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Perch 2.0 has 47 facts recorded in Dontopedia across 1 reference, with 8 live disagreements.
Mostly:has auc score on task(10), performance is highest on(7), tp:verdict reason(5)
outperformsOnTaskoutperformsOnCetaceanTasksoutperformshasAucScoreOnTasktp:verdictReasontp:simulationVerdicthasTrainingTaxahasModelParameterUnithasModelParameterCounthasEmbeddingDimensionOther subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. “X motherOf this subject” — and answer questions the forward facts can't. Grouped by predicate.
comparesPerformanceOfCompares Performance of(1)ex:model-comparisonlearnedDuringTrainingOfLearned During Training of(1)ex:bittern-lessonoutperformsOutperforms(1)ex:birdnet-v2.3supportsSupports(1)ex:perch-hoplite-github-repositoryunseenForModelUnseen for Model(1)ex:dclde-2026The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Has Window Size Unit | s | [1] |
| Has Window Size | 5 | [1] |
| Has Sample Rate Unit | kHz | [1] |
| Has Sample Rate | 32 | [1] |
| Evaluated on Task | Task Dclde 2026 Species K8 | [1] |
| Training Data Includes | Inaturalist Cetacean Recordings | [1] |
| Rdfs:label | Perch 2.0 | [1] |
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
doc:agent/chunk-009/f33235ee-7e4c-40ec-b809-de198012fc5fnighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. Scaling laws for neural language models. arXiv [cs.LG], Jan. 2020. E. Mercado and S. Handel. Understanding the structure of humpback whale songs (l). The Jo…
doc:agent/chunk-008/5506d265-7ff5-434b-b60e-b755c8a596d6Marine Science, 11:1394695, 2024. J. A. Allen, E. C. Garland, C. Garrigue, R. A. Dunlop, and M. J. Noad. Song complexity is maintained during inter-population cultural transmission of humpback whale songs. Scientific reports, 12(1): 8999, 2…
doc:agent/chunk-007/04710b2a-ba75-48cb-94b5-13d951854faaatasets with thousands of classes can be high performing, even on out-of-domain down- stream tasks. Next, the ‘bittern lesson’ learned when training Perch 2.0 was that bird species classification in particular is a challenging su- pervision…
doc:agent/chunk-006/44f49039-e92d-4aae-a989-a3343ce76194= 8k = 16k = 8 k = 16k = 8 k = 16 GMWM0.8900.9140.7640.8210.9360.9540.868* 0.917*0.8230.855 SurfPerch 0.9320.9470.8590.9030.9810.9840.7960.8990.982* 0.986* Perch 1.0 0.9580.9680.9010.9310.9770.9810.8360.9050.9580.970 Perch 2.0 0.9…
doc:agent/chunk-005/31b9995b-056a-4dab-a3da-ede4fabae094V2.348 kHz3.0102420.0MBirds, Frogs AVES-bio16 kHzVariable768 2 94.4MGeneral Audio BirdAVES (large)16 kHzVariable1024 3 315.4MGeneral Audio + Birds 4 Comparison models. As our goal is to provide guidance on which pretrained embedding models …
doc:agent/chunk-004/2ce1467e-29e9-40e4-a12c-ee1e34601ebcludes new classes unseen by the models. The classes used in the NOAA PIPAN evaluation set include anthropomorphic noise, unknown whale species, and the following baleen whale species: common minke whale, humpback whale, sei whale, blue whal…
doc:agent/chunk-003/05e7df2c-afdb-4b38-8576-118d1c22e948ained on log-mel spectrograms using a classification loss. Additionally, the model used a form of self-distillation and a self-supervised loss (in the form of source recording prediction) with the goal of producing strong embeddings that ar…
doc:agent/chunk-002/6ad8a5fa-2898-42fc-95e1-ea78861375f7ion as new sounds are discovered while not having large amounts of human labeled data. Despite these challenges, passive acoustic monitoring is a critical tool for marine conservation and ecology (Fleishman et al., 2023), and discoveries ab…
doc:agent/chunk-001/2b871fa0-4034-4d77-a1ce-b818711dd372Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind Abs…
doc:agent/chunk-005/84c4d25d-a6fb-4da9-95ec-773c6e223fa2monitoring. Ecol. Inform., 61(101236):101236, Mar. 2021. 6 J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. Scaling laws for neural language models. arXiv [cs.LG], Jan. 2020…
doc:agent/chunk-004/597f88dd-b871-4083-99cd-a9a4484853abe datasets with thousands of classes can be high performing, even on out-of-domain down- stream tasks. Next, the ‘bittern lesson’ learned when training Perch 2.0 was that bird species classification in particular is a challenging su- pervis…
doc:agent/chunk-003/e23b9efa-8e61-4312-a564-68c6956429b2ce on which pretrained embedding models should be used for agile modeling and transfer learning (with existing tools), we limit our comparisons to models supported in the Perch Hoplite Github repository 5 . We compare the performance of the…
doc:agent/chunk-002/f0b400dc-caae-4eca-b34a-d5598b9eddf0l of producing strong embeddings that are linearly separable for a wide range of bioacoustics tasks. Embeddings from the Perch model have shown successful generalization to tasks other than species classification (e.g., individual identific…
doc:agent/chunk-001/ae1f6e1d-0812-43e1-93c6-1e7778c77d74Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind Abs…
tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind A…
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