The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.

Splits: The first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.

Based on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.

Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation.

What is COCO?

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

Tasks: Detection | DensePose | Keypoints | Stuff | Panoptic | Captions

Detection

1. Overview The COCO Object Detection Task is designed to push the state of the art in object detection forward. COCO features two object detection tasks: using either bounding box output or object segmentation output (the latter is also known as instance segmentation). For full details of this task please see the detection evaluation page. Note: only the detection task with object segmentation output will be featured at the COCO 2020 challenge (more details follow below).

This task is part of the Joint COCO and LVIS Recognition Challenge Workshop at ECCV 2020. For further details about the joint workshop please visit the workshop page. Researchers are encouraged to participate in both the COCO and LVIS Object Detection Tasks (the tasks share identical data formats and evaluation metrics). Please also see the related COCO keypoint, stuff, and panoptic tasks. Whereas the detection task addresses thing classes (person, car, elephant), the stuff task focuses on stuff classes (grass, wall, sky) and the newly introduced panoptic task addresses both simultaneously.

The COCO train, validation, and test sets, containing more than 200,000 images and 80 object categories, are available on the download page. All object instances are annotated with a detailed segmentation mask. Annotations on the training and validation sets (with over 500,000 object instances segmented) are publicly available.

This is the fifth iteration of the detection task and it exactly follows the COCO 2019 Object Detection Task. In particular, the same data, metrics, and guidelines are being used for this year's task. As in 2019 only the instance segmentation task will be featured at the challenge, with winners being invited to present at the workshop. For detection with bounding boxes outputs, researchers may continue to submit to test-dev and val on the evaluation server, but not to test-challenge, and results will not be presented at the workshop. As detection has steadily advanced, the purpose of this change is to encourage the community to focus on the more challenging and visually informative instance segmentation task.

1. Dates August 7, 2020 Submission deadline (11:59 PM PST) (1 week extension) August 10, 2020 Technical report submission deadline (11:59 PM PST) August 17, 2020 Challenge winners notified August 21, 2020 Presenter's slides and videos due (submitted to organizers) August 23, 2020 ECCV 2020 Workshop

2. New Rules and Awards Participants must submit a technical report that includes a detailed ablation study of their submission via CMT. For the technical report, use the following ECCV-based template. Suggested length of the report is 2-7 pages. The reports will be made public. This report will substitute the short text description that we requested previously. Only submissions with the report will be considered for any award and will be put in the COCO leaderboard. This year for each challenge track we will have two different awards: best result award and most innovative award. The most innovative award will be based on the method description in the submitted technical reports and decided by the COCO award committee. The commitee will invite teams to present at the workshop based on the innovations of the submissions rather than the best scores. This year we introduce single best paper award for the most innovative and successful solution across all challenges. The winner will be determined by the workshop organization committee.

3. Organizers Yin Cui (Google Research) Tsung-Yi Lin (Google Research) Matteo Ruggero Ronchi (Caltech) Alexander Kirillov (Facebook AI Research)

4. Award Committee Yin Cui (Google Research) Tsung-Yi Lin (Google Research) Alexander Kirillov (Facebook AI Research) Natalia Neverova (Facebook AI Research) Matteo Ru...

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

This model provides a high-efficiency computer vision resource focused exclusively on human detection by leveraging a specialized subset of the industry-standard COCO dataset. By isolating over 5,000 images dedicated to the "person" class, this pre-trained object detection model offers a streamlined and lightweight solution for applications where human localization is the primary objective.

Ways to Use COCO Dataset Limited (Person Only) Model

1. Intelligent Occupancy Monitoring: Integrate the model into HVAC and lighting control systems to automatically adjust building energy usage based on the real-time presence of people in specific rooms or zones.
2. Customer Flow Analytics: Deploy the model in retail environments to generate heatmaps of visitor traffic, helping store managers optimize layout designs and identify high-engagement product areas.
3. Privacy-Conscious Surveillance: Use this focused "person-only" detector as a pre-processing step for security systems, allowing the software to ignore non-human movement (like animals or blowing debris) to reduce false alarms.
4. Human-Robot Proximity Sensing: Empower collaborative industrial robots or service droids with the ability to detect personnel in their immediate vicinity, ensuring they maintain safe distances and halt operations when necessary.
5. Queue Management Systems: Implement the model at bank tellers, airport check-ins, or theme park lines to automatically estimate wait times and trigger alerts when additional staff are needed to handle person-density surges.

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

This dataset is a filtered subset of the MS COCO dataset, prepared specifically for object detection tasks using YOLO models.

It contains 25 commonly used object classes, each with approximately 300 images, and annotations converted into YOLO format.

Dataset Features: - 25 object classes from COCO - YOLO-format bounding box annotations - Ready-to-use data.yaml configuration file - Suitable for training YOLOv5, YOLOv8, and other detection models - Ideal for academic projects, experiments, and benchmarking

Selected Classes: person, bicycle, car, motorcycle, airplane, bus, train, truck, traffic light, stop sign, bench, bird, cat, dog, horse, cow, elephant, bottle, cup, bowl, pizza, cake, chair, couch, potted plant

Structure: detection/ ├── images/ │ ├── image_000001.jpg │ ├── image_000002.jpg ├── labels/ │ ├── image_000001.txt │ ├── image_000002.txt ├── data.yaml

Annotation Format: Each label file follows YOLO format: class_id x_center y_center width height

All values are normalized between 0 and 1.

names: 0: person 1: bicycle 2: car 3: motorcycle 4: airplane 5: bus 6: train 7: truck 8: traffic light 9: stop sign 10: bench 11: bird 12: cat 13: dog 14: horse 15: cow 16: elephant 17: bottle 18: cup 19: bowl 20: pizza 21: cake 22: chair 23: couch 24: potted plant

nc: 25

Dataset

This dataset was created by Om Lande

Released under Apache 2.0

Contents

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.

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">

Object Detection Coco

## Overview

Object Detection Coco is a dataset for object detection tasks - it contains Coco annotations for 206 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).

Context

The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 164K images.

This is the original version from 2014 made available here for easy access in Kaggle and because it does not seem to be still available on the COCO Dataset website. This has been retrieved from the mirror that Joseph Redmon has setup on this own website.

Content

The 2014 version of the COCO dataset is an excellent object detection dataset with 80 classes, 82,783 training images and 40,504 validation images. This dataset contains all this imagery on two folders as well as the annotation with the class and location (bounding box) of the objects contained in each image.

The initial split provides training (83K), validation (41K) and test (41K) sets. Since the split between training and validation was not optimal in the original dataset, there is also two text (.part) files with a new split with only 5,000 images for validation and the rest for training. The test set has no labels and can be used for visual validation or pseudo-labelling.

Acknowledgements

This is mostly inspired by Erik Linder-Norén and [Joseph Redmon](https://pjreddie.com/darknet/yolo

This is an open source object detection model by TensorFlow in TensorFlow Lite format. While it is not recommended to use this model in production surveys, it can be useful for demonstration purposes and to get started with smart assistants in ArcGIS Survey123. You are responsible for the use of this model. When using Survey123, it is your responsibility to review and manually correct outputs.This object detection model was trained using the Common Objects in Context (COCO) dataset. COCO is a large-scale object detection dataset that is available for use under the Creative Commons Attribution 4.0 License.The dataset contains 80 object categories and 1.5 million object instances that include people, animals, food items, vehicles, and household items. For a complete list of common objects this model can detect, see Classes.The model can be used in ArcGIS Survey123 to detect common objects in photos that are captured with the Survey123 field app. Using the modelFollow the guide to use the model. You can use this model to detect or redact common objects in images captured with the Survey123 field app. The model must be configured for a survey in Survey123 Connect.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputCamera feed (either low-resolution preview or high-resolution capture).OutputImage with common object detections written to its EXIF metadata or an image with detected objects redacted.Model architectureThis is an open source object detection model by TensorFlow in TensorFlow Lite format with MobileNet architecture. The model is available for use under the Apache License 2.0.Sample resultsHere are a few results from the model.

DamarJati/mini-NSFW-Object-Detection-coco dataset hosted on Hugging Face and contributed by the HF Datasets community

Helmet Detection Coco

## Overview

Helmet Detection Coco is a dataset for object detection tasks - it contains Objects annotations for 3,607 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

## 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).

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.

COCO Object Detection

## Overview

COCO Object Detection is a dataset for object detection tasks - it contains Objects annotations for 206 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).

Experimental results of the object detection task on the COCO dataset.

The MS COCO (Microsoft Common Objects in Context) 2014 dataset is a large-scale benchmark for object detection, segmentation, and key-point detection. It contains 164,000+ annotated images across 80 object categories.

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