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 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.

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.

segmentation

A large-scale dataset for object detection and instance segmentation.

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.

The COCO dataset is a foundational large-scale benchmark for object detection, segmentation, captioning, and keypoint analysis. Created by Microsoft, it features complex everyday scenes with common objects in their natural contexts. With over 330,000 images and 2.5 million labeled instances, it has become the gold standard for training and evaluating computer vision models.

File Information

images/
Contains 2 subdirectories split by usage:
train2017/: Main training set (118K images)
val2017/: Validation set (5K images)
File Naming: 000000000009.jpg (12-digit zero-padded IDs)
Formats: JPEG images with varying resolutions (average 640×480)

annotations/
Contains task-specific JSON files with consistent naming:
captions_*.json: 5 human-generated descriptions per image

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.

Context

This dataset contains pickled Python objects with data from the annotations of the Microsoft (MS) COCO dataset. COCO is a large-scale object detection, segmentation, and captioning dataset.

Content

Except for the objs file, which is a plain text file continuing a list of objects, the data in this dataset is all in the pickle format, a way of storing Python objects at binary data files.

Important: These pickles were pickled using Python 2. Since Kernels use Python 3, you will need to specify the encoding when unpickling these files. The Python utility scripts here have been updated to correctly unpickle these files.

# the correct syntax to read these pickled files into Python 3
pickle.load(open('file_path, 'rb'), encoding = "latin1")

Acknowledgements

As a derivative of the original COCO dataset, this dataset is distributed under a CC-BY 4.0 license. These files were distributed as part of the supporting materials for Zhao et al 2017. If you use these files in your work, please cite the following paper:

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K. W. (2017). Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 2979-2989).

COCO Dataset

## Overview

COCO Dataset is a dataset for instance segmentation tasks - it contains Common Objects annotations for 123,272 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.

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.

The COCO dataset is a large-scale dataset for object detection and image classification.

Purpose: experiments with YOLO models in monochrome.

The original COCO2017 dataset has been processed: - added YOLO annotations for 80 classes; - all images are converted to monochrome (greyscale) with an equalized histogram.

The number of images: - training: 118,287; - validation: 5,000.

Links to the original COCO 2017 dataset https://cocodataset.org by Microsoft: url_images = 'http://images.cocodataset.org/zips/' url_annotations = 'http://images.cocodataset.org/annotations/annotations_trainval2017.zip'

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

This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes.

COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes.

We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using wordnet

This repository contains the following files:

coco_classes_map.txt, contains the mapping for the 80 coco classes

lvis_classes_map.txt, contains the mapping for the 1460 coco classes

openimages_classes_map.txt, contains the mapping for the 601 coco classes

classname_hyperset_definition.csv, contains the final set of 1460 classes, their definition and hierarchy

all-classnames.xlsx, contains a side-by-side view of all classes considered

This mapping was used in VISIONE [Amato et al. 2021, Amato et al. 2022] that is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). For the object detection VISIONE uses three pre-trained models: VfNet Zhang et al. 2021, Mask R-CNN He et al. 2017, and a Faster R-CNN+Inception ResNet (trained on the Open Images V4).

This is repository is released under a Creative Commons Attribution license, please cite the following paper if you use it in your work in any form:

@inproceedings{amato2021visione, title={The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Debole, Franca and Falchi, Fabrizio and Gennaro, Claudio and Vadicamo, Lucia and Vairo, Claudio}, journal={Journal of Imaging}, volume={7}, number={5}, pages={76}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }

References:

[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_52

[Amato et al. 2021] Amato, G., Bolettieri, P., Carrara, F., Debole, F., Falchi, F., Gennaro, C., Vadicamo, L. and Vairo, C., 2021. The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. Journal of Imaging, 7(5), p.76.

[Gupta et al.2019] Gupta, A., Dollar, P. and Girshick, R., 2019. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5356-5364).

[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).

[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.

[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.

[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8514-8523).

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

Microsoft COCO 2017 Dataset

## Overview

Microsoft COCO 2017 Dataset is a dataset for object detection tasks - it contains Coco Objects annotations for 2,245 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).

Source: This dataset is a subset of the MS COCO dataset, originally released by Microsoft under the CC BY 4.0 License. This subset was extracted for educational and research purposes.

What is COCO?

COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features:

MS-COCO website

Microsoft COCO Pose Detection

## Overview

Microsoft COCO Pose Detection is a dataset for computer vision tasks - it contains Objects annotations for 5,105 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.