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.

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

There are already a lot of datasets linked to computer vision tasks (Imagenet, MS COCO, Pascal VOC, OpenImages, and numerous others), but they all suffer from important bias. One bias of significance for us is the data origin: most datasets are composed of data coming from developed countries. Facing this situation, and the need of data with local context in developing countries, we try here to adapt common data generation process to inclusive data, meaning data drawn from locations and cultural context that are unseen or poorly represented. We chose to replicate MS COCO's data generation process, as it is well documented and easy to implement. Data was collected from January to April 2022 through Flickr platform. This dataset contains the results of our data collection process, as follows : 23 text files containing comma separated URLs for each of the 23 geographic zones identified in the UN M49 norm. These text files are named according to the names of the geographic zones they cover. Annotations for 400 images per geographic zones. Those annotations are COCO-style, and inform on the presence or absence of 91 categories of objects or concepts on the images. They are shared in a JSON format. Licenses for the 400 annotations per geographic zones, based on the original licenses of the data and specified per image. Those licenses are shared under CSV format. A document explaining the objectives and methodology underlying the data collection, also describing the different components of the dataset.

Tambahan Data Coco

## Overview

Tambahan Data Coco is a dataset for object detection tasks - it contains Motor Mobil annotations for 6,906 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.

Slicky325/COCO-data dataset hosted on Hugging Face and contributed by the HF Datasets community

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

SpeechCoco

Introduction

Our corpus is an extension of the MS COCO image recognition and captioning dataset. MS COCO comprises images paired with a set of five captions. Yet, it does not include any speech. Therefore, we used Voxygen's text-to-speech system to synthesise the available captions.

The addition of speech as a new modality enables MSCOCO to be used for researches in the field of language acquisition, unsupervised term discovery, keyword spotting, or semantic embedding using speech and vision.

Our corpus is licensed under a Creative Commons Attribution 4.0 License.

Data Set

This corpus contains 616,767 spoken captions from MSCOCO's val2014 and train2014 subsets (respectively 414,113 for train2014 and 202,654 for val2014).

We used 8 different voices. 4 of them have a British accent (Paul, Bronwen, Judith, and Elizabeth) and the 4 others have an American accent (Phil, Bruce, Amanda, Jenny).

In order to make the captions sound more natural, we used SOX tempo command, enabling us to change the speed without changing the pitch. 1/3 of the captions are 10% slower than the original pace, 1/3 are 10% faster. The last third of the captions was kept untouched.

We also modified approximately 30% of the original captions and added disfluencies such as "um", "uh", "er" so that the captions would sound more natural.

Each WAV file is paired with a JSON file containing various information: timecode of each word in the caption, name of the speaker, name of the WAV file, etc. The JSON files have the following data structure:

{ "duration": float, "speaker": string, "synthesisedCaption": string, "timecode": list, "speed": float, "wavFilename": string, "captionID": int, "imgID": int, "disfluency": list }

On average, each caption comprises 10.79 tokens, disfluencies included. The WAV files are on average 3.52 seconds long.

Repository

The repository is organized as follows:

CORPUS-MSCOCO (~75GB once decompressed)

train2014/ : folder contains 413,915 captions

json/

wav/

translations/

train_en_ja.txt

train_translate.sqlite3

train_2014.sqlite3

val2014/ : folder contains 202,520 captions

json/

wav/

translations/

train_en_ja.txt

train_translate.sqlite3

val_2014.sqlite3

speechcoco_API/

speechcoco/

init.py

speechcoco.py

setup.py

Filenames

.wav files contain the spoken version of a caption

.json files contain all the metadata of a given WAV file

.sqlite3 files are SQLite databases containing all the information contained in the JSON files

We adopted the following naming convention for both the WAV and JSON files:

imageID_captionID_Speaker_DisfluencyPosition_Speed[.wav/.json]

Script

We created a script called speechcoco.py in order to handle the metadata and allow the user to easily find captions according to specific filters. The script uses the *.db files.

Features:

Aggregate all the information in the JSON files into a single SQLite database

Find captions according to specific filters (name, gender and nationality of the speaker, disfluency position, speed, duration, and words in the caption). The script automatically builds the SQLite query. The user can also provide his own SQLite query.

The following Python code returns all the captions spoken by a male with an American accent for which the speed was slowed down by 10% and that contain "keys" at any position

create SpeechCoco object

db = SpeechCoco(train_2014.sqlite3, train_translate.sqlite3, verbose=True)

filter captions (returns Caption Objects)

captions = db.filterCaptions(gender="Male", nationality="US", speed=0.9, text='%keys%') for caption in captions: print(' {}\t{}\t{}\t{}\t{}\t{}\t\t{}'.format(caption.imageID, caption.captionID, caption.speaker.name, caption.speaker.nationality, caption.speed, caption.filename, caption.text))

... 298817 26763 Phil 0.9 298817_26763_Phil_None_0-9.wav A group of turkeys with bushes in the background. 108505 147972 Phil 0.9 108505_147972_Phil_Middle_0-9.wav Person using a, um, slider cell phone with blue backlit keys. 258289 154380 Bruce 0.9 258289_154380_Bruce_None_0-9.wav Some donkeys and sheep are in their green pens . 545312 201303 Phil 0.9 545312_201303_Phil_None_0-9.wav A man walking next to a couple of donkeys. ...

Find all the captions belonging to a specific image

captions = db.getImgCaptions(298817) for caption in captions: print(' {}'.format(caption.text))

Birds wondering through grassy ground next to bushes. A flock of turkeys are making their way up a hill. Um, ah. Two wild turkeys in a field walking around. Four wild turkeys and some bushes trees and weeds. A group of turkeys with bushes in the background.

Parse the timecodes and have them structured

input:

... [1926.3068, "SYL", ""], [1926.3068, "SEPR", " "], [1926.3068, "WORD", "white"], [1926.3068, "PHO", "w"], [2050.7955, "PHO", "ai"], [2144.6591, "PHO", "t"], [2179.3182, "SYL", ""], [2179.3182, "SEPR", " "] ...

output:

print(caption.timecode.parse())

... { 'begin': 1926.3068, 'end': 2179.3182, 'syllable': [{'begin': 1926.3068, 'end': 2179.3182, 'phoneme': [{'begin': 1926.3068, 'end': 2050.7955, 'value': 'w'}, {'begin': 2050.7955, 'end': 2144.6591, 'value': 'ai'}, {'begin': 2144.6591, 'end': 2179.3182, 'value': 't'}], 'value': 'wait'}], 'value': 'white' }, ...

Convert the timecodes to Praat TextGrid files

caption.timecode.toTextgrid(outputDir, level=3)

Get the words, syllables and phonemes between n seconds/milliseconds

The following Python code returns all the words between 0.2 and 0.6 seconds for which at least 50% of the word's total length is within the specified interval

pprint(caption.getWords(0.20, 0.60, seconds=True, level=1, olapthr=50))

... 404537 827239 Bruce US 0.9 404537_827239_Bruce_None_0-9.wav Eyeglasses, a cellphone, some keys and other pocket items are all laid out on the cloth. . [ { 'begin': 0.0, 'end': 0.7202778, 'overlapPercentage': 55.53412863758955, 'word': 'eyeglasses' } ] ...

Get the translations of the selected captions

As for now, only japanese translations are available. We also used Kytea to tokenize and tag the captions translated with Google Translate

captions = db.getImgCaptions(298817) for caption in captions: print(' {}'.format(caption.text))

# Get translations and POS
print('\tja_google: {}'.format(db.getTranslation(caption.captionID, "ja_google")))
print('\t\tja_google_tokens: {}'.format(db.getTokens(caption.captionID, "ja_google")))
print('\t\tja_google_pos: {}'.format(db.getPOS(caption.captionID, "ja_google")))
print('\tja_excite: {}'.format(db.getTranslation(caption.captionID, "ja_excite")))

Birds wondering through grassy ground next to bushes. ja_google: 鳥は茂みの下に茂った地面を抱えています。 ja_google_tokens: 鳥 は 茂み の 下 に 茂 っ た 地面 を 抱え て い ま す 。 ja_google_pos: 鳥/名詞/とり は/助詞/は 茂み/名詞/しげみ の/助詞/の 下/名詞/した に/助詞/に 茂/動詞/しげ っ/語尾/っ た/助動詞/た 地面/名詞/じめん を/助詞/を 抱え/動詞/かかえ て/助詞/て い/動詞/い ま/助動詞/ま す/語尾/す 。/補助記号/。 ja_excite: 低木と隣接した草深いグラウンドを通って疑う鳥。

A flock of turkeys are making their way up a hill. ja_google: 七面鳥の群れが丘を上っています。 ja_google_tokens: 七 面 鳥 の 群れ が 丘 を 上 っ て い ま す 。 ja_google_pos: 七/名詞/なな 面/名詞/めん 鳥/名詞/とり の/助詞/の 群れ/名詞/むれ が/助詞/が 丘/名詞/おか を/助詞/を 上/動詞/のぼ っ/語尾/っ て/助詞/て い/動詞/い ま/助動詞/ま す/語尾/す 。/補助記号/。 ja_excite: 七面鳥の群れは丘の上で進んでいる。

Um, ah. Two wild turkeys in a field walking around. ja_google: 野生のシチメンチョウ、野生の七面鳥 ja_google_tokens: 野生 の シチメンチョウ 、 野生 の 七 面 鳥 ja_google_pos: 野生/名詞/やせい の/助詞/の シチメンチョウ/名詞/しちめんちょう 、/補助記号/、 野生/名詞/やせい の/助詞/の 七/名詞/なな 面/名詞/めん 鳥/名詞/ちょう ja_excite: まわりで移動しているフィールドの2羽の野生の七面鳥

Four wild turkeys and some bushes trees and weeds. ja_google: 4本の野生のシチメンチョウといくつかの茂みの木と雑草 ja_google_tokens: 4 本 の 野生 の シチメンチョウ と いく つ か の 茂み の 木 と 雑草 ja_google_pos: 4/名詞/4 本/接尾辞/ほん の/助詞/の 野生/名詞/やせい の/助詞/の シチメンチョウ/名詞/しちめんちょう と/助詞/と

Llava recaptioned COCO2014 ValSet.

Used for text-to-image generation evaluaion. More detial can be found in What If We Recaption Billions of Web Images with LLaMA-3?

  Dataset Structure

"image_id" (str): COCO image id. "coco_url" (image): the COCO image url. "caption" (str): the original COCO caption. "recaption" (str): the llava recaptioned COCO caption.

  Citation

BibTeX: @article{li2024recapdatacomp, title={What If We Recaption Billions of Web Images with… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/Recap-COCO-30K.

Slice Coco Test Data

## Overview

Slice Coco Test Data is a dataset for object detection tasks - it contains Objects In Aerial Images annotations for 2,484 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 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).

Percept-Lens-COCO Captions Dataset Description Purpose. The Percept-Lens-COCO Captions dataset is curated as part of the Percept-Lens benchmark to evaluate the generalization capability of AI-generated image detectors under prompt-induced distribution shifts. It serves as an out-of-distribution (OOD) test set, designed to probe model robustness against semantically rich prompts not seen during training. Nature. This dataset contains only AI-generated images created from text prompts sourced from the COCO Captions Dataset. These prompts span abstract concepts, visual reasoning, compositional scenes, and imaginative queries. Images were generated using diverse diffusion models, including Stable Diffusion variants, and FLUX.variants, ensuring stylistic variation. No real images are included—each image is the synthetic output of a generative model responding to a high-level prompt. Scope. The dataset includes approximately 85,000 images, with each image linked to its original prompt. It is used exclusively for evaluation within the Percept-Lens benchmark and is not part of the training data. This subset is particularly useful for testing semantic generalization, where linguistic complexity drives visual synthesis beyond familiar domains.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Comprehensive dataset containing 1 verified CoCo Ichibanya locations in United States with complete contact information, ratings, reviews, and location data.

Original dataset description and JSON file structure: https://www.lvisdataset.org/dataset Best practices: https://www.lvisdataset.org/bestpractices

LVIS is based on the COCO 2017 dataset, that you can find here: https://www.kaggle.com/datasets/awsaf49/coco-2017-dataset Image data is the same as COCO 2017, the difference is in the annotations.

LVIS has annotations for instance segmentation in a format similar to COCO. The annotations are stored using JSON. The LVIS API can be used to access and manipulate annotations. Each image now comes with two additional fields. not_exhaustive_category_ids : List of category ids which don't have all of their instances marked exhaustively. neg_category_ids : List of category ids which were verified as not present in the image. coco_url : Image URL. The last two path elements identify the split in the COCO dataset and the file name (e.g., http://images.cocodataset.org/train2017/000000391895.jpg). This information can be used to load the correct image from your downloaded copy of the COCO dataset. Categories LVIS categories are loosely based on WordNet synsets. synset : Provides a unique string identifier for each category. Loosely based on WordNet synets. synonyms : List of object names that belong to the same synset. def : The meaning of the synset. Most of the meanings are derived from WordNet. image_count : Number of images in which the category is annotated. instance_count : Number of annotated instances of the category. frequency : We divide the categories into three buckets based on image_count in the train set.

Coco Conversion Of Data

## Overview

Coco Conversion Of Data is a dataset for instance segmentation tasks - it contains Pets XmF8 annotations for 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).

This dataset provides information about the number of properties, residents, and average property values for Coco Bed Road cross streets in Cloutierville, LA.

26330 Global import shipment records of Coco with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset provides information about the number of properties, residents, and average property values for Coco Place cross streets in Littleton, CO.

This dataset provides information about the number of properties, residents, and average property values for Coco Palm Drive cross streets in Tustin, CA.