Multispecies Animal Re-ID Using a Large Community-Curated Dataset

• URL: http://arxiv.org/abs/2412.05602v1
• Date: Sat, 07 Dec 2024 09:56:33 GMT
• Title: Multispecies Animal Re-ID Using a Large Community-Curated Dataset
• Authors: Lasha Otarashvili, Tamilselvan Subramanian, Jason Holmberg, J. J. Levenson, Charles V. Stewart,
• Abstract summary: We construct a dataset that includes 49 species, 37K individual animals, and 225K images, using this data to train a single embedding network for all species. Our model consistently outperforms models trained separately on each species, achieving an average gain of 12.5% in top-1 accuracy. The model is already in production use for 60+ species in a large-scale wildlife monitoring system.
• Score: 0.19418036471925312
• License:
• Abstract: Recent work has established the ecological importance of developing algorithms for identifying animals individually from images. Typically, a separate algorithm is trained for each species, a natural step but one that creates significant barriers to wide-spread use: (1) each effort is expensive, requiring data collection, data curation, and model training, deployment, and maintenance, (2) there is little training data for many species, and (3) commonalities in appearance across species are not exploited. We propose an alternative approach focused on training multi-species individual identification (re-id) models. We construct a dataset that includes 49 species, 37K individual animals, and 225K images, using this data to train a single embedding network for all species. Our model employs an EfficientNetV2 backbone and a sub-center ArcFace loss function with dynamic margins. We evaluate the performance of this multispecies model in several ways. Most notably, we demonstrate that it consistently outperforms models trained separately on each species, achieving an average gain of 12.5% in top-1 accuracy. Furthermore, the model demonstrates strong zero-shot performance and fine-tuning capabilities for new species with limited training data, enabling effective curation of new species through both incremental addition of data to the training set and fine-tuning without the original data. Additionally, our model surpasses the recent MegaDescriptor on unseen species, averaging an 19.2% top-1 improvement per species and showing gains across all 33 species tested. The fully-featured code repository is publicly available on GitHub, and the feature extractor model can be accessed on HuggingFace for seamless integration with wildlife re-identification pipelines. The model is already in production use for 60+ species in a large-scale wildlife monitoring system.

Related papers

• CKSP: Cross-species Knowledge Sharing and Preserving for Universal Animal Activity Recognition [7.012560255346829]
  We propose a one-for-many framework, dubbed Cross-species Knowledge Sharing and Preserving (CKSP), based on sensor data of diverse animal species. We employ a shared full-rank convolutional layer to learn generic features, enabling the CKSP framework to learn inter-species complementarity. Considering the training conflict arising from discrepancies in data distributions among species, we devise a Species-specific Batch Normalization (SBN) module.
  arXiv  Detail & Related papers  (2024-10-22T02:44:10Z)
• Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition [0.19183348587701113]
  Transferring the weights of a pre-trained model to assist another task has become a crucial part of modern deep learning. Our experiments will demonstrate the usefulness of in-domain models and datasets for bird species recognition.
  arXiv  Detail & Related papers  (2024-04-26T08:47:28Z)
• Learning the 3D Fauna of the Web [70.01196719128912]
  We develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data. We show that prior category-specific attempts fail to generalize to rare species with limited training images.
  arXiv  Detail & Related papers  (2024-01-04T18:32:48Z)
• Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition [30.327642724046903]
  Species196 is a large-scale semi-supervised dataset of 196-category invasive species. It collects over 19K images with expert-level accurate annotations Species196-L, and 1.2M unlabeled images of invasive species Species196-U.
  arXiv  Detail & Related papers  (2023-09-25T14:46:01Z)
• On the Connection between Pre-training Data Diversity and Fine-tuning Robustness [66.30369048726145]
  We find that the primary factor influencing downstream effective robustness is data quantity. We demonstrate our findings on pre-training distributions drawn from various natural and synthetic data sources.
  arXiv  Detail & Related papers  (2023-07-24T05:36:19Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• DINOv2: Learning Robust Visual Features without Supervision [75.42921276202522]
  This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature.
  arXiv  Detail & Related papers  (2023-04-14T15:12:19Z)
• Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 2022 [30.8004334312293]
  We introduce our solution in SnakeCLEF 2022 for fine-grained snake species recognition on a heavy long-tailed class distribution. With an ensemble of several different models, a private score 82.65%, ranking the 3rd, is achieved on the final leaderboard.
  arXiv  Detail & Related papers  (2022-07-04T05:55:58Z)
• SuperAnimal pretrained pose estimation models for behavioral analysis [42.206265576708255]
  Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. We present a series of technical innovations that enable a new method, collectively called SuperAnimal, to develop unified foundation models.
  arXiv  Detail & Related papers  (2022-03-14T18:46:57Z)
• MSeg: A Composite Dataset for Multi-domain Semantic Segmentation [100.17755160696939]
  We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. We reconcile the generalization and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. A model trained on MSeg ranks first on the WildDash-v1 leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.
  arXiv  Detail & Related papers  (2021-12-27T16:16:35Z)
• Transferring Dense Pose to Proximal Animal Classes [83.84439508978126]
  We show that it is possible to transfer the knowledge existing in dense pose recognition for humans, as well as in more general object detectors and segmenters, to the problem of dense pose recognition in other classes. We do this by establishing a DensePose model for the new animal which is also geometrically aligned to humans. We also introduce two benchmark datasets labelled in the manner of DensePose for the class chimpanzee and use them to evaluate our approach.
  arXiv  Detail & Related papers  (2020-02-28T21:43:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.