classification in camera trap images. However, as we try to expand the geographic scope of these models we are faced with an interesting question: how do we train models that perform well on new (unseen during training) camera trap locations? Can we leverage data from other modalities, such as citizen science data and remote sensing data? In order to tackle this problem, we have prepared a challenge where the training data and test data are from different cameras spread across the globe. For each camera, we provide a series of remote sensing imagery that is tied to the location of the camera. We also provide citizen science imagery from the set of species seen in our data. The challenge is to correctly classify species in the test camera traps.

Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2020.

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Citing papers per year: 2021: 14; 2022: 24; 2023: 19; 2024: 16; 2025: 10; 2026: 3.

86 papers.

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  1. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  2. 2021Domain Generalization: A Surveycited by 760
  3. 2023DataComp: In search of the next generation of multimodal datasetscited by 635
  4. 2022Exploring Visual Prompts for Adapting Large-Scale Modelscited by 271
  5. 2021Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalizationcited by 248
  6. 2023BioCLIP: A Vision Foundation Model for the Tree of Lifecited by 159
  7. 2022Finetune like you pretrain: Improved finetuning of zero-shot vision modelscited by 149
  8. 2024Scalable Pre-training of Large Autoregressive Image Modelscited by 115
  9. 2022Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shiftcited by 94
  10. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
  11. 2022Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervisioncited by 80
  12. 2021The iWildCam 2021 Competition Datasetcited by 77
38 more
  1. 2022A Broad Study of Pre-training for Domain Generalization and Adaptationcited by 64
  2. 2021Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensemblescited by 54
  3. 2021SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learningcited by 53
  4. 2022Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Expertscited by 50
  5. 2022Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalizationcited by 38
  6. 2023Diagnosing Model Performance Under Distribution Shiftcited by 37
  7. 2023Understanding and Improving Feature Learning for Out-of-Distribution Generalizationcited by 35
  8. 2022Calibrated ensembles can mitigate accuracy tradeoffs under distribution shiftcited by 34
  9. 2021INTERN: A New Learning Paradigm Towards General Visioncited by 33
  10. 2022Active Learning Helps Pretrained Models Learn the Intended Taskcited by 33
  11. 2023On the Connection between Pre-training Data Diversity and Fine-tuning Robustnesscited by 25
  12. 2024Fine-Tuning is Fine, if Calibratedcited by 24
  13. 2021Species Distribution Modeling for Machine Learning Practitioners: A Reviewcited by 21
  14. 2023Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shiftcited by 20
  15. 2024ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shiftscited by 16
  16. 2024Domain Generalisation via Imprecise Learningcited by 14
  17. 2024A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimationcited by 13
  18. 2024Selective Classification Under Distribution Shiftscited by 12
  19. 2022An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentationcited by 11
  20. 2023Bias in Pruned Vision Models: In-Depth Analysis and Countermeasurescited by 11
  21. 2025Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbationscited by 11
  22. 2021ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identificationcited by 8
  23. 2023Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issuescited by 8
  24. 2025When to retrain a machine learning modelcited by 8
  25. 2022Active Learning with Expected Error Reductioncited by 7
  26. 2022Stochastic Optimization for Spectral Risk Measurescited by 7
  27. 2023Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustnesscited by 7
  28. 2023Robust Fine-Tuning of Vision-Language Models for Domain Generalizationcited by 7
  29. 2024Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentationscited by 7
  30. 2023Distributionally Robust Optimization with Bias and Variance Reductioncited by 6
  31. 2024Adapting Prediction Sets to Distribution Shifts Without Labelscited by 6
  32. 2025Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretationcited by 6
  33. 2024Improving Generalization via Meta-Learning on Hard Samplescited by 5
  34. 2024Evaluating Model Performance Under Worst-case Subpopulationscited by 5
  35. 2025Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalizationcited by 5
  36. 2021Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographscited by 4
  37. 2022(Im)possibility of Collective Intelligencecited by 4
  38. 2022Minimax Optimal Estimation of Stability Under Distribution Shiftcited by 4

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