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.

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

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

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.

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).

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 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">

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

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 ...

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).

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.

Dataset

This dataset was created by deepanshu

Contents

MS COCO Object Detector

## Overview

MS COCO Object Detector is a dataset for object detection tasks - it contains Fruits annotations for 1,156 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 Dataset From YOLO

## Overview

COCO Dataset From YOLO is a dataset for object detection tasks - it contains Objects annotations for 9,330 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).

A dataset for object detection with various 1L + images of subjects and objects

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).

Dataset

This dataset was created by Techno Spider

Contents

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