Dataset

This dataset was created by Ramsi Kalia

Contents

This Is Keypoint-Only subset from COCO 2017 Dataset. You can access the original COCO Dataset from here

This Dataset contains three folders: annotations, val2017, and train2017. - Contents in annotation folder is two jsons, for val dan train. Each jsons contains various informations, like the image id, bounding box, and keypoints locations. - Contents of val2017 and train2017 is various images that have been filtered. They are the images that have num_keypoints > 0 according to the annotation file.

This Zenodo dataset contain the Common Objects in Context (COCO) files linked to the following publication:

Verhaegen, G, Cimoli, E, & Lindsay, D (2021). Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning. Biodiversity Data Journal.

Each COCO zip folder contains an "annotations" folder including a json file and an "images" folder containing the annotated images.

Details on each COCO zip folders:

Beroe_sp_A_images-coco 1.0.zip

COCO annotations of Beroe sp. A for the following 114 images:

MCMEC2018_20181116_NIKON_Beroe_sp_A_c_1 to MCMEC2018_20181116_NIKON_Beroe_sp_A_c_16, MCMEC2018_20181125_NIKON_Beroe_sp_A_d_1 to MCMEC2018_20181125_NIKON_Beroe_sp_A_d_57, MCMEC2018_20181127_NIKON_Beroe_sp_A_e_1 to MCMEC2018_20181127_NIKON_Beroe_sp_A_e_2, MCMEC2019_20191116_SONY_Beroe_sp_A_a_1 to MCMEC2019_20191116_SONY_Beroe_sp_A_a_28, and MCMEC2019_20191127_SONY_Beroe_sp_A_f_1 to MCMEC2019_20191127_SONY_Beroe_sp_A_f_12

Beroe_sp_B_images-coco 1.0.zip

COCO annotations of Beroe sp. B for the following 2 images:

MCMEC2019_20191115_SONY_Beroe_sp_B_a_1 and MCMEC2019_20191115_SONY_Beroe_sp_B_a_2

Callianira_cristata_images-coco 1.0.zip

COCO annotations of Callianira cristata for the following 21 images:

MCMEC2019_20191120_SONY_Callianira_cristata_b_1 to MCMEC2019_20191120_SONY_Callianira_cristata_b_21

Diplulmaris_antarctica_images-coco 1.0.zip

COCO annotations of Diplulmaris antarctica for the following 83 images:

MCMEC2019_20191116_SONY_Diplulmaris_antarctica_a_1 to MCMEC2019_20191116_SONY_Diplulmaris_antarctica_a_9, and MCMEC2019_20191201_SONY_Diplulmaris_antarctica_c_1 to MCMEC2019_20191201_SONY_Diplulmaris_antarctica_c_74

Koellikerina_maasi_images-coco 1.0.zip

COCO annotations of Koellikerina maasi for the following 49 images:

MCMEC2018_20181127_NIKON_Koellikerina_maasi_b_1 to MCMEC2018_20181127_NIKON_Koellikerina_maasi_b_4, MCMEC2018_20181129_NIKON_Koellikerina_maasi_c_1 to MCMEC2018_20181129_NIKON_Koellikerina_maasi_c_29, and MCMEC2019_20191126_SONY_Koellikerina_maasi_a_1 to MCMEC2019_20191126_SONY_Koellikerina_maasi_a_16

Leptomedusa_sp_A-coco 1.0.zip

COCO annotations of Leptomedusa sp. A for Figure 5 (see paper).

Leuckartiara_brownei_images-coco 1.0.zip

COCO annotations of Leuckartiara brownei for the following 48 images:

MCMEC2018_20181129_NIKON_Leuckartiara_brownei_b_1 to MCMEC2018_20181129_NIKON_Leuckartiara_brownei_b_27, MCMEC2018_20181129_NIKON_Leuckartiara_brownei_c_1 to MCMEC2018_20181129_NIKON_Leuckartiara_brownei_c_6, and MCMEC2019_20191116_SONY_Leuckartiara_brownei_a_1 to MCMEC2019_20191116_SONY_Leuckartiara_brownei_a_15

MCMEC2019_20191115_SONY_Mertensiidae_sp_A_a_3-coco 1.0.zip

COCO annotations of Mertensiidae sp. A for the following video (total of 1847 frames): MCMEC2019_20191115_SONY_Mertensiidae_sp_A_a_3 (https://youtu.be/0W2HHLW71Pw)

MCMEC2019_20191116_SONY_Leuckartiara_brownei_a_3-coco 1.0.zip

COCO annotations of Leuckartiara brownei for the following video (total of 1367 frames): MCMEC2019_20191116_SONY_Leuckartiara_brownei_a_3 (https://youtu.be/dEIbVYlF_TQ)

MCMEC2019_20191122_SONY_Callianira_cristata_a_1-coco 1.0.zip

COCO annotations of Callianira cristata for the following video (total of 2423 frames): MCMEC2019_20191122_SONY_Callianira_cristata_a_1 (https://youtu.be/30g9CvYh5JE)

MCMEC2019_20191122_SONY_Leptomedusa_sp_B_a_1-coco 1.0.zip

COCO annotations of Leptomedusa sp. B for the following video (total of 1164 frames): MCMEC2019_20191122_SONY_Leptomedusa_sp_B_a_1 (https://youtu.be/hrufuPQ7F8U)

MCMEC2019_20191126_SONY_Koellikerina_maasi_a_1-coco 1.0.zip

COCO annotations of Koellikerina maasi for the following video (total of 1643 frames): MCMEC2019_20191126_SONY_Koellikerina_maasi_a_1 (https://youtu.be/QiBPf_HYrQ8)

MCMEC2019_20191129_SONY_Mertensiidae_sp_A_b_1-coco 1.0.zip

COCO annotations of Mertensiidae sp. A for the following video (total of 239 frames): MCMEC2019_20191129_SONY_Mertensiidae_sp_A_b_1 (https://youtu.be/pvXYlQGZIVg)

MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_2-coco 1.0.zip

COCO annotations of Pyrostephos vanhoeffeni for the following video (total of 444 frames): MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_2 (https://youtu.be/2rrQCybEg0Q)

MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_3-coco 1.0.zip

COCO annotations of Pyrostephos vanhoeffeni for the following video (total of 683 frames): MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_3 (https://youtu.be/G9tev_gdUvQ)

MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_4-coco 1.0.zip

COCO annotations of Pyrostephos vanhoeffeni for the following video (total of 1127 frames): MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_4 (https://youtu.be/NfJjKBRh5Hs)

MCMEC2019_20191130_SONY_Beroe_sp_A_b_1-coco 1.0.zip

COCO annotations of Beroe sp. A for the following video (total of 2171 frames): MCMEC2019_20191130_SONY_Beroe_sp_A_b_1 (https://youtu.be/kGBUQ7ZtH9U)

MCMEC2019_20191130_SONY_Beroe_sp_A_b_2-coco 1.0.zip

COCO annotations of Beroe sp. A for the following video (total of 359 frames): MCMEC2019_20191130_SONY_Beroe_sp_A_b_2 (https://youtu.be/Vbl_KEmPNmU)

Mertensiidae_sp_A_images-coco 1.0.zip

COCO annotations of Mertensiidae sp. A for the following 49 images:

MCMEC2018_20181127_NIKON_Mertensiidae_sp_A_c_1 to MCMEC2018_20181127_NIKON_Mertensiidae_sp_A_c_2, MCMEC2018_20181127_NIKON_Mertensiidae_sp_A_f_1 to MCMEC2018_20181127_NIKON_Mertensiidae_sp_A_f_8, MCMEC2018_20181129_NIKON_Mertensiidae_sp_A_d_1 to MCMEC2018_20181129_NIKON_Mertensiidae_sp_A_d_13, MCMEC2018_20181201_ROV_Mertensiidae_sp_A_e_1 to MCMEC2018_20181201_ROV_Mertensiidae_sp_A_e_15, and MCMEC2019_20191115_SONY_Mertensiidae_sp_A_a_1 to MCMEC2019_20191115_SONY_Mertensiidae_sp_A_a_11

Pyrostephos_vanhoeffeni_images-coco 1.0.zip

COCO annotations of Pyrostephos vanhoeffeni for the following 14 images: MCMEC2019_20191125_SONY_Pyrostephos_vanhoeffeni_a_1 to MCMEC2019_20191125_SONY_Pyrostephos_vanhoeffeni_a_8, MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_1 to MCMEC2019_20191129_SONY_Pyrostephos_vanhoeffeni_b_6

Solmundella_bitentaculata_images-coco 1.0.zip

COCO annotations of Solmundella bitentaculata for the following 13 images: MCMEC2018_20181127_NIKON_Solmundella_bitentaculata_a_1 to MCMEC2018_20181127_NIKON_Solmundella_bitentaculata_a_13

Context

The dataset contains car images with one or more damaged parts. The img/ folder has all 80 images in the dataset. There are three more folders train/, val/ and test/ for training, validation and testing purposes respectively.

Folders

train/: - Contains 59 images. - COCO_train_annos.json: Train annotation file for damages where damage is the one and only category. - COCO_mul_train_annos.json: Train annotation file for parts having damages. There are five categories of parts based on which part the damage has happened. The parts can be namely, headlamp, front_bumper, hood, door, rear_bumper.

val/: - Contains 11 images. - COCO_val_annos.json: Validation annotation file for damages where damage is the one and only category. - COCO_mul_val_annos.json: Validation annotation file for parts having damages. There are five categories of parts based on which part the damage has happened. The parts can be namely, headlamp, front_bumper, hood, door, rear_bumper.

test/: - Contains 8 images.

Annotation files have the following keys:

• "annotations": Contains the bounding box and segmentation array.
• "categories": Contains the list of categories in the annotation.
• "images": Details of each image used in the annotation.
• "info": Creator information
• "licenses": License information

The folder contains the train/ and individual test splits: test_05019_cohort_1/, test_05019_cohort_2/ and test_03557/ sub folders. Each of them have corresponding annotations with the exact same name.

test_*_consensus.json: consensus annotations corresponding to each subset test_*_*_*.json: individual junior, mid-level (medior) or expert test set annotations for the specified subset train.json: the train set expert annotations

COCO is a large-scale object detection, segmentation, and captioning dataset.

Note: * Some images from the train and validation sets don't have annotations. * Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images). * Coco defines 91 classes but the data only uses 80 classes. * Panotptic annotations defines defines 200 classes but only uses 133.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('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/coco-2014-1.1.0.png" alt="Visualization" width="500px">

Dataset: COCO-Formatted Object Detection Dataset

Overview

This dataset is designed for object detection tasks and follows the COCO format. It contains 300 images and corresponding annotation files in JSON format. The dataset is split into training, validation, and test sets, ensuring a balanced distribution for model evaluation.

Dataset Structure

The dataset is organized into three main folders:

train/ (70% - 210 images)

valid/ (15% - 45 images)

test/ (15% - 45 images)

Each folder contains:

Images in JPEG/PNG format.

A corresponding _annotations.coco.json file that includes bounding box annotations.

Preprocessing & Augmentations

The dataset has undergone several preprocessing and augmentation steps to enhance model generalization:

Image Preprocessing:

Auto-orientation applied

Resized to 640x640 pixels (stretched)

Augmentation Techniques:

Flip: Horizontal flipping

Crop: 0% minimum zoom, 5% maximum zoom

Rotation: Between -5° and +5°

Saturation: Adjusted between -4% and +4%

Brightness: Adjusted between -10% and +10%

Blur: Up to 0px

Noise: Up to 0.1% of pixels

Bounding Box Augmentations:

Flipping, cropping, rotation, brightness adjustments, blur, and noise applied accordingly to maintain annotation consistency.

Annotation Format

The dataset follows the COCO (Common Objects in Context) format, which includes:

images section: Contains image metadata such as filename, width, and height.

annotations section: Includes bounding boxes, category IDs, and segmentation masks (if applicable).

categories section: Defines class labels.

This is Part 2/2 of the ActiveHuman dataset! Part 1 can be found here. Dataset Description ActiveHuman was generated using Unity's Perception package. It consists of 175428 RGB images and their semantic segmentation counterparts taken at different environments, lighting conditions, camera distances and angles. In total, the dataset contains images for 8 environments, 33 humans, 4 lighting conditions, 7 camera distances (1m-4m) and 36 camera angles (0-360 at 10-degree intervals). The dataset does not include images at every single combination of available camera distances and angles, since for some values the camera would collide with another object or go outside the confines of an environment. As a result, some combinations of camera distances and angles do not exist in the dataset. Alongside each image, 2D Bounding Box, 3D Bounding Box and Keypoint ground truth annotations are also generated via the use of Labelers and are stored as a JSON-based dataset. These Labelers are scripts that are responsible for capturing ground truth annotations for each captured image or frame. Keypoint annotations follow the COCO format defined by the COCO keypoint annotation template offered in the perception package.

Folder configuration The dataset consists of 3 folders:

JSON Data: Contains all the generated JSON files. RGB Images: Contains the generated RGB images. Semantic Segmentation Images: Contains the generated semantic segmentation images.

Essential Terminology

Annotation: Recorded data describing a single capture. Capture: One completed rendering process of a Unity sensor which stored the rendered result to data files (e.g. PNG, JPG, etc.). Ego: Object or person on which a collection of sensors is attached to (e.g., if a drone has a camera attached to it, the drone would be the ego and the camera would be the sensor). Ego coordinate system: Coordinates with respect to the ego. Global coordinate system: Coordinates with respect to the global origin in Unity. Sensor: Device that captures the dataset (in this instance the sensor is a camera). Sensor coordinate system: Coordinates with respect to the sensor. Sequence: Time-ordered series of captures. This is very useful for video capture where the time-order relationship of two captures is vital. UIID: Universal Unique Identifier. It is a unique hexadecimal identifier that can represent an individual instance of a capture, ego, sensor, annotation, labeled object or keypoint, or keypoint template.

Dataset Data The dataset includes 4 types of JSON annotation files files:

annotation_definitions.json: Contains annotation definitions for all of the active Labelers of the simulation stored in an array. Each entry consists of a collection of key-value pairs which describe a particular type of annotation and contain information about that specific annotation describing how its data should be mapped back to labels or objects in the scene. Each entry contains the following key-value pairs:

id: Integer identifier of the annotation's definition. name: Annotation name (e.g., keypoints, bounding box, bounding box 3D, semantic segmentation). description: Description of the annotation's specifications. format: Format of the file containing the annotation specifications (e.g., json, PNG). spec: Format-specific specifications for the annotation values generated by each Labeler.

Most Labelers generate different annotation specifications in the spec key-value pair:

BoundingBox2DLabeler/BoundingBox3DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. KeypointLabeler:

template_id: Keypoint template UUID. template_name: Name of the keypoint template. key_points: Array containing all the joints defined by the keypoint template. This array includes the key-value pairs:

label: Joint label. index: Joint index. color: RGBA values of the keypoint. color_code: Hex color code of the keypoint skeleton: Array containing all the skeleton connections defined by the keypoint template. Each skeleton connection defines a connection between two different joints. This array includes the key-value pairs:

label1: Label of the first joint. label2: Label of the second joint. joint1: Index of the first joint. joint2: Index of the second joint. color: RGBA values of the connection. color_code: Hex color code of the connection. SemanticSegmentationLabeler:

label_name: String identifier of a label. pixel_value: RGBA values of the label. color_code: Hex color code of the label.

captures_xyz.json: Each of these files contains an array of ground truth annotations generated by each active Labeler for each capture separately, as well as extra metadata that describe the state of each active sensor that is present in the scene. Each array entry in the contains the following key-value pairs:

id: UUID of the capture. sequence_id: UUID of the sequence. step: Index of the capture within a sequence. timestamp: Timestamp (in ms) since the beginning of a sequence. sensor: Properties of the sensor. This entry contains a collection with the following key-value pairs:

sensor_id: Sensor UUID. ego_id: Ego UUID. modality: Modality of the sensor (e.g., camera, radar). translation: 3D vector that describes the sensor's position (in meters) with respect to the global coordinate system. rotation: Quaternion variable that describes the sensor's orientation with respect to the ego coordinate system. camera_intrinsic: matrix containing (if it exists) the camera's intrinsic calibration. projection: Projection type used by the camera (e.g., orthographic, perspective). ego: Attributes of the ego. This entry contains a collection with the following key-value pairs:

ego_id: Ego UUID. translation: 3D vector that describes the ego's position (in meters) with respect to the global coordinate system. rotation: Quaternion variable containing the ego's orientation. velocity: 3D vector containing the ego's velocity (in meters per second). acceleration: 3D vector containing the ego's acceleration (in ). format: Format of the file captured by the sensor (e.g., PNG, JPG). annotations: Key-value pair collections, one for each active Labeler. These key-value pairs are as follows:

id: Annotation UUID . annotation_definition: Integer identifier of the annotation's definition. filename: Name of the file generated by the Labeler. This entry is only present for Labelers that generate an image. values: List of key-value pairs containing annotation data for the current Labeler.

Each Labeler generates different annotation specifications in the values key-value pair:

BoundingBox2DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values. x: Position of the 2D bounding box on the X axis. y: Position of the 2D bounding box position on the Y axis. width: Width of the 2D bounding box. height: Height of the 2D bounding box. BoundingBox3DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values. translation: 3D vector containing the location of the center of the 3D bounding box with respect to the sensor coordinate system (in meters). size: 3D vector containing the size of the 3D bounding box (in meters) rotation: Quaternion variable containing the orientation of the 3D bounding box. velocity: 3D vector containing the velocity of the 3D bounding box (in meters per second). acceleration: 3D vector containing the acceleration of the 3D bounding box acceleration (in ). KeypointLabeler:

label_id: Integer identifier of a label. instance_id: UUID of one instance of a joint. Keypoints with the same joint label that are visible on the same capture have different instance_id values. template_id: UUID of the keypoint template. pose: Pose label for that particular capture. keypoints: Array containing the properties of each keypoint. Each keypoint that exists in the keypoint template file is one element of the array. Each entry's contents have as follows:

index: Index of the keypoint in the keypoint template file. x: Pixel coordinates of the keypoint on the X axis. y: Pixel coordinates of the keypoint on the Y axis. state: State of the keypoint.

The SemanticSegmentationLabeler does not contain a values list.

egos.json: Contains collections of key-value pairs for each ego. These include:

id: UUID of the ego. description: Description of the ego. sensors.json: Contains collections of key-value pairs for all sensors of the simulation. These include:

id: UUID of the sensor. ego_id: UUID of the ego on which the sensor is attached. modality: Modality of the sensor (e.g., camera, radar, sonar). description: Description of the sensor (e.g., camera, radar).

Image names The RGB and semantic segmentation images share the same image naming convention. However, the semantic segmentation images also contain the string Semantic_ at the beginning of their filenames. Each RGB image is named "e_h_l_d_r.jpg", where:

e denotes the id of the environment. h denotes the id of the person. l denotes the id of the lighting condition. d denotes the camera distance at which the image was captured. r denotes the camera angle at which the image was captured.

The folder contains the train/ and individual test splits: test_05019_cohort_1/, test_05019_cohort_2/ and test_03557/ sub folders with the extracted patches from the ALDH1L1 stained tissue sections. Each of them have corresponding annotations with the exact same name.

test_*_cohort.json: consensus annotations corresponding to each subset test_*_*_*.json: individual junior, mid-level (medior) or expert test set annotations for the specified subset train.json: the train set expert annotations

This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level..

The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.

The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_

This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.

The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article.

Field experimentation roles:

The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.

Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'.

Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.

This Dataset contains HuBMap Dataset in COCO format to use in any Object Detection and Instance Segmentation Task.

COCO format easily supports Segmentation Frameworks such as AdelaiDet, Detectron2, TensorFlow etc.

The dataset is structured with images split into directories and no downscaling was done.

The following notebook explains how to convert custom annotations to COCO format:

https://www.kaggle.com/sreevishnudamodaran/build-custom-coco-annotations-512x512-tiled

Thanks to the Kaggle community and staff for all the support!

Please don't miss to upvote and comment if you like my work :)

Hope I everyone finds this useful!

Directory Structure:

   - coco_train
     - images(contains images in jpg format)
       - original_tiff_image_name
         - tile_column_number
           - image
           .
           .
           .
          .
          .
          .
        .
        .
        .
     - train.json (contains all the segmentation annotations in coco 
     -       format with proper relative path of the images)

This dataset includes 80 car images with annotated damages and damaged parts, split into training, validation, and testing folders. It features annotations for vehicle parts such as headlamp, front bumper, hood, door, and rear bumper, supporting AI model training for automated damage detection and assessment.

This dataset is a collection of manually and automatically annotated multispectral Images over Raphanus Raphanistrum infestations among Wheat crops.The images are categorized in two directories namely 'Manual' and 'Auotmated'. The sub-directory 'Manual' consists of manually acquired 85 images in .PNG format and annotations in COCO segmentation format titled region_data.json. Whereas, the sub-directory 'Automated' consists of 80 automatically annotated images in .JPG format and 80 annotation files in .XML Pascal VOC format.

The scientific framework of image acquisition and annotations are explained in the Data in Brief paper. This is just a prerequisite to the data article.

Roles:

The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. We are also in the process of articulating a data-in-brief article associated with this repository

Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data and annotation management and litmus testing of the datasets.

Database includes 1660 images of asbestos rock-chunks with asbestos veins taken in the different weather and day time conditions. All Data taken in the Bazhenovskoye field, Russia. All data are labeled for instance segmentation (as well as object detection and semantic segmentation) problems and have labeling in the COCO format. The archive contains both: all data in the images folder and annotation in the annotations folder. The labeling was performed manually in the CVAT software. The image size is 2592 × 2048.

"Towards Robotic Mapping of a Honeybee Comb" Dataset This dataset supports the analyses and experiments of the paper: J. Janota et al., "Towards Robotic Mapping of a Honeybee Comb," 2024 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), Delft, Netherlands, 2024, doi: 10.1109/MARSS61851.2024.10612712. Link to Paper | Link to Code Repository Cell Detection The celldet_2023 dataset contains a total of 260 images of the honeycomb (at resolution 67 µm per pixel), with masks from the ViT-H Segment Anything Model (SAM) and annotations for these masks. The structure of the dataset is following:celldet_2023├── {image_name}.png├── ...├── masksH (folder with masks for each image)├────{image_name}.json├────...├── annotations├────annotated_masksH (folder with annotations for training images)├──────{image_name in training part}.csv├──────...├────annotated_masksH_val (folder with annotations for validation images)├──────{image_name in validation part}.csv}├──────...├────annotated_masksH_test (folder with annotations for test images)├──────{image_name in test part}.csv}├──────... Masks For each image there is a .json file that contains all the masks produced by the SAM for the particular image, the masks are in COCO Run-Length Encoding (RLE) format. Annotations The annotation files are split into folders based on whether they were used for training, validation or testing. For each image (and thus also for each .json file with masks), there is a .csv file with two columns: Column id Description 0 order id of the mask in the corresponding .json file 1 mask label: 1 if fully visible cell, 2 if partially occluded cell, 0 otherwise Loading the Dataset For an example of loading the data, see the data loader in the paper repository: python cell_datasetV2.py --img_dir --mask_dir

I wanted to train a custom YOLO object detection model, but the MS-COCO dataset was not in a good format. So I parsed the instances json files in the MS-COCO annotations and processed the dataset to be a YOLO friendly format.

I downloaded the dataset from COCO webste. You can download any split you need from the COCO dataset website

Directory info: 1. test: Only contains the test images 2. train: Has two sub folders, images - contains the training images, labels - contains the training labels in a .txt file for each train image 3. val: Has two sub folders, images - contains the validation images, labels - contains the validation labels in a .txt file for each validation image

I do not own the dataset in any way. I merely parsed the dataset to a be in a ready to train YOLO format. Download the original dataset from the COCO webste

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations.

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD.

Zip file has 2 main folders: 1. dataset_final_phase , 2. dataset_phase_1

Structure of dataset_final_phase:

2 folders:

1. test cases stenosis with 300 images with annotations

2. test case segmentation with 300 images with annotations

Structure of dataset_phase_1:

1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations folder, where 200 XCA images are provided.

2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations folder, where 200 XCA images are provided.

The corresponding Dataset Article will be provided later.

[1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions

refcocoplus with COCO 2017 Image Paths

This dataset is a version of the original refcocoplus dataset that uses COCO 2017 image paths instead of COCO 2014.

  Changes from Original

Image paths updated from COCO 2014 format to COCO 2017 format Images loaded from COCO 2017 directory structure All other annotations remain unchanged

  Usage

from datasets import load_dataset

dataset = load_dataset("jhkwak-bp/refcocoplus-coco2017")

  Citation

Please cite the… See the full description on the dataset page: https://huggingface.co/datasets/jhkwak-bp/refcocoplus-coco2017.

This repository has VME dataset (images and annotations files). Also, it has the script for constructing CDSI dataset.

VME is a satellite imagery dataset built for vehicle detection in the Middle East. VME images (satellite_images folder) are under CC BY-NC-ND 4.0 license, whereas the rest of folders (annotations_HBB, annotations_OBB, CDSI_construction_scripts) are under CC BY 4.0 license.

VME_CDSI_datasets.zip has four components:

annotations_OBB: It holds TXT files in YOLO format with Oriented Bounding Box (OBB) annotations. Each annotation file is named after the corresponding image name

annotations_HBB: This component contains HBB annotation files in JSON file formatted in MS-COCO format defined by four values in pixels (x_min, y_min, width, height) of training, validation, and test splits

satellite_images: This folder consists of VME images of size 512x512 in PNG format

CDSI_construction_scripts: This directory comprises all instructions needed to build the CDSI dataset in detail: a) instructions for downloading each dataset from its repository, b) The conversion to MS-COCO format script for each dataset is under the dataset name folder, c) The combination instructions. The training, validation, and test splits are available under "CDSI_construction_scripts/data_utils" folder.

annotations_HBB, annotations_OBB, CDSI_construction_scripts, are available in our GitHub repository

Please cite our dataset & paper with the preferred format as shown in the "Citation" section

@article{al-emadi_vme_2025, title = {{VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond}}, volume = {12}, issn = {2052-4463}, url = {https://doi.org/10.1038/s41597-025-04567-y}, doi = {10.1038/s41597-025-04567-y}, pages = {500}, number = {1}, journal = {Scientific Data}, author = {Al-Emadi, Noora and Weber, Ingmar and Yang, Yin and Ofli, Ferda}, date = {2025-03-25}, publisher={Spring Nature}, year={2025} }

This page only provides the drone-view image dataset.

• For the ground-level image dataset, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I).
• For the point clouds, please visit SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III).

The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a color_palette.xlsx file, which contains the RGB_id and corresponding RGB values.

All files follow the naming convention: {central_tree_id}_{timestamp}, where {central_tree_id} represents the ID of the tree centered in the image, which is typically in a prominent position, and timestamp indicates the time when the data was collected.

Specifically, each 7z file includes the following folders:

• rgb: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36.

• depth_pfm: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.

• instance_segmentation: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps tree_id to RGB_id. The tree_id can be used to look up detailed information about each tree in obj_info_final.xlsx, while the RGB_id can be matched to the corresponding RGB values in color_palette.xlsx. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.

• obj_info_final.xlsx: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).

• landscape_info.txt: This file contains the ground location information within the scene, sampled every 0.5 meters.

For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets:

• {name}_coco.json: This file contains the annotation of each tree in the scene.
• {name}_filtered.json: This file is derived from the previous one, but filtering is applied to rule out overlapping instances.

⚠️: 7z files that begin with "!" indicate that the RGB values in the images within the instance_segmentation folder cannot be found in color_palette.xlsx. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in link1 and link2.