--- title: "2025-MBARI-Benthic-Supercategory-Object-Detector" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/41aa0ccd-4b79-4714-9234-a31753804f22" updated: "2026-11-13" --- # 2025-MBARI-Benthic-Supercategory-Object-Detector > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds broad groups of seafloor objects in images, going by its name, from FathomNet for the yolov26 library. The page has no model card, so test it on your own images before you rely on it. 2025-MBARI-Benthic-Supercategory-Object-Detector is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by FathomNet ## About ### What it is An object detection model published by FathomNet. Going by its name, it finds broad groups of seafloor objects, called benthic supercategories, in images, and the name also carries MBARI and 2025. Hugging Face tags it for the yolov26 library and shows no model card text, so this page states nothing beyond that. ### Who it suits Marine researchers and computer vision developers who work with seafloor images and are happy to inspect a model without written guidance. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Marine science | Ocean researchers | Look for broad groups of seafloor objects in images | Try it on a photo taken near the seabed | | Environmental monitoring | Survey teams | Check whether a detector fits their own seafloor imagery | Run it on a few sample frames and review the results | | AI research | Computer vision developers | Study a detector tagged yolov26 | Open the downloaded files and see what they hold | You can skip it if you need documented classes, accuracy figures or run instructions, since the page gives none. ### Where to use it The page names only the yolov26 library as its home, so use it in a setup that supports that library. - A detection setup that supports the yolov26 library - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Seafloor image analysis | Marine researchers | Detections of broad object groups, as the name suggests | | Model inspection | Developers | The downloaded files to study and test | ### How to set it up Fetch the files with the Hugging Face tools, then open them in a tool that supports the yolov26 library. The page gives no further setup steps. - Python, for the Hugging Face download commands - A tool that supports the yolov26 library 1. Install the Hugging Face tools. 2. Download the model files to your computer. 3. Load them in your yolov26 tooling and test on a few images. ```bash pip install -U huggingface_hub hf download FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector ``` ### How to use it The page shows no run commands. Load the downloaded files in the tooling for the yolov26 library, pass in an image, and read the detections it returns. ### Example Give the detector an underwater seafloor photo and look at which broad groups of objects it marks. The page shows no sample output, so judge the results yourself. ### For newcomers and experts An object detector finds things in a picture and marks where they are. A supercategory is a broad group that holds many narrower kinds. A first small try is to download the files and look at what is inside. For experts, the page gives no architecture, class list, training data or metrics, so validate it on your own imagery. The license is cc-by-4.0, which asks you to credit the author when you reuse the model. Start from a seafloor image → Run the detector → Read the marked object groups ### Good to know This page is short because the model page itself has no description. - No model card: the page lists no classes, metrics or training data. - Library: Hugging Face tags it as yolov26. - License: cc-by-4.0. - Cost to run: none named, no API key, account or paid service. - Data it reaches: it downloads the model files, and works on whichever images you give it. - Not for: the page names no limits, so check it against your own data first. ## README ````markdown # 2025-MBARI-Benthic-Supercategory-Object-Detector Finds broad groups of seafloor objects in images, going by its name, from FathomNet for the yolov26 library. The page has no model card, so test it on your own images before you rely on it. Made by FathomNet on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: An object detection model published by FathomNet. Going by its name, it finds broad groups of seafloor objects, called benthic supercategories, in images, and the name also carries MBARI and 2025. Hugging Face tags it for the yolov26 library and shows no model card text, so this page states nothing beyond that. Who it suits: Marine researchers and computer vision developers who work with seafloor images and are happy to inspect a model without written guidance. ```table Industry | Who uses it | How they use it | Example Marine science | Ocean researchers | Look for broad groups of seafloor objects in images | Try it on a photo taken near the seabed Environmental monitoring | Survey teams | Check whether a detector fits their own seafloor imagery | Run it on a few sample frames and review the results AI research | Computer vision developers | Study a detector tagged yolov26 | Open the downloaded files and see what they hold ``` You can skip it if you need documented classes, accuracy figures or run instructions, since the page gives none. Where to use it: The page names only the yolov26 library as its home, so use it in a setup that supports that library. - A detection setup that supports the yolov26 library - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Seafloor image analysis | Marine researchers | Detections of broad object groups, as the name suggests Model inspection | Developers | The downloaded files to study and test ``` How to set it up: Fetch the files with the Hugging Face tools, then open them in a tool that supports the yolov26 library. The page gives no further setup steps. - Python, for the Hugging Face download commands - A tool that supports the yolov26 library 1. Install the Hugging Face tools. 2. Download the model files to your computer. 3. Load them in your yolov26 tooling and test on a few images. ```bash pip install -U huggingface_hub hf download FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector ``` How to use it: The page shows no run commands. Load the downloaded files in the tooling for the yolov26 library, pass in an image, and read the detections it returns. Example: Give the detector an underwater seafloor photo and look at which broad groups of objects it marks. The page shows no sample output, so judge the results yourself. For newcomers and experts: An object detector finds things in a picture and marks where they are. A supercategory is a broad group that holds many narrower kinds. A first small try is to download the files and look at what is inside. For experts, the page gives no architecture, class list, training data or metrics, so validate it on your own imagery. The license is cc-by-4.0, which asks you to credit the author when you reuse the model. ```flow Start from a seafloor image Run the detector Read the marked object groups ``` Good to know: This page is short because the model page itself has no description. - No model card: the page lists no classes, metrics or training data. - Library: Hugging Face tags it as yolov26. - License: cc-by-4.0. - Cost to run: none named, no API key, account or paid service. - Data it reaches: it downloads the model files, and works on whichever images you give it. - Not for: the page names no limits, so check it against your own data first. ## On Hugging Face, 13 November 2026 - Library: yolov26 - Downloads in the last 30 days: 36 - Likes: 3 - License: cc-by-4.0 - Tags: yolov26 ## Links - Model: https://huggingface.co/FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector - Files: https://huggingface.co/FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector on Hugging Face](https://huggingface.co/FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector) ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-07-06 - First published: 2025-08-16 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector on Hugging Face](https://huggingface.co/FathomNet/2025-MBARI-Benthic-Supercategory-Object-Detector) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/41aa0ccd-4b79-4714-9234-a31753804f22) - [Its page on Mindket](https://mindket.com/p/41aa0ccd-4b79-4714-9234-a31753804f22)