detection-datasets/coco dataset hosted on Hugging Face and contributed by the HF Datasets community

This is the full 2017 COCO object detection dataset (train and valid), which is a subset of the most recent 2020 COCO object detection dataset.

COCO is a large-scale object detection, segmentation, and captioning dataset of many object types easily recognizable by a 4-year-old. The data is initially collected and published by Microsoft. The original source of the data is here and the paper introducing the COCO dataset is here.

COCO Object Detection Dataset | 2017

Downloaded from here and it includes Train images for now.

This dataset contains 1028 images each 640x380 pixels. The dataset is split into 249 test and 779 training examples. Every image comes with MS COCO format annotations. The dataset was collected in Carla Simulator, driving around in autopilot mode in various environments (Town01, Town02, Town03, Town04, Town05) and saving every i-th frame. The labels where then automatically generated using the semantic segmentation information.

COCO Detection

## Overview

COCO Detection is a dataset for object detection tasks - it contains Car Dog Cat Person annotations for 411 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 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.

Microsoft Common Objects in Context (COCO) Dataset

The Common Objects in Context (COCO) dataset is a widely recognized collection designed to spur object detection, segmentation, and captioning research. Created by Microsoft, COCO provides annotations, including object categories, keypoints, and more. The model it a valuable asset for machine learning practitioners and researchers. Today, many model architectures are benchmarked against COCO, which has enabled a standard system by which architectures can be compared.

While COCO is often touted to comprise over 300k images, it's pivotal to understand that this number includes diverse formats like keypoints, among others. Specifically, the labeled dataset for object detection stands at 123,272 images.

The full object detection labeled dataset is made available here, ensuring researchers have access to the most comprehensive data for their experiments. With that said, COCO has not released their test set annotations, meaning the test data doesn't come with labels. Thus, this data is not included in the dataset.

The Roboflow team has worked extensively with COCO. Here are a few links that may be helpful as you get started working with this dataset:

• An introduction to the COCO dataset
• Weird images in COCO, and what that tells us about the utility and limits of COCO

Dataset

This dataset was created by Om Lande

Released under Apache 2.0

Contents

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

MJ-COCO-2025 is a modified version of the MS-COCO-2017 dataset, in which the annotation errors have been automatically corrected using model-driven methods. The name "MJ" originates from the initials of Min Je Kim, the individual who updated the dataset. "MJ" also stands for "Modification & Justification," emphasizing that the modifications were not manually edited but were systematically validated through machine learning models to increase reliability and quality. Thus, MJ-COCO-2025 reflects both a personal identity and a commitment to improving the dataset through thoughtful modification, ensuring improved accuracy, reliability and consistency. The comparative results of MS-COCO and MJ-COCO datasets are presented in Table 1 and Figure 1. The MJ-COCO-2025 dataset features the improvements, including fixes for group annotations, addition of missing annotations, removal of redundant or overlapping labels, etc. These refinements aim to improve training and evaluation performance in object detection tasks.

Summary of Improvements:

The re-labeled MJ-COCO-2025 dataset exhibits notable improvements in annotation quality compared to the original MS-COCO-2017 dataset. As shown in Table 1, it includes substantial increases in categories such as previously missing annotations and group annotations. At the same time, the dataset has been refined by reducing annotation noise through the removal of duplicates, resolution of challenging or debatable cases, and elimination of non-existent object annotations.

Table 1: Comparison of Class-wise Annotations: MS-COCO-2017 and MJ-COCO-2025. Class Names | MS-COCO | MJ-COCO | Difference | Class Names | MS-COCO | MJ-COCO | Difference ---------------------|---------|---------|------------|----------------------|---------|---------|------------ Airplane | 5,135 | 5,810 | 675 | Kite | 9,076 | 15,092 | 6,016 Apple | 5,851 | 19,527 | 13,676 | Knife | 7,770 | 6,697 | -1,073 Backpack | 8,720 | 10,029 | 1,309 | Laptop | 4,970 | 5,280 | 310 Banana | 9,458 | 49,705 | 40,247 | Microwave | 1,673 | 1,755 | 82 Baseball Bat | 3,276 | 3,517 | 241 | Motorcycle | 8,725 | 10,045 | 1,320 Baseball Glove | 3,747 | 3,440 | -307 | Mouse | 2,262 | 2,377 | 115 Bear | 1,294 | 1,311 | 17 | Orange | 6,399 | 18,416 | 12,017 Bed | 4,192 | 4,177 | -15 | Oven | 3,334 | 4,310 | 976 Bench | 9,838 | 9,784 | -54 | Parking Meter | 1,285 | 1,355 | 70 Bicycle | 7,113 | 7,853 | 740 | Person | 262,465 | 435,252 | 172,787 Bird | 10,806 | 13,346 | 2,540 | Pizza | 5,821 | 6,049 | 228 Boat | 10,759 | 13,386 | 2,627 | Potted Plant | 8,652 | 11,252 | 2,600 Book | 24,715 | 35,712 | 10,997 | Refrigerator | 2,637 | 2,728 | 91 Bottle | 24,342 | 32,455 | 8,113 | Remote | 5,703 | 5,428 | -275 Bowl | 14,358 | 13,591 | -767 | Sandwich | 4,373 | 3,925 | -448 Broccoli | 7,308 | 14,275 | 6,967 | Scissors | 1,481 | 1,558 | 77 Bus | 6,069 | 7,132 | 1,063 | Sheep | 9,509 | 12,813 | 3,304 Cake | 6,353 | 8,968 | 2,615 | Sink | 5,610 | 5,969 | 359 Car | 43,867 | 51,662 | 7,795 | Skateboard | 5,543 | 5,761 | 218 Carrot | 7,852 | 15,411 | 7,559 | Skis | 6,646 | 8,945 | 2,299 Cat | 4,768 | 4,895 | 127 | Snowboard | 2,685 | 2,565 | -120 Cell Phone | 6,434 | 6,642 | 208 | Spoon | 6,165 | 6,156 | -9 Chair | 38,491 | 56,750 | 18,259 | Sports Ball | 6,347 | 6,060 | -287 Clock | 6,334 | 7,618 | 1,284 | Stop Sign | 1,983 | 2,684 | 701 Couch | 5,779 | 5,598 | -181 | Suitcase | 6,192 | 7,447 | 1,255 Cow | 8,147 | 8,990 | 843 | Surfboard | 6,126 | 6,175 | 49 Cup | 20,650 | 22,545 | 1,895 | Teddy Bear | 4,793 | 6,432 | 1,639 Dining Table | 15,714 | 16,569 | 855 | Tennis Racket | 4,812 | 4,932 | 120 Dog | 5,508 | 5,870 | 362 | Tie | 6,496 | 6,048 | -448 Donut | 7,179 | 11,622 | 4,443 ...

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).

MS COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.

Dataset

This dataset was created by deepanshu

Contents

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">

10 Coco Detection

## Overview

10 Coco Detection is a dataset for object detection tasks - it contains Coco Objects annotations for 1,434 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).

Vehicles Coco Dataset

## Overview

Vehicles Coco Dataset is a dataset for object detection tasks - it contains Vehicles annotations for 9,629 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).

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.

COCO

## Overview

COCO is a dataset for object detection tasks - it contains Coco annotations for 5,000 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).

Frontal Face(Coco)

## Overview

Frontal Face(Coco) is a dataset for object detection tasks - it contains Faces annotations for 30 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset is a filtered subset of the COCO 2017 dataset containing only the 'cat' class. The images and annotations are optimized for training object detection models