Cvat

## Overview

Cvat is a dataset for computer vision tasks - it contains 1 annotations for 386 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).

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).

CVAT Upload

## Overview

CVAT Upload is a dataset for object detection tasks - it contains 123 annotations for 1,370 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).

Overview

This dataset contains 8,992 images of Uno cards and 26,976 labeled examples on various textured backgrounds.

This dataset was collected, processed, and released by Roboflow user Adam Crawshaw, released with a modified MIT license: https://firstdonoharm.dev/

https://i.imgur.com/P8jIKjb.jpg" alt="Image example">

Use Cases

Adam used this dataset to create an auto-scoring Uno application:

Getting Started

Fork or download this dataset and follow our How to train state of the art object detector YOLOv4 for more.

Annotation Guide

See here for how to use the CVAT annotation tool.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Butterfly Wing VIS CVAT

## Overview

Butterfly Wing VIS CVAT is a dataset for instance segmentation tasks - it contains Wing 9Y6m annotations for 582 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).

Overview

This dataset contains 74 images of aerial maritime photographs taken with via a Mavic Air 2 drone and 1,151 bounding boxes, consisting of docks, boats, lifts, jetskis, and cars. This is a multi class problem. This is an aerial object detection dataset. This is a maritime object detection dataset.

The drone was flown at 400 ft. No drones were harmed in the making of this dataset.

This dataset was collected and annotated by the Roboflow team, released with MIT license.

https://i.imgur.com/9ZYLQSO.jpg" alt="Image example">

Use Cases

• Identify number of boats on the water over a lake via quadcopter.
• Boat object detection dataset
• Aerial Object Detection proof of concept
• Identify if boat lifts have been taken out via a drone
• Identify cars with a UAV drone
• Find which lakes are inhabited and to which degree.
• Identify if visitors are visiting the lake house via quad copter.
• Proof of concept for UAV imagery project
• Proof of concept for maritime project
• Etc.

This dataset is a great starter dataset for building an aerial object detection model with your drone.

Getting Started

Fork or download this dataset and follow our How to train state of the art object detector YOLOv4 for more. Stay tuned for particular tutorials on how to teach your UAV drone how to see and comprable airplane imagery and airplane footage.

Annotation Guide

See here for how to use the CVAT annotation tool that was used to create this dataset.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

The SmartBay Observatory in Galway Bay is an important contribution by Ireland to the growing global network of real-time data capture systems deployed within the ocean – technology giving us new insights into the ocean which we have not had before.

The observatory was installed on the seafloor 1.5km off the coast of Spiddal, County Galway, Ireland . The observatory uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. This observatory equipment allows ocean researchers unique real-time access to monitor ongoing changes in the marine environment. Data relating to the marine environment at the site is transferred in real-time from the SmartBay Observatory through a fibre optic telecommunications cable to the Marine Institute headquarters and onwards onto the internet. The data includes a live video stream, the depth of the observatory node, the sea temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water.

The Smartbay Marine Species Object Detection training Dataset is an initial Bounding Box Annotated image dataset used in attempting to Train a YOLOv8 Object Detection Model to classify the Marine Fauna observed in the Smartbay Observatory Video footage using species names.

The imagery used in this training dataset consists of image frame captures from the Smartbay video Archive files, CC-BY imagery from the www.minka-sdg.org website and images taken by Eva Cullen in the "Galway Atlantaquaria" Aquarium in Galway, Ireland.

The imagery were annotated using CVAT, collated on Roboflow and exported in YOLOv8 training dataset format.

Drone Image

## Overview

Drone Image is a dataset for object detection tasks - it contains Car Motorcycle Van Bus Lorry annotations for 9,029 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).

Summerschool_cvat

## Overview

Summerschool_cvat is a dataset for object detection tasks - it contains Flags annotations for 1,049 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).

This project is a product of the Hodin Lab at the University of Washington Friday Harbor Laboratories. It has the goal of developing a Pacific Northwest sea star instance segmentation and classification tool. Ultimately this tool will be used as a part of a sea star photo re-identification pipeline. Additionally, we hope this tool aids in the use of camera transect surveys of marine habitat.

The primary target species for this model is the Sunflower Seastar Pycnopodia helianthoides, the inclusion of other species is to make the model more robust to confusion species when deployed. For this reason, and our labs access to images of the Sunflower seastar it is over represented in the dataset.

If you have images of sea stars and wish to contribute to the project contact Willem @ willemlw@uw.edu

Our target number of annotated images is >10k with >100 annotated examples for each species.

We hope to have a future extension of this model which includes both star and prey annotations.

We are drawing images from a diverse set of sources including. iNaturalist Google search Collaborators Personal lab + field images boldsystems

We are using the annotation tool CVAT

Involved members are: Willem Lee Weertman Marilyn Duncan Ian Taylor Jason Hodin Brook Ashcraft

https://www.youtube.com/watch?v=4MA_6oZQz7s&ab_channel=tektronix475

Spotted caps, are the normal OK class (fully closed). Clean caps, are the bad or anomally target class (partially closed). One double prediction at 3:59. 100x100 classification accuracy, out of 200 samples. Inference over unseen test dataset. 150 epochs training. 700 samples training dataset, no data augmentation.

PREPROCESSING Auto-Orient: Applied Resize: Stretch to 416x416 Grayscale: Applied AUGMENTATIONS No augmentations were applied.

Anomaly detection with: Roboflow, tensorflow, google colab, Ultralytics, yolo v5, cvat,