YOLO Coco Data Format

## Overview

YOLO Coco Data Format is a dataset for object detection tasks - it contains Objects annotations for 692 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

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">

COCO (Common Objects in COntext) is a popular dataset in Computer Vision. It contains annotations for Computer Vision tasks - object detection, segmentation, keypoint detection, stuff segmentation, panoptic segmentation, densepose, and image captioning. For more details visit COCO Dataset

The Tensor Processing Unit (TPU) hardware accelerators are very fast. The challenge is often to feed them data fast enough to keep them busy. Google Cloud Storage (GCS) is capable of sustaining very high throughput but as with all cloud storage systems, initiating a connection costs some network back and forth. Therefore, having our data stored as thousands of individual files is not ideal. This dataset contains COCO dataset with object detection annotations in a smaller number of files and you can use the power of tf.data.Dataset to read from multiple files in parallel.

TFRecord file format Tensorflow's preferred file format for storing data is the protobuf-based TFRecord format. Other serialization formats would work too but you can load a dataset from TFRecord files directly by writing:

filenames = tf.io.gfile.glob(FILENAME_PATTERN) dataset = tf.data.TFRecordDataset(filenames) dataset = dataset.map(...)

For more details https://codelabs.developers.google.com/codelabs/keras-flowers-data/

You can use the following code in your kaggle notebook to get Google Cloud Storage (GCS) path of any public Kaggle dataset .

from kaggle_datasets import KaggleDatasets
GCS_PATH = KaggleDatasets().get_gcs_path()

View the notebook COCO Object Detection dataset in TFRecord to see how TFRecord files are created from the original COCO dataset.

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 filtered version of DOTA v 1 data set . It contains the annotations for the following classes - "plane", "ship", "storage-tank", "harbor", "bridge", "large-vehicle", "small-vehicle", "helicopter". Also the annotation format is changed into COCO format to support axis based bounding.

Original dataset: https://captain-whu.github.io/DOTA/ Original authors: Xia et al. (2018) — DOTA: A Large-scale Dataset for Object Detection in Aerial Images. License: CC BY-NC-SA 4.0

@inproceedings{xia2018dota, title={DOTA: A Large-scale Dataset for Object Detection in Aerial Images}, author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei}, booktitle={CVPR}, year={2018}, pages={3974--3983} }

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

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.

Images taken by AUVs, AE2000f and Tunasand, as part of the Falkor 2018 Explorative Robotics Expedition, labelled using the coco data format for 5 classes of organism - Crab, Hagfish, Rockfish, Sea Star, and Flat Fish.

This remarkable dataset of lunar images captured by the LRO Camera has been meticulously labeled in COCO format for object detection tasks in computer vision. The COCO annotation format provides a standardized way of describing objects in the images, including their locations and class labels, enabling machine learning algorithms to learn to recognize and detect objects in the images more accurately.

This dataset captures a wide variety of lunar features, including craters, mountains, and other geological formations, all labeled with precise and consistent COCO annotation. The dataset's comprehensive coverage of craters and other geological features on the Moon provides a treasure trove of data and insights into the evolution of our closest celestial neighbor.

The COCO annotation format is particularly well-suited for handling complex scenes with multiple objects, occlusions, and overlapping objects. With the precise labeling of objects provided by COCO annotation, this dataset enables researchers and scientists to train machine learning algorithms to automatically detect and analyze these features in large datasets.

In conclusion, this valuable dataset of lunar images labeled in COCO annotation format provides a powerful tool for research and discovery in the field of planetary science. With its comprehensive coverage and precise labeling of lunar features, it offers a wealth of data and insights into the evolution of the Moon's landscape, facilitating research and understanding of this enigmatic celestial body.

This dataset consists of five subsets with annotated images in COCO format, designed for object detection and tracking plant growth: 1. Cucumber_Train Dataset (for Faster R-CNN) - Includes training, validation, and test images of cucumbers from different angles. - Annotations: Bounding boxes in COCO format for object detection tasks.

1. Tomato Dataset
2. Contains images of tomato plants for 24 hours at hourly intervals from a fixed angle.

3. Annotations: Bounding boxes in COCO format.

4. Pepper Dataset

5. Contains images of pepper plants for 24 hours at hourly intervals from a fixed angle.

6. Annotations: Bounding boxes in COCO format.

7. Cannabis Dataset

8. Contains images of cannabis plants for 24 hours at hourly intervals from a fixed angle.

9. Annotations: Bounding boxes in COCO format.

10. Cucumber Dataset

11. Contains images of cucumber plants for 24 hours at hourly intervals from a fixed angle.

12. Annotations: Bounding boxes in COCO format.

This dataset supports training and evaluation of object detection models across diverse crops.

The IMPTOX project has received funding from the EU's H2020 framework programme for research and innovation under grant agreement n. 965173. Imptox is part of the European MNP cluster on human health.

More information about the project here.

Description: This repository includes the trained weights and a custom COCO-formatted dataset used for developing and testing a Faster R-CNN R_50_FPN_3x object detector, specifically designed to identify particles in micro-FTIR filter images.

Contents:

Weights File (neuralNetWeights_V3.pth):

Format: .pth

Description: This file contains the trained weights for a Faster R-CNN model with a ResNet-50 backbone and a Feature Pyramid Network (FPN), trained for 3x schedule. These weights are specifically tuned for detecting particles in micro-FTIR filter images.

Custom COCO Dataset (uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip):

Format: .zip

Description: This zip archive contains a custom COCO-formatted dataset, including JPEG images and their corresponding annotation file. The dataset consists of images of micro-FTIR filters with annotated particles.

Contents:

Images: JPEG format images of micro-FTIR filters.

Annotations: A JSON file in COCO format providing detailed annotations of the particles in the images.

Management: The dataset can be managed and manipulated using the Pycocotools library, facilitating easy integration with existing COCO tools and workflows.

Applications: The provided weights and dataset are intended for researchers and practitioners in the field of microscopy and particle detection. The dataset and model can be used for further training, validation, and fine-tuning of object detection models in similar domains.

Usage Notes:

The neuralNetWeights_V3.pth file should be loaded into a PyTorch model compatible with the Faster R-CNN architecture, such as Detectron2.

The contents of uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip should be extracted and can be used with any COCO-compatible object detection framework for training and evaluation purposes.

Code can be found on the related Github repository.

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.

Data abstract:
The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.)

Paper abstract:
The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%.

Dataset

The Man OverBoard Drone (MOBDrone) dataset is a large-scale collection of aerial footage images. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the mean sea level. Images are manually annotated with more than 180K bounding boxes localizing objects belonging to 5 categories --- person, boat, lifebuoy, surfboard, wood. More than 113K of these bounding boxes belong to the person category and localize people in the water simulating the need to be rescued.

In this repository, we provide:

66 Full HD video clips (total size: 5.5 GB)

126,170 images extracted from the videos at a rate of 30 FPS (total size: 243 GB)

3 annotation files for the extracted images that follow the MS COCO data format (for more info see https://cocodataset.org/#format-data):

annotations_5_custom_classes.json: this file contains annotations concerning all five categories; please note that class ids do not correspond with the ones provided by the MS COCO standard since we account for two new classes not previously considered in the MS COCO dataset --- lifebuoy and wood

annotations_3_coco_classes.json: this file contains annotations concerning the three classes also accounted by the MS COCO dataset --- person, boat, surfboard. Class ids correspond with the ones provided by the MS COCO standard.

annotations_person_coco_classes.json: this file contains annotations concerning only the 'person' class. Class id corresponds to the one provided by the MS COCO standard.

The MOBDrone dataset is intended as a test data benchmark. However, for researchers interested in using our data also for training purposes, we provide training and test splits:

Test set: All the images whose filename starts with "DJI_0804" (total: 37,604 images)

Training set: All the images whose filename starts with "DJI_0915" (total: 88,568 images)

More details about data generation and the evaluation protocol can be found at our MOBDrone paper: https://arxiv.org/abs/2203.07973 The code to reproduce our results is available at this GitHub Repository: https://github.com/ciampluca/MOBDrone_eval See also http://aimh.isti.cnr.it/dataset/MOBDrone

Citing the MOBDrone

The MOBDrone is released under a Creative Commons Attribution license, so please cite the MOBDrone if it is used in your work in any form. Published academic papers should use the academic paper citation for our MOBDrone paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people

@inproceedings{MOBDrone2021, title={MOBDrone: a Drone Video Dataset for Man OverBoard Rescue}, author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko Passera and Fabrizio Falchi}, booktitle={ICIAP2021: 21th International Conference on Image Analysis and Processing}, year={2021} }

and this Zenodo Dataset

@dataset{donato_cafarelli_2022_5996890, author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko Passera and Fabrizio Falchi}, title = {{MOBDrone: a large-scale drone-view dataset for man overboard detection}}, month = feb, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.5996890}, url = {https://doi.org/10.5281/zenodo.5996890} }

Personal works, such as machine learning projects/blog posts, should provide a URL to the MOBDrone Zenodo page (https://doi.org/10.5281/zenodo.5996890), though a reference to our MOBDrone paper would also be appreciated.

Contact Information

If you would like further information about the MOBDrone or if you experience any issues downloading files, please contact us at mobdrone[at]isti.cnr.it

Acknowledgements

This work was partially supported by NAUSICAA - "NAUtical Safety by means of Integrated Computer-Assistance Appliances 4.0" project funded by the Tuscany region (CUP D44E20003410009). The data collection was carried out with the collaboration of the Fly&Sense Service of the CNR of Pisa - for the flight operations of remotely piloted aerial systems - and of the Institute of Clinical Physiology (IFC) of the CNR - for the water immersion operations.

Dataset Overview

COCO-King is a large-scale dataset for reference-guided image completion tasks, derived from the COCO dataset. It features images with masked objects and corresponding reference images of those objects, enabling models to learn how to replace or complete masked regions with guidance from reference images.

Dataset Size and Structure

Total size: 690MB Images: 9,558 total images (8,134 training + 1,424 validation) Categories: 170 diverse object categories Directory structure:

coco-king/ ├── train/ │ ├── images/ # Original images with objects to be masked │ ├── mask/ # Binary masks (white background, black object) │ └── reference/ # Augmented reference images of masked objects ├── val/ │ ├── images/ # Validation images │ ├── mask/ # Validation masks │ └── reference/ # Validation reference images ├── metadata.json # Complete dataset metadata ├── train_annotations.json # COCO-format training annotations └── val_annotations.json # COCO-format validation annotations

Unique Features

Specially Curated Masks

Smoothed Contours: Each mask features smooth, rounded edges to mimic human-drawn masks rather than pixel-perfect segmentations

Processing Pipeline: Masks underwent morphological operations and Gaussian blurring to create natural-looking boundaries

Single Masked Object per Image: Each image has one primary object masked (the largest that meets size criteria), despite containing multiple objects (avg. 7 objects per image)

Rich Reference Images

Paint by Example Style Augmentations: Reference images are augmented similar to the Paint by Example paper:

Mild color jittering (brightness, contrast, saturation, hue) Random horizontal flips Small random rotations (up to 10 degrees) Mild perspective transformations Occasional equalization and auto-contrast

Balanced Object Selection

Size Range: Objects cover 0.89% to 42% of image area (average: ~25%) Multiple Objects: Every image contains multiple objects (ranging from 2 to 29) Diverse Categories: Well-distributed across 170 object categories

Dataset Highlights

• Person is the most common category (1,138 training, 184 validation)
• Top categories include sky, trees, clouds, road, grass, walls, buildings
• Average of 7 objects per image provides context and complexity
• Bounding boxes are strategically sized to be neither too small nor too dominant
• Each image-mask-reference triplet is carefully curated to ensure quality

Applications

This dataset is ideal for: Exemplar-based image inpainting/completion: Using reference images to guide the filling of masked regions Reference-guided object placement: Learning to place objects in scenes with proper perspective and lighting

Object replacement: Replacing objects in images with new objects while maintaining scene coherence

Style/appearance transfer: Learning to transfer appearance characteristics to objects in new scenes

Research on Paint by Example or similar architectures: Models that aim to fill masked regions based on reference images

Data Processing

Derived from COCO dataset with additional processing

Each image triplet (image, mask, reference) was processed to ensure: The masked object is of appropriate size Masks have smooth, natural contours Reference images maintain object identity while providing variation through augmentation

This dataset offers a unique resource for developing and benchmarking models that can intelligently replace or complete portions of images based on reference examples.

Large-scale Multi-modality Models Evaluation Suite

Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval

🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets

  This Dataset

This is a formatted version of RefCOCO. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models. @inproceedings{kazemzadeh-etal-2014-referitgame, title = "{R}efer{I}t{G}ame: Referring to Objects in… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/RefCOCO.

The Dataset

A collection of images of parking lots for vehicle detection, segmentation, and counting. Each image is manually labeled with pixel-wise masks and bounding boxes localizing vehicle instances. The dataset includes about 250 images depicting several parking areas describing most of the problematic situations that we can find in a real scenario: seven different cameras capture the images under various weather conditions and viewing angles. Another challenging aspect is the presence of partial occlusion patterns in many scenes such as obstacles (trees, lampposts, other cars) and shadowed cars. The main peculiarity is that images are taken during the day and the night, showing utterly different lighting conditions.

We suggest a three-way split (train-validation-test). The train split contains images taken during the daytime while validation and test splits include images gathered at night. In line with these splits we provide some annotation files:

train_coco_annotations.json and val_coco_annotations.json --> JSON files that follow the golden standard MS COCO data format (for more info see https://cocodataset.org/#format-data) for the training and the validation splits, respectively. All the vehicles are labeled with the COCO category 'car'. They are suitable for vehicle detection and instance segmentation.

train_dot_annotations.csv and val_dot_annotations.csv --> CSV files that contain xy coordinates of the centroids of the vehicles for the training and the validation splits, respectively. Dot annotation is commonly used for the visual counting task.

ground_truth_test_counting.csv --> CSV file that contains the number of vehicles present in each image. It is only suitable for testing vehicle counting solutions.

Citing our work

If you found this dataset useful, please cite the following paper

@inproceedings{Ciampi_visapp_2021, doi = {10.5220/0010303401850195}, url = {https://doi.org/10.5220%2F0010303401850195}, year = 2021, publisher = {{SCITEPRESS} - Science and Technology Publications}, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {Domain Adaptation for Traffic Density Estimation}, booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications} }

and this Zenodo Dataset

@dataset{ciampi_ndispark_6560823, author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato}, title = {{Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas}}, month = may, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.6560823}, url = {https://doi.org/10.5281/zenodo.6560823} }

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it

CarDD with YOLO Annotations (Images + Labels)

This dataset packages the Car Damage Detection (CarDD) images together with YOLO-format labels converted from the original COCO/SOD annotations.

• Images & original annotations: CarDD (PIC Lab, CAS).
• YOLO labels: Converted by Gabriel Fernandes Carvalho.
• Task: Object detection of car damage categories.
• Format: YOLO text files — each line is class x_center y_center width height (normalized).

Classes

• 0 dent
• 1 scratch
• 2 crack
• 3 glass shatter
• 4 lamp broken
• 5 tire flat

boulder_detection Dataset

An object detection dataset in YOLO format containing 3 splits: train, val, test.

  Dataset Metadata

License: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Version: 1.0 Date Published: 2025-05-16 Cite As: TBD

  Dataset Details

Format: YOLO

Splits: train, val, test

Classes: boulder

  Additional Formats

Includes COCO format annotations Includes Pascal VOC format annotations

  Data Format

This dataset… See the full description on the dataset page: https://huggingface.co/datasets/gremlin97/boulder_detection.

COCO Format dataset for SIIM Covid19 Object Detection Challenge

Challenge dataset format is dicom and it is then converted to numpy, resizing 512x512 in png format. The original csv file is also converted to coco format json file resulting train.json and val.json

In challenge dataeset, there are four classes namely Negative for Pneumonia, Typical Appearance, Indeterminate Appearance, Atypical Appearance. The bounding boxes are given the label 'opacity' in the competition data (image level) for all images and thus there is just a single class (class-0) in the cooc annotations.

I have just modeled the problem using just one class or one object. I have changed label 'opacity' to 'Covid_Abnormality' for convenience in the dataset.

train.json contains annotations for index 1 to 5000(total 5000) and val.json contains for index 5001 to 7852 (total 2852)

Code for converting 512x512png to coco format.

new_annotations folder contains annotation jsons files whose image names exclude '_image' and formatted with jpg

Example,

dataset/annotations/train.json --> "000a312787f2_image.png"

new_annotations/train.json --> "000a312787f2.jpg"

Acknowledgements

Challenge link --> https://www.kaggle.com/c/siim-covid19-detection Resized 512x512 png dataset link --> https://www.kaggle.com/sreevishnudamodaran/siim-covid19-512-images-and-metadata