About:

The dataset was collected on the https://www.rapidata.ai platform and contains tens of thousands of human annotations of 70+ different kinds of objects. Rapidata makes it easy to collect manual labels in several data modalities with this repository containing freehand drawings on ~2000 images from the COCO dataset. Users are shown an image and are asked to paint a class of objects with a brush tool - there is always a single such object on the image, so the task is not… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/coco-human-inpainted-objects.

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">

COCO Dataset Limited (Person Only)

## Overview

COCO Dataset Limited (Person Only) is a dataset for object detection tasks - it contains People annotations for 5,438 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).

Coco Person

## Overview

Coco Person is a dataset for object detection tasks - it contains Coco Person annotations for 5,081 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 [MIT license](https://creativecommons.org/licenses/MIT).

coco.to is ranked #265422 in JP with 4.51K Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

The COCO dataset is a large dataset of labeled images and annotations. It is a popular dataset for machine learning and artificial intelligence research. The dataset consists of 330,000 images and 500,000 object annotations. The annotations include the bounding boxes of objects in the images, as well as the labels of the objects.

coco-diary.com is ranked #18339 in JP with 136.62K Traffic. Categories: . Learn more about website traffic, market share, and more!

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)

Coco Subset

## Overview

Coco Subset is a dataset for object detection tasks - it contains Object annotations for 7,953 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).

Dataset

This dataset was created by Samyak Dadda

Contents

Multitask COCO Dataset – Detection, Keypoints, and Segmentation

This publicly available Multitask COCO dataset has been preprocessed for seamless use in object detection, keypoint detection, and segmentation tasks. It enables multi-label annotations for COCO, ensuring robust performance across various vision applications. Special thanks to yermandy for providing access to multi-label annotations.

Optimized for deep learning models, this dataset is structured for easy integration into training pipelines, supporting diverse applications in computer vision research.

merve/coco dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Phan Nguyễn Hữu Phong

Released under Apache 2.0

Contents

coco.fr is ranked #55607 in FR with 18.19K Traffic. Categories: Retail. Learn more about website traffic, market share, and more!

Dataset

This dataset was created by Baligh Mnassri

Contents

Content

This is ready to use data with weights and configuration along with coco names to detect objects with YOLO algorithm.

🎓 Related Course for Detection Tasks

Training YOLO v3 for Objects Detection with Custom Data. Build your own detector by labelling, training and testing on image, video and in real time with camera. Join here: https://www.udemy.com/course/training-yolo-v3-for-objects-detection-with-custom-data/

🎥 Detections on images (example)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3400968%2Fb509b1629d8444ca2520512fb87813c0%2Fslides_detections_2_small.gif?generation=1583224702271176&alt=media" alt="Detections on Images" title="YOLO v3 Objects Detections on Images">

🚩 Concept Map of the Course

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3400968%2Fc51fc6aba2c0cd6d22512f486880868a%2FConcept_map_YOLO_3.png?generation=1584694252456677&alt=media" alt="Concept Map of the Course" title="Concept Map of the Course YOLO v3">

👉 Join the Course

https://www.udemy.com/course/training-yolo-v3-for-objects-detection-with-custom-data/

Acknowledgements

Trained weights and configuration file was taken from pjreddie.

@article{yolov3, title={YOLOv3: An Incremental Improvement}, author={Redmon, Joseph and Farhadi, Ali}, journal = {arXiv}, year={2018} }

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3400968%2Fa57f58b38e3caab6fbf72169895f5074%2Fresult.gif?generation=1585955236302060&alt=media" alt="">

Dataset

This dataset was created by Asrul Said

Contents

cjc/coco dataset hosted on Hugging Face and contributed by the HF Datasets community

The MS COCO (Microsoft Common Objects in Context) 2017 dataset is a large-scale benchmark for object detection, segmentation, key-point detection, and image captioning. It includes over 328K images with comprehensive annotations that drive advancements in computer vision research.

COCO Trash

## Overview

COCO Trash is a dataset for object detection tasks - it contains Trash annotations for 3,738 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).