# FathomNet: A global image database for enabling artificial intelligence in the ocean

arXiv: https://arxiv.org/abs/2109.14646 · HTML: https://mlsanity.com/paper/2109.14646/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 88 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Measuring Robustness to Natural Distribution Shifts in Image Classification](https://mlsanity.com/paper/2007.00644/) (2020)
- [From ImageNet to Image Classification: Contextualizing Progress on Benchmarks](https://mlsanity.com/paper/2005.11295/) (2020)
- [A survey on Semi-, Self- and Unsupervised Learning for Image Classification](https://mlsanity.com/paper/2002.08721/) (2020)
- [A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk](https://mlsanity.com/paper/1712.05796/) (2017)
- [FathomNet: An underwater image training database for ocean exploration and discovery](https://mlsanity.com/paper/2007.00114/) (2020)

## Built on by (most cited first, 25 of 25)

- [BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning](https://mlsanity.com/paper/2505.23883/) (2025)
- [Semi-Supervised Visual Tracking of Marine Animals using Autonomous Underwater Vehicles](https://mlsanity.com/paper/2302.07344/) (2023)
- [Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images](https://mlsanity.com/paper/2407.08027/) (2024)
- [BenthicNet: A global compilation of seafloor images for deep learning applications](https://mlsanity.com/paper/2405.05241/) (2024)
- [Insect Identification in the Wild: The AMI Dataset](https://mlsanity.com/paper/2406.12452/) (2024)
- [AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis](https://mlsanity.com/paper/2502.01785/) (2025)
- [Context-Driven Detection of Invertebrate Species in Deep-Sea Video](https://mlsanity.com/paper/2206.00718/) (2022)
- [FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data](https://mlsanity.com/paper/2412.02784/) (2024)
- [AI-Driven Marine Robotics: Emerging Trends in Underwater Perception and Ecosystem Monitoring](https://mlsanity.com/paper/2509.01878/) (2025)
- [Robot Goes Fishing: Rapid, High-Resolution Biological Hotspot Mapping in Coral Reefs with Vision-Guided Autonomous Underwater Vehicles](https://mlsanity.com/paper/2305.02330/) (2023)
- [The FathomNet2023 Competition Dataset](https://mlsanity.com/paper/2307.08781/) (2023)
- [Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation](https://mlsanity.com/paper/2412.02262/) (2024)
- [MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species](https://mlsanity.com/paper/2601.03729/) (2026)
- [An Efficient Detection and Control System for Underwater Docking using Machine Learning and Realistic Simulation: A Comprehensive Approach](https://mlsanity.com/paper/2311.01522/) (2023)
- [FathomVerse: A community science dataset for ocean animal discovery](https://mlsanity.com/paper/2412.01701/) (2024)
- [PoseIDON: 6DoF Pose Estimation with Foundation Model Features for Marine Sediment Burial Mapping](https://mlsanity.com/paper/2506.10386/) (2025)
- [Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning](https://mlsanity.com/paper/2602.08986/) (2026)
- [Label-efficient underwater image classification with logistic regression on frozen foundation model embeddings](https://mlsanity.com/paper/2604.00313/) (2026)
- [ShellfishNet: A Domain-Specific Benchmark for Visual Recognition of Marine Molluscs](https://mlsanity.com/paper/2605.07338/) (2026)
- [Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery](https://mlsanity.com/paper/2606.25989/) (2026)
- [How many labels do you need? A decision framework for cross-habitat marine species recognition](https://mlsanity.com/paper/2607.02559/) (2026)
- [Decoupled Pipeline with Proposal Reranking and Score Fusion for Positive-Unlabeled Marine Species Detection](https://mlsanity.com/paper/2607.18700/) (2026)
- [WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition](https://mlsanity.com/paper/2608.21281/) (2026)
- [Weakly Supervised Seafloor Segmentation for Seagrass Habitat Mapping in Side-Scan Sonar Imagery](https://mlsanity.com/paper/2608.24756/) (2026)
- [CoralscapesV2: Panoptic and Fine-Grained Visual Scene Understanding in Coral Reefs](https://mlsanity.com/paper/2609.12826/) (2026)

## Abstract (arXiv)

The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As baselines are sought by the research community, the volume and rate of this required data collection rapidly outpaces our abilities to process and analyze them. Recent advances in machine learning enables fast, sophisticated analysis of visual data, but have had limited success in the ocean due to lack of data standardization, insufficient formatting, and demand for large, labeled datasets. To address this need, we built FathomNet, an open-source image database that standardizes and aggregates expertly curated labeled data. FathomNet has been seeded with existing iconic and non-iconic imagery of marine animals, underwater equipment, debris, and other concepts, and allows for future contributions from distributed data sources. We demonstrate how FathomNet data can be used to train and deploy models on other institutional video to reduce annotation effort, and enable automated tracking of underwater concepts when integrated with robotic vehicles. As FathomNet continues to grow and incorporate more labeled data from the community, we can accelerate the processing of visual data to achieve a healthy and sustainable global ocean.