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Dataset Description

This dataset has been converted to COCO format and contains bounding box annotations for content detection.

  Dataset Structure

The dataset is split into training and validation sets:

Training set: 583 images Validation set: 146 images

  Format

The dataset follows the COCO format with the following structure:

images: Contains the image files annotations.json: Contains the COCO format annotations dataset.yaml: Configuration file for training… See the full description on the dataset page: https://huggingface.co/datasets/zigg-ai/content-regions-1k-coco.

This dataset contains annotated images for object detection for containers and hands in a first-person view (egocentric view) during drinking activities. Both YOLOV8 format and COCO format are provided.Please refer to our paper for more details.Purpose: Training and testing the object detection model.Content: Videos from Session 1 of Subjects 1-20.Images: Extracted from the videos of Subjects 1-20 Session 1.Additional Images:~500 hand/container images from Roboflow Open Source data.~1500 null (background) images from VOC Dataset and MIT Indoor Scene Recognition Dataset:1000 indoor scenes from 'MIT Indoor Scene Recognition'400 other unrelated objects from VOC DatasetData Augmentation:Horizontal flipping±15% brightness change±10° rotationFormats Provided:COCO formatPyTorch YOLOV8 formatImage Size: 416x416 pixelsTotal Images: 16,834Training: 13,862Validation: 1,975Testing: 997Instance Numbers:Containers: Over 10,000Hands: Over 8,000

COCO Captions contains over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions are be provided for each image.

The IMPTOX project has received funding from the EU's H2020 framework programme for research and innovation under grant agreement n. 965173. Imptox is part of the European MNP cluster on human health.

More information about the project here.

Description: This repository includes the trained weights and a custom COCO-formatted dataset used for developing and testing a Faster R-CNN R_50_FPN_3x object detector, specifically designed to identify particles in micro-FTIR filter images.

Contents:

Weights File (neuralNetWeights_V3.pth):

Format: .pth

Description: This file contains the trained weights for a Faster R-CNN model with a ResNet-50 backbone and a Feature Pyramid Network (FPN), trained for 3x schedule. These weights are specifically tuned for detecting particles in micro-FTIR filter images.

Custom COCO Dataset (uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip):

Format: .zip

Description: This zip archive contains a custom COCO-formatted dataset, including JPEG images and their corresponding annotation file. The dataset consists of images of micro-FTIR filters with annotated particles.

Contents:

Images: JPEG format images of micro-FTIR filters.

Annotations: A JSON file in COCO format providing detailed annotations of the particles in the images.

Management: The dataset can be managed and manipulated using the Pycocotools library, facilitating easy integration with existing COCO tools and workflows.

Applications: The provided weights and dataset are intended for researchers and practitioners in the field of microscopy and particle detection. The dataset and model can be used for further training, validation, and fine-tuning of object detection models in similar domains.

Usage Notes:

The neuralNetWeights_V3.pth file should be loaded into a PyTorch model compatible with the Faster R-CNN architecture, such as Detectron2.

The contents of uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip should be extracted and can be used with any COCO-compatible object detection framework for training and evaluation purposes.

Code can be found on the related Github repository.

Data abstract:
The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.)

Paper abstract:
The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%.

Context

Primarily the data was taken from roboflow and the annotation masks were prepared manually by me to be used for few-shot learning instance segmentation.

Content

The folders contain 3 zip file train: Containing training images. validation: Contains validation images. coco annotations: contacting annotations for the train and validation in MS-COCO JSON format.

Acknowledgements

The dataset is primarily taken from roboflow and then processed by me. So, I heartily thank roboflow team to provide us such datasets with which we can try different tasks.

Inspiration

At this time, instance segmentation is largely used by ML/DL developers. Also, there is a huge data in the market for free, which can be gathered and creating several datasets which will help us find new techniques to form new ideas as well as refining the current SOTA techniques or models. The researchers out there is the true inspiration who publish new papers so that the industry can adopt advanced futuristic works and make production fly to the sky.

Important Point

The dataset has been prepared for few-shot learning.

This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to perform instance segmentation of Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 810 images (.jpg) of size 224x224 pixels. We also provide partitioning of the data into Train (567 images), Test (162 images), and Validation (81 images) subsets. Their annotations are provided in three different .json files following the COCO annotation format.

A collection of 3 referring expression datasets based off images in the COCO dataset. A referring expression is a piece of text that describes a unique object in an image. These datasets are collected by asking human raters to disambiguate objects delineated by bounding boxes in the COCO dataset.

RefCoco and RefCoco+ are from Kazemzadeh et al. 2014. RefCoco+ expressions are strictly appearance based descriptions, which they enforced by preventing raters from using location based descriptions (e.g., "person to the right" is not a valid description for RefCoco+). RefCocoG is from Mao et al. 2016, and has more rich description of objects compared to RefCoco due to differences in the annotation process. In particular, RefCoco was collected in an interactive game-based setting, while RefCocoG was collected in a non-interactive setting. On average, RefCocoG has 8.4 words per expression while RefCoco has 3.5 words.

Each dataset has different split allocations that are typically all reported in papers. The "testA" and "testB" sets in RefCoco and RefCoco+ contain only people and only non-people respectively. Images are partitioned into the various splits. In the "google" split, objects, not images, are partitioned between the train and non-train splits. This means that the same image can appear in both the train and validation split, but the objects being referred to in the image will be different between the two sets. In contrast, the "unc" and "umd" splits partition images between the train, validation, and test split. In RefCocoG, the "google" split does not have a canonical test set, and the validation set is typically reported in papers as "val*".

Stats for each dataset and split ("refs" is the number of referring expressions, and "images" is the number of images):

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('ref_coco', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/ref_coco-refcoco_unc-1.1.0.png" alt="Visualization" width="500px">

Note: To better find the files to download, select "Change View: Tree". The dataset contains: 2931 images from conventional pig farming with object detection annotations in yolo and coco format with predefined training, validation and test splits Trained model weights for pig detection A thorough explanation of all files contained in this data repository can be found in ReadMe.txt.

This dataset consists of five subsets with annotated images in COCO format, designed for object detection and tracking plant growth: 1. Cucumber_Train Dataset (for Faster R-CNN) - Includes training, validation, and test images of cucumbers from different angles. - Annotations: Bounding boxes in COCO format for object detection tasks.

1. Tomato Dataset
2. Contains images of tomato plants for 24 hours at hourly intervals from a fixed angle.

3. Annotations: Bounding boxes in COCO format.

4. Pepper Dataset

5. Contains images of pepper plants for 24 hours at hourly intervals from a fixed angle.

6. Annotations: Bounding boxes in COCO format.

7. Cannabis Dataset

8. Contains images of cannabis plants for 24 hours at hourly intervals from a fixed angle.

9. Annotations: Bounding boxes in COCO format.

10. Cucumber Dataset

11. Contains images of cucumber plants for 24 hours at hourly intervals from a fixed angle.

12. Annotations: Bounding boxes in COCO format.

This dataset supports training and evaluation of object detection models across diverse crops.

The Dataset

A collection of images of parking lots for vehicle detection, segmentation, and counting. Each image is manually labeled with pixel-wise masks and bounding boxes localizing vehicle instances. The dataset includes about 250 images depicting several parking areas describing most of the problematic situations that we can find in a real scenario: seven different cameras capture the images under various weather conditions and viewing angles. Another challenging aspect is the presence of partial occlusion patterns in many scenes such as obstacles (trees, lampposts, other cars) and shadowed cars. The main peculiarity is that images are taken during the day and the night, showing utterly different lighting conditions.

We suggest a three-way split (train-validation-test). The train split contains images taken during the daytime while validation and test splits include images gathered at night. In line with these splits we provide some annotation files:

train_coco_annotations.json and val_coco_annotations.json --> JSON files that follow the golden standard MS COCO data format (for more info see https://cocodataset.org/#format-data) for the training and the validation splits, respectively. All the vehicles are labeled with the COCO category 'car'. They are suitable for vehicle detection and instance segmentation.

train_dot_annotations.csv and val_dot_annotations.csv --> CSV files that contain xy coordinates of the centroids of the vehicles for the training and the validation splits, respectively. Dot annotation is commonly used for the visual counting task.

ground_truth_test_counting.csv --> CSV file that contains the number of vehicles present in each image. It is only suitable for testing vehicle counting solutions.

Citing our work

If you found this dataset useful, please cite the following paper

@inproceedings{Ciampi_visapp_2021, doi = {10.5220/0010303401850195}, url = {https://doi.org/10.5220%2F0010303401850195}, year = 2021, publisher = {{SCITEPRESS} - Science and Technology Publications}, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {Domain Adaptation for Traffic Density Estimation}, booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications} }

and this Zenodo Dataset

@dataset{ciampi_ndispark_6560823, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {{Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas}}, month = may, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.6560823}, url = {https://doi.org/10.5281/zenodo.6560823} }

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it

This dataset contains 4,599 high-quality, annotated images of 25 commonly used chemistry lab apparatuses. The images, each containing structures in real-world settings, have been captured from different angles, backgrounds, and distances, while also undergoing variations in lighting to aid in the robustness of object detection models. Every image has been labeled using bounding box annotation in YOLO and COCO format, alongside the class IDs and normalized bounding box coordinates making object detection more precise. The annotations and bounding boxes have been built using the Roboflow platform.To achieve a better learning procedure, the dataset has been split into three sub-datasets: training, validation, and testing. The training dataset constitutes 70% of the entire dataset, with validation and testing at 20% and 10% respectively. In addition, all images undergo scaling to a standard of 640x640 pixels while being auto-oriented to rectify rotation discrepancies brought about by the EXIF metadata. The dataset is structured in three main folders - train, valid, and test, and each contains images/ and labels/ subfolders. Every image contains a label file containing class and bounding box data corresponding to each detected object.The whole dataset features 6,960 labeled instances per 25 apparatus categories including beakers, conical flasks, measuring cylinders, test tubes, among others. The dataset can be utilized for the development of automation systems, real-time monitoring and tracking systems, tools for safety monitoring, alongside AI educational tools.

TreeAI - Advancing Tree Species Identification from Aerial Images with Deep Learning

We present a baseline deep learning dataset of 2547 polygons for 36 tree species in Northern Australia. Polygons were drawn on imagery that was collected using Remotely Piloted Aircraft System (RPAS). The dataset consists of:

7 orthomosaics

7 shape files with polygon annotations

1 training dataset in COCO format

1 validation dataset in COCO format

Training and validation datasets were derived from the orthomosaics by tiling each image at 1024x1024 pixel size with 512 pixel step size (overlap).

To perform deep learning model training with this dataset go to https://github.com/ajansenn/SavannaTreeAI for more information.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23345571%2F4471e4ade50676d782d4787f77aa08ad%2F1000_F_256252609_6WIHRGbpzSaVQwioubxwgXdSJTNONNcK.jpg?generation=1739209341333909&alt=media" alt="">

This dataset contains 2,700 images focused on detecting potholes, cracks, and open manholes on roads. It has been augmented to enhance the variety and robustness of the data. The images are organized into training and validation sets, with three distinct categories:

• Potholes: class 0
• Cracks: class 1
• Open Manholes: class 2

Included in the Dataset: - Bounding Box Annotations in YOLO Format (.txt files) - Format: YOLOv8 & YOLO11 compatible - Purpose: Ready for training YOLO-based object detection models

• Folder Structure Organized into:

  • train/ folder
  • valid/ folder
  • Class-specific folders
  • An all_classes/ folder for combined access Benefit: Easy access for training, validation, and augmentation tasks

• Dual Format Support

  • COCO JSON Annotations Included -Compatible with models like Faster R-CNN Enables flexibility across different object detection frameworks

• Use Cases Targeted

  • Model training
  • Model testing
  • Custom data augmentation
  • Specific focus: Road safety and infrastructure detection

Here's a clear breakdown of the folder structure:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23345571%2F023b40c98bf858c58394d6ed2393bfc3%2FScreenshot%202025-05-01%20202438.png?generation=1746109541780835&alt=media" alt="">

This dataset is intended for the reproducability of the methods in 'Exploration of TPU Architectures for the Optimized Transformer in Drainage Crossing Detection', along with the provided GitHub repository at https://github.com/SHUs-Lab/BTSD24AN.The dataset consists of 6,012 LiDAR-derived DEM georeferenced rasters in TIF format, each with an 800m x 800m extent and a cell size of 1m x 1m. Elevation data is stored as 32-bit floating point values, indicating meters above sea level, and comes from the USGS 3DEP program.The rasters cover four watersheds in the Continental United States: Sacramento-Stone Corral in California, Vermilion River in Illinois, Maple River in North Dakota, and West Fork Big Blue in Nebraska. Drainage crossings within these watersheds were labeled as centroids, and corresponding rasters containing these centroids were extracted. Bounding boxes of 100m x 100m were defined around these centroids, and the data were converted to the COCO format for use with the DETR model.After filtering out anomalous rasters, 6,007 rasters with 13,141 drainage crossing bounding boxes were used. The Maple River Watershed data was reserved for transfer learning.The directory structure is as follows:processed_data├── initial_data│ ├── annotations│ ├── test│ ├── train│ └── validate└── transfer_data ├── annotations └── test

This dataset contains annotated marine vessels from 15 different Sentinel-2 product, used for training object detection models for marine vessel detection. The vessels are annotated as bounding boxes, covering also some amount of the wake, if present.

Source data

Individual products used to generate annotations are shown in the following table:

Location Product name

Archipelago sea S2A_MSIL1C_20220515T100031_N0400_R122_T34VEM_20220515T120450

S2B_MSIL1C_20220619T100029_N0400_R122_T34VEM_20220619T104419

S2A_MSIL1C_20220721T095041_N0400_R079_T34VEM_20220721T115325

S2A_MSIL1C_20220813T095601_N0400_R122_T34VEM_20220813T120233

Gulf of Finland S2B_MSIL1C_20220606T095029_N0400_R079_T35VLG_20220606T105944

S2B_MSIL1C_20220626T095039_N0400_R079_T35VLG_20220626T104321

S2B_MSIL1C_20220703T094039_N0400_R036_T35VLG_20220703T103953

S2A_MSIL1C_20220721T095041_N0400_R079_T35VLG_20220721T115325

Bothnian Bay S2A_MSIL1C_20220627T100611_N0400_R022_T34WFT_20220627T134958

S2B_MSIL1C_20220712T100559_N0400_R022_T34WFT_20220712T121613

S2B_MSIL1C_20220828T095549_N0400_R122_T34WFT_20220828T104748

Bothnian Sea S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043

S2B_MSIL1C_20220619T100029_N0400_R122_T34VEN_20220619T104419

S2A_MSIL1C_20220624T100041_N0400_R122_T34VEN_20220624T120211

S2A_MSIL1C_20220813T095601_N0400_R122_T34VEN_20220813T120233

Kvarken S2A_MSIL1C_20220617T100611_N0400_R022_T34VER_20220617T135008

S2B_MSIL1C_20220712T100559_N0400_R022_T34VER_20220712T121613

S2A_MSIL1C_20220826T100611_N0400_R022_T34VER_20220826T135136

Even though the reference data IDs are for L1C products, L2A products from the same acquisition dates can be used along with the annotations. However, Sen2Cor has been known to produce incorrect reflectance values for water bodies.

The raw products can be acquired from Copernicus Data Space Ecosystem.

Annotations

The annotations are bounding boxes drawn around marine vessels so that some amount of their wakes, if present, are also contained within the boxes. The data are distributed as geopackage files, so that one geopackage corresponds to a single Sentinel-2 tile, and each package has separate layers for individual products as shown below:

T34VEM

|-20220515

|-20220619

|-20220721

|-20220813

All layers have a column id, which has the value boat for all annotations.

CRS is EPSG:32634 for all products except for the Gulf of Finland (35VLG), which is in EPSG:32635. This is done in order to have the bounding boxes to be aligned with the pixels in the imagery.

As tiles 34VEM and 34VEN have an overlap of 9.5x100 km, 34VEN is not annotated from the overlapping part to prevent data leakage between splits.

Annotation process The minimum size for an object to be considered as a potential marine vessel was set to 2x2 pixels. Three separate acquisitions for each location were used to detect smallest objects, so that if an object was located at the same place in all images, then it was left unannotated. The data were annotated by two experts.

Product name Number of annotations

S2A_MSIL1C_20220515T100031_N0400_R122_T34VEM_20220515T120450 183

S2B_MSIL1C_20220619T100029_N0400_R122_T34VEM_20220619T104419 519

S2A_MSIL1C_20220721T095041_N0400_R079_T34VEM_20220721T115325 1518

S2A_MSIL1C_20220813T095601_N0400_R122_T34VEM_20220813T120233 1371

S2B_MSIL1C_20220606T095029_N0400_R079_T35VLG_20220606T105944 277

S2B_MSIL1C_20220626T095039_N0400_R079_T35VLG_20220626T104321 1205

S2B_MSIL1C_20220703T094039_N0400_R036_T35VLG_20220703T103953 746

S2A_MSIL1C_20220721T095041_N0400_R079_T35VLG_20220721T115325 971

S2A_MSIL1C_20220627T100611_N0400_R022_T34WFT_20220627T134958 122

S2B_MSIL1C_20220712T100559_N0400_R022_T34WFT_20220712T121613 162

S2B_MSIL1C_20220828T095549_N0400_R122_T34WFT_20220828T104748 98

S2B_MSIL1C_20210714T100029_N0301_R122_T34VEN_20210714T121056 450

S2B_MSIL1C_20220619T100029_N0400_R122_T34VEN_20220619T104419 66

S2A_MSIL1C_20220624T100041_N0400_R122_T34VEN_20220624T120211 424

S2A_MSIL1C_20220813T095601_N0400_R122_T34VEN_20220813T120233 399

S2A_MSIL1C_20220617T100611_N0400_R022_T34VER_20220617T135008 83

S2B_MSIL1C_20220712T100559_N0400_R022_T34VER_20220712T121613 184

S2A_MSIL1C_20220826T100611_N0400_R022_T34VER_20220826T135136 88

Annotation statistics Sentinel-2 images have spatial resolution of 10 m, so below statistics can be converted to pixel sizes by dividing them by 10 (diameter) or 100 (area).

mean min 25% 50% 75% max

Area (m²) 5305.7 567.9 1629.9 2328.2 5176.3 414795.7

Diameter (m) 92.5 33.9 57.9 69.4 108.3 913.9

As most of the annotations cover also most of the wake of the marine vessel, the bounding boxes are significantly larger than a typical boat. There are a few annotations larger than 100 000 m², which are either cruise or cargo ships that are travelling along ordinal directions instead of cardinal directions, instead of e.g. smaller leisure boats.

Annotations typically have diameter less than 100 meters, and the largest diameters correspond to similar instances than the largest bounding box areas.

Train-test-split

We used tiles 34VEN and 34VER as the test dataset. For validation, we split the other three tile areas into 5x5 equal sized grid, and used 20 % of the area (i.e 5 cells) for the validation. The same split also makes it possible to do cross-validation.

Post-processing

Before evaluating, the predictions for the test set are cleaned using the following steps:

1. All prediction whose centroid points are not located on water are discarded. The water mask used contains layers jarvi (Lakes), meri (Sea) and virtavesialue (Rivers as polygon geometry) from the Topographical database by the National Land Survey of Finland. Unfortunately this also discards all points not within the Finnish borders.

2. All predictions whose centroid points are located on water rock areas are discarded. The mask is the layer vesikivikko (Water rock areas) from the Topographical database.

3. All predictions that contain an above water rock within the bounding box are discarded. The mask contains classes 38511, 38512, 38513 from the layer vesikivi in the Topographical database.

4. All predictions that contain a lighthouse or a sector light within the bounding box are discarded. Lighthouses and sector lights come from Väylävirasto data, ty_njr class ids are 1, 2, 3, 4, 5, 8

5. All predictions that are wind turbines, found in Topographical database layer tuulivoimalat

6. All predictions that are obviously too large are discarded. The prediction is defined to be "too large" if either of its edges is longer than 750 meters.

Model checkpoint for the best performing model is available on Hugging Face platform: https://huggingface.co/mayrajeo/marine-vessel-detection-yolo

Usage The simplest way to chip the rasters into suitable format and convert the data to COCO or YOLO formats is to use geo2ml. First download the raw mosaics and convert them into GeoTiff files and then use the following to generate the datasets.

To generate COCO format dataset run

from geo2ml.scripts.data import create_coco_dataset raster_path = '' outpath = '' poly_path = '' layer = '' create_coco_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, dataset_name='', gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)

To generate YOLO format dataset run

from geo2ml.scripts.data import create_yolo_dataset raster_path = '' outpath = '' poly_path = '' layer = '' create_yolo_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)

Pothole Dataset

https://i.imgur.com/7Xz8d5M.gif" alt="Example Image">

This is a collection of 665 images of roads with the potholes labeled. The dataset was created and shared by Atikur Rahman Chitholian as part of his undergraduate thesis and was originally shared on Kaggle.

Note: The original dataset did not contain a validation set; we have re-shuffled the images into a 70/20/10 train-valid-test split.

Usage

This dataset could be used for automatically finding and categorizing potholes in city streets so the worst ones can be fixed faster.

The dataset is provided in a wide variety of formats for various common machine learning models.

We present the Weddell Sea Benthic Dataset (WSBD), a computer vision-ready collection of high-resolution seafloor imagery and corresponding annotations designed to support automated analysis of Antarctic benthic communities. The dataset comprises 100 top-down images captured during RV Polarstern Expedition PS118 (cruises 69-1 and 69-6) in 2019, using the Ocean Floor Observation and Bathymetry System (OFOBS) in the Weddell Sea, Antarctica. A subset of this imagery was manually annotated by ecologists at the British Antarctic Survey (BAS) to support ecological analyses, including benthic community composition and species interaction studies. These annotations were subsequently standardised into 25 morphotypes to serve as class labels for object detection tasks. Bounding box annotations are provided in COCO format, alongside the training, validation, and test splits used during model development at BAS. This dataset provides a benchmark for developing and evaluating machine learning models aimed at enhancing biodiversity monitoring in Antarctic benthic environments.

This work was funded by the UKRI Future Leaders Fellowship MR/W01002X/1 ''The past, present and future of unique cold-water benthic (sea floor) ecosystems in the Southern Ocean'' awarded to Rowan Whittle.