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

The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.

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).

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

Coco Vehicle

## Overview

Coco Vehicle is a dataset for object detection tasks - it contains Person Cars annotations for 954 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).

coco2017

Image-text pairs from MS COCO2017.

  Data origin

Data originates from cocodataset.org While coco-karpathy uses a dense format (with several sentences and sendids per row), coco-karpathy-long uses a long format with one sentence (aka caption) and sendid per row. coco-karpathy-long uses the first five sentences and therefore is five times as long as coco-karpathy. phiyodr/coco2017: One row corresponds one image with several sentences. phiyodr/coco2017-long: One row… See the full description on the dataset page: https://huggingface.co/datasets/phiyodr/coco2017.

COCO Captions contains over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions are be provided for each image.

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.

This dataset contains all COCO 2017 images and annotations split in training (118287 images) and validation (5000 images).

Planes COCO

## Overview

Planes COCO is a dataset for object detection tasks - it contains Planes In Satellitte annotations for 500 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 Card for Coco Captions

This dataset is a collection of caption pairs given to the same image, collected from the Coco dataset. See Coco for additional information. This dataset can be used directly with Sentence Transformers to train embedding models. Note that two captions for the same image do not strictly have the same semantic meaning.

  Dataset Subsets





  pair subset

Columns: "caption1", "caption2" Column types: str, str Examples:{ 'caption1': 'A… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/coco-captions.

Verbs in COCO (V-COCO) is a dataset that builds off COCO for human-object interaction detection. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances. Each person has annotations for 29 action categories and there are no interaction labels including objects.

InpaintCOCO - Fine-grained multimodal concept understanding (for color, size, and COCO objects)

  Dataset Summary

A data sample contains 2 images and 2 corresponding captions that differ only in one object, the color of an object, or the size of an object.

Many multimodal tasks, such as Vision-Language Retrieval and Visual Question Answering, present results in terms of overall performance. Unfortunately, this approach overlooks more nuanced concepts, leaving us unaware… See the full description on the dataset page: https://huggingface.co/datasets/phiyodr/InpaintCOCO.

The COCO-Text dataset is a dataset for text detection and recognition. It is based on the MS COCO dataset, which contains images of complex everyday scenes. The COCO-Text dataset contains non-text images, legible text images and illegible text images. In total there are 22184 training images and 7026 validation images with at least one instance of legible text.

A BitTorrent file to download data with the title 'COCO 2017 Resized to 256x256'

COCO-QA is a dataset for visual question answering. It consists of:

123287 images 78736 train questions 38948 test questions 4 types of questions: object, number, color, location Answers are all one-word.

COCO-OOD dataset contains only unknown categories, consisting of 504 images with fine-grained annotations of 1655 unknown objects. All annotations consist of original annotations in COCO and the augmented annotations on the basis of the COCO definition.

COCO 2017 mirror

This is a just mirror of the raw COCO dataset files, for convenience. You have to download it using something like: pip install huggingface_hub

huggingface-cli download --local-dir coco-2017 pcuenq/coco-2017-mirror

And then unzip the files before use.

Tiny COCO

## Overview

Tiny COCO is a dataset for object detection tasks - it contains Coco Objects annotations for 5,025 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).

Microsoft COCO is a new image recognition, segmentation, and captioning dataset. Microsoft COCO has several features: Object segmentation Recognition in Context Multiple objects per image More than 300,000 images More than 2 Million instances 80 object categories 5 captions per image The 2014 Testing Images are for the MS COCO Captioning Challenge, while the 2015 Testing Images are for the MS COCO Detection Challenge. The train and val data are common to both challenges. Note also that as an alternative to downloading the large image zip files, individual images may be downloaded from the COCO website using the "coco_url" field specified in the image info struct.