Dataset: COCO-Formatted Object Detection Dataset

Overview

This dataset is designed for object detection tasks and follows the COCO format. It contains 300 images and corresponding annotation files in JSON format. The dataset is split into training, validation, and test sets, ensuring a balanced distribution for model evaluation.

Dataset Structure

The dataset is organized into three main folders:

train/ (70% - 210 images)

valid/ (15% - 45 images)

test/ (15% - 45 images)

Each folder contains:

Images in JPEG/PNG format.

A corresponding _annotations.coco.json file that includes bounding box annotations.

Preprocessing & Augmentations

The dataset has undergone several preprocessing and augmentation steps to enhance model generalization:

Image Preprocessing:

Auto-orientation applied

Resized to 640x640 pixels (stretched)

Augmentation Techniques:

Flip: Horizontal flipping

Crop: 0% minimum zoom, 5% maximum zoom

Rotation: Between -5° and +5°

Saturation: Adjusted between -4% and +4%

Brightness: Adjusted between -10% and +10%

Blur: Up to 0px

Noise: Up to 0.1% of pixels

Bounding Box Augmentations:

Flipping, cropping, rotation, brightness adjustments, blur, and noise applied accordingly to maintain annotation consistency.

Annotation Format

The dataset follows the COCO (Common Objects in Context) format, which includes:

images section: Contains image metadata such as filename, width, and height.

annotations section: Includes bounding boxes, category IDs, and segmentation masks (if applicable).

categories section: Defines class labels.

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.

Vehicles Coco

## Overview

Vehicles Coco is a dataset for object detection tasks - it contains Vehicles annotations for 18,998 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 annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to perform instance segmentation of Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 810 images (.jpg) of size 224x224 pixels. We also provide partitioning of the data into Train (567 images), Test (162 images), and Validation (81 images) subsets. Their annotations are provided in three different .json files following the COCO annotation format.

Context

Dataset with annotated fire

Content

This dataset is annotated in COCO format. But the csv file of annotation is deliberately not cleaned to give edge for learning. Check csv and compare with picture. Dont get hasty

Acknowledgements

Images collected from github repo: https://github.com/cair Annotated by LabelImg Converted by roboflow

Inspiration

To prevent massive accident from fire. Alerting potential threat of accident from live camera feed.

Dataset Description

This dataset has been converted to COCO format and contains bounding box annotations for content detection.

  Dataset Structure

The dataset is split into training and validation sets:

Training set: 583 images Validation set: 146 images

  Format

The dataset follows the COCO format with the following structure:

images: Contains the image files annotations.json: Contains the COCO format annotations dataset.yaml: Configuration file for training… See the full description on the dataset page: https://huggingface.co/datasets/zigg-ai/content-regions-1k-coco.

Context

The dataset contains car images with one or more damaged parts. The img/ folder has all 80 images in the dataset. There are three more folders train/, val/ and test/ for training, validation and testing purposes respectively.

Folders

train/: - Contains 59 images. - COCO_train_annos.json: Train annotation file for damages where damage is the one and only category. - COCO_mul_train_annos.json: Train annotation file for parts having damages. There are five categories of parts based on which part the damage has happened. The parts can be namely, headlamp, front_bumper, hood, door, rear_bumper.

val/: - Contains 11 images. - COCO_val_annos.json: Validation annotation file for damages where damage is the one and only category. - COCO_mul_val_annos.json: Validation annotation file for parts having damages. There are five categories of parts based on which part the damage has happened. The parts can be namely, headlamp, front_bumper, hood, door, rear_bumper.

test/: - Contains 8 images.

Annotation files have the following keys:

• "annotations": Contains the bounding box and segmentation array.
• "categories": Contains the list of categories in the annotation.
• "images": Details of each image used in the annotation.
• "info": Creator information
• "licenses": License information

Railway Track Coco Format

## Overview

Railway Track Coco Format is a dataset for object detection tasks - it contains Sleepers Fasteners Track annotations for 304 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 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.

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 Keypoint-Only subset from COCO 2017 Dataset. You can access the original COCO Dataset from here

This Dataset contains three folders: annotations, val2017, and train2017. - Contents in annotation folder is two jsons, for val dan train. Each jsons contains various informations, like the image id, bounding box, and keypoints locations. - Contents of val2017 and train2017 is various images that have been filtered. They are the images that have num_keypoints > 0 according to the annotation file.

conequest_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: 2025-05-11 Cite As: TBD

  Dataset Details

Format: YOLO

Splits: train, val, test

Classes: cone

  Additional Formats

Includes COCO format annotations Includes Pascal VOC format annotations

  Usage

from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/gremlin97/conequest_detection.

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.

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).

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\%.

Segmentation Data Subset

image_id: Refer to image_id on image_data subset segmentation_id: Segmentation identifier segmentation_information: COCO Format annotations: [[x1, y1, x2, y2, x3, y3, x4, y4]]

This dataset contains 4,599 high-quality, annotated images of 25 commonly used chemistry lab apparatuses. The images, each containing structures in real-world settings, have been captured from different angles, backgrounds, and distances, while also undergoing variations in lighting to aid in the robustness of object detection models. Every image has been labeled using bounding box annotation in YOLO and COCO format, alongside the class IDs and normalized bounding box coordinates making object detection more precise. The annotations and bounding boxes have been built using the Roboflow platform.To achieve a better learning procedure, the dataset has been split into three sub-datasets: training, validation, and testing. The training dataset constitutes 70% of the entire dataset, with validation and testing at 20% and 10% respectively. In addition, all images undergo scaling to a standard of 640x640 pixels while being auto-oriented to rectify rotation discrepancies brought about by the EXIF metadata. The dataset is structured in three main folders - train, valid, and test, and each contains images/ and labels/ subfolders. Every image contains a label file containing class and bounding box data corresponding to each detected object.The whole dataset features 6,960 labeled instances per 25 apparatus categories including beakers, conical flasks, measuring cylinders, test tubes, among others. The dataset can be utilized for the development of automation systems, real-time monitoring and tracking systems, tools for safety monitoring, alongside AI educational tools.

Real-world dataset of ~400 images of cuboid-shaped parcels with full 2D and 3D annotations in the COCO format.

Relevant computer vision tasks:

• bounding box detection
• instance segmentation
• keypoint estimation
• 3D bounding box estimation
• 3D voxel reconstruction (.binvox files)
• 3D reconstruction (.obj files)

For details, see our paper and project page.

If you use this resource for scientific research, please consider citing

@inproceedings{naumannScrapeCutPasteLearn2022,
  title    = {Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics},
  author    = {Naumann, Alexander and Hertlein, Felix and Zhou, Benchun and Dörr, Laura and Furmans, Kai},
  booktitle  = {{{IEEE Conference}} on {{Machine Learning}} and Applications ({{ICMLA}})},
  date     = 2022
}

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.