CVAT Coco

## Overview

CVAT Coco is a dataset for object detection tasks - it contains Defect Distance Event annotations for 9,899 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The global data annotation and labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market, estimated at $2 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $10 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of AI applications across diverse sectors such as automotive (autonomous driving), healthcare (medical image analysis), and finance (fraud detection) is creating an insatiable need for accurate and efficiently labeled data. Secondly, the advancement of deep learning techniques requires massive datasets, further boosting demand for annotation and labeling tools. Finally, the emergence of sophisticated tools offering automated and semi-supervised annotation capabilities is streamlining the process and reducing costs, making the technology accessible to a broader range of organizations. However, market growth is not without its challenges. Data privacy concerns and the need for robust data security protocols pose significant restraints. The high cost associated with specialized expertise in data annotation can also limit adoption, particularly for smaller companies. Despite these challenges, the market segmentation reveals opportunities. The automatic annotation segment is anticipated to grow rapidly due to its efficiency gains, while applications within the healthcare and automotive sectors are expected to dominate the market share, reflecting the considerable investment in AI across these industries. Leading players like Labelbox, Scale AI, and SuperAnnotate are strategically positioning themselves to capitalize on this growth by focusing on developing advanced tools, expanding their partnerships, and entering new geographic markets. The North American market currently holds the largest share, but the Asia-Pacific region is projected to experience the fastest growth due to increased investment in AI research and development across countries such as China and India.

The Tasmanian Orange Roughy Stereo Image Machine Learning Dataset is a collection of annotated stereo image pairs collected by a net-attached Acoustic and Optical System (AOS) during orange roughy (Hoplostethus atlanticus) biomass surveys off the northeast coast of Tasmania, Australia in July 2019. The dataset consists of expertly annotated imagery from six AOS deployments (OP12, OP16, OP20, OP23, OP24, and OP32), representing a variety of conditions including different fish densities, benthic substrates, and altitudes above the seafloor. Each image was manually annotated with bounding boxes identifying orange roughy and other marine species. For all annotated images, paired stereo images from the opposite camera have been included where available to enable stereo vision analysis. This dataset was specifically developed to investigate the effectiveness of machine learning-based object detection techniques for automating fish detection under variable real-world conditions, providing valuable resources for advancing automated image processing in fisheries science. Lineage: Data were obtained onboard the 32 m Fishing Vessel Saxon Onward during an orange roughy acoustic biomass survey off the northeast coast of Tasmania in July 2019. Stereo image pairs were collected using a net-attached Acoustic and Optical System (AOS), which is a self-contained autonomous system with multi-frequency and optical capabilities mounted on the headline of a standard commercial orange roughy demersal trawl. Images were acquired by a pair of Prosilica GX3300 Gigabyte Ethernet cameras with Zeiss F2.8 lenses (25 mm focal length), separated by 90 cm and angled inward at 7° to provide 100% overlap at a 5 m range. Illumination was provided by two synchronised quantum trio strobes. Stereo pairs were recorded at 1 Hz in JPG format with a resolution of 3296 x 2472 pixels and a 24-bit depth.

Human experts manually annotated images from the six deployments using both the CVAT annotation tool (producing COCO format annotations) and LabelImg tool (producing XML format annotations). Only port camera views were annotated for all deployments. Annotations included bounding boxes for "orange roughy" and "orange roughy edge" (for partially visible fish), as well as other marine species (brittle star, coral, eel, miscellaneous fish, etc.). Prior to annotation, under-exposed images were enhanced based on altitude above the seafloor using a Dark Channel Prior (DCP) approach, and images taken above 10 m altitude were discarded due to poor visibility.

For all annotated images, the paired stereo images (from the opposite camera) have been included where available to enable stereo vision applications. The dataset represents varying conditions of fish density (1-59 fish per image), substrate types (light vs. dark), and altitudes (2.0-10.0 m above seafloor), making it particularly valuable for training and evaluating object detection models under variable real-world conditions.

The final standardised COCO dataset contains 1051 annotated port-side images, 849 paired images (without annotations), and 14414 total annotations across 17 categories. The dataset's category distribution includes orange roughy (9887), orange roughy edge (2928), mollusc (453), cnidaria (359), misc fish (337), sea anemone (136), sea star (105), sea feather (100), sea urchin (45), coral (22), eel (15), oreo (10), brittle star (8), whiptail (4), chimera (2), siphonophore (2), and shark (1).

For a detailed description of this dataset, based on the Datasheets for Datasets (Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92.), check the VINEPICs_datasheet.md file.

For what purpose was the dataset created? VINEPICs was developed specifically for the purpose of detecting grape bunches in RGB images and facilitating tasks such as object detection, semantic segmentation, and instance segmentation. The detection of grape bunches serves as the initial phase in an analysis pipeline designed for vine plant phenotyping. The dataset encompasses a wide range of lighting conditions, camera orientations, plant defoliation levels, species variations, and cultivation methods. Consequently, this dataset presents an opportunity to explore the influence of each source of variability on grape bunch detection.

What do the instances that comprise the dataset represent? The dataset consists of RGB images showcasing various species of vine plants. Specifically, the images represent three different Vitis vinifera varieties: - Red Globe, a type of table grape - Cabernet Sauvignon, a red wine grape - Ortrugo, a white wine grape

These images have been collected over different years and dates at the vineyard facility of Università Cattolica del Sacro Cuore in Piacenza, Italy. You can find the images stored in the "data/images" directory, organized into subdirectories based on the starting time of data collection, indicating the day (and, if available, the approximate time in minutes). Images collected in 2022 are named using timestamps with nanosecond precision.

Is there a label or target associated with each instance? Each image has undergone manual annotation using the Computer Vision Annotation Tool (CVAT) (https://github.com/opencv/cvat). Grape bunches have been meticulously outlined with polygon annotations. These annotations belong to a single class, "bunch," and have been saved in a JSON file using the COCO Object Detection format, including segmentation masks (https://cocodataset.org/#format-data).

What mechanisms or procedures were used to collect the data? The data was collected using a D435 Intel Realsense camera, which was mounted on a four-wheeled skid-steering robot. The robot was teleoperated during the data collection process. The data was recorded by streaming the camera's feed into rosbag format. Specifically, the camera was connected via a USB 3.0 interface to a PC running Ubuntu 18.04 and ROS Melodic.

Overview: The Signs Segmentation (SS) Dataset comprises 300 images, each containing a single line of ordered Ancient Egyptian hieroglyphic signs. These images were automatically cropped from segmented lines within the HLA Dataset using our trained layout analysis models. The SS Dataset is specifically designed for the task of segmenting individual hieroglyphic signs within a line of text.

Data Generation and Annotation: The lines of hieroglyphs in the HLA Dataset were processed using our trained models to automatically extract individual lines. These cropped line images form the basis of the SS Dataset. Each image in this dataset was then manually annotated using the CVAT platform with polygonal segmentation masks for three distinct classes: “Left Sign”, “Right Sign”, and “Dual Sign” (representing ligatures or signs that visually merge) that present the orientation of signs.

Key Statistics:

Total Images: 300 Content: Single lines of ordered hieroglyphic signs Annotation Classes: 3 (“Left Sign”, “Right Sign”, “Dual Sign”) Annotation Type: Polygon Segmentation Masks Potential Uses: This dataset is well-suited for training and evaluating models for:

Individual hieroglyphic sign segmentation within a line of text. Distinguishing between individual and joined signs. Classify the orientation of signs. Json annotation files "in coco format":

Train: 272 images. Validation: 28 images. Test: 0 images.

The dataset consists of birds (Budgerigar) flying motion in a controlled environment. Trajectories of birds flying from one perch to another perch were recorded using video cameras. The dataset contains 355 clips of individual events of a bird's flying motion, along with annotated images and 3D data generated from the events.

Only the bird in motion was annotated and 3D trajectories were calculated for all five parts of the bird that were annotated. The annotations were done manually using Computer Vision Annotation Tool (CVAT). For generating the 3D trajectories of the bird's flight motion, Matlab (MathWorks®) was used. The files for all five 3D trajectories are labelled as such; point3D_bird for the bird’s body, point3D_head for the head of the bird, point3D_tail for the tail of the bird, point_3D_left_wing and point_3D_right_wing for the left wing and right wing of the bird respectively. The annotation's file format is JSON, and the format of the annotation is Microsoft COCO . The 3D coordinates for the bird's trajectories are in .mat file format.

Dataset folder structure:  clip_1  -->point3D_bird  -->point3D_head  -->point3D_tail  -->point3D_left_wing  -->point3D_right_wing  -->left.mp4  -->right.mp4  -->left  -->-->images (includes all the frames of clip 1 left)  -->-->annotations (includes .json file for annotation of clip 1 left)  -->right  -->-->images (includes all the frames of clip 1 right)  -->-->annotations (includes .json file for annotation of clip 1 right) . . .  clip_355

There are two zip files: 1. Budges355 (Random 10 clips) where you can randomly find 10 clips for previewing the dataset. 2. Budges355 (clip 1-355).zip where you can find full dataset