Dataset

This dataset was created by Ari

Contents

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} }

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.

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.

Load COCO 2017 dataset Load any dataset in COCO format to Ikomia format. Then, any training algorithms from the Ikomia marketplace can be connected to this converter....

Original dataset credit: https://cocodataset.org/#home

Weapon COCO Format

## Overview

Weapon COCO Format is a dataset for object detection tasks - it contains Weapon Dataset annotations for 1,501 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 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">

IP102 COCO Format Annotations

This dataset contains preprocessed annotations for the IP102 Insect Pest Recognition Dataset converted to COCO format, making it ready for object detection models like DETR, Faster R-CNN, YOLO, and other modern detectors.

About IP102 Dataset

IP102 is a large-scale benchmark dataset for insect pest recognition containing: - 75,222 images of insect pests - 102 categories of agricultural pests - Images collected from real agricultural scenarios

What's Included

This dataset provides: - train_annotations.json - Training set annotations in COCO format - val_annotations.json - Validation set annotations in COCO format
- test_annotations.json (optional) - Test set annotations

Format Specification

Annotations follow the standard COCO Object Detection format: json { "images": [ { "id": 1, "file_name": "image_001.jpg", "width": 640, "height": 480 } ], "annotations": [ { "id": 1, "image_id": 1, "category_id": 5, "bbox": [x, y, width, height], "area": 12345, "iscrowd": 0 } ], "categories": [ { "id": 1, "name": "rice_leaf_roller", "supercategory": "insect" } ] }

Usage Example

import json
from pycocotools.coco import COCO

# Load annotations
with open('/kaggle/input/ip102-coco-annotations/train_annotations.json') as f:
  coco_data = json.load(f)

# Or use COCO API
coco = COCO('/kaggle/input/ip102-coco-annotations/train_annotations.json')

print(f"Number of images: {len(coco_data['images'])}")
print(f"Number of annotations: {len(coco_data['annotations'])}")
print(f"Number of categories: {len(coco_data['categories'])}")

🔗 Compatible With

• ✅ DETR (Detection Transformer)
• ✅ Faster R-CNN
• ✅ Mask R-CNN
• ✅ RetinaNet
• ✅ YOLOv5/v8 (with conversion)
• ✅ Detectron2
• ✅ Any framework supporting COCO format

Dataset Statistics

• Total Images: ~75,000
• Classes: 102 insect pest categories
• Format: COCO JSON
• Task: Object Detection / Instance Segmentation

Citation

If you use this dataset, please cite the original IP102 paper: @article{wu2019ip102, title={IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition}, author={Wu, Xiaoping and Zhan, Chi and Lai, Yu-Kun and Cheng, Ming-Ming and Yang, Jufeng}, journal={CVPR}, year={2019} }

📝 Notes

• Annotations created from original IP102 bounding box labels
• Validated for training modern object detection models
• Compatible with PyTorch, TensorFlow, and other frameworks
• Preprocessed to save computation time on Kaggle

Updates

• v1.0 (2025-01-XX): Initial release with train/val splits

Acknowledgments

Original dataset by Wu et al. (CVPR 2019). This is a format conversion for easier integration with modern detection frameworks.

Ready to train your insect detection model! 🐛🔍 ```

Tags (choisissez 5-10) :

object detection
computer vision
agriculture
coco format
insect recognition
pest detection
deep learning
detr
dataset
annotation

License:

CC BY-NC-SA 4.0 (same as original IP102)

ou ``` Database: Open Database, Contents: © Original Authors

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).

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).

Conversion Of Format And Classes To Coco

## Overview

Conversion Of Format And Classes To Coco is a dataset for object detection tasks - it contains Objects annotations for 7,460 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).

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 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 Dataset contains HuBMap Dataset in COCO format to use in any Object Detection and Instance Segmentation Task.

COCO format easily supports Segmentation Frameworks such as AdelaiDet, Detectron2, TensorFlow etc.

The dataset is structured with images split into directories and no downscaling was done.

The following notebook explains how to convert custom annotations to COCO format:

https://www.kaggle.com/sreevishnudamodaran/build-custom-coco-annotations-512x512-tiled

Thanks to the Kaggle community and staff for all the support!

Please don't miss to upvote and comment if you like my work :)

Hope I everyone finds this useful!

Directory Structure:

   - coco_train
     - images(contains images in jpg format)
       - original_tiff_image_name
         - tile_column_number
           - image
           .
           .
           .
          .
          .
          .
        .
        .
        .
     - train.json (contains all the segmentation annotations in coco 
     -       format with proper relative path of the images)

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).

Auto-annotate images with GroundingDINO and SAM models Auto-annotate images using a text prompt. GroundingDINO is employed for object detection (bounding boxes), followed by MobileSAM or SAM for segmentation. The annotations are then saved in both Pascal VOC format and COCO format....

Coco2yolo Format

## Overview

Coco2yolo Format is a dataset for object detection tasks - it contains Coco annotations for 8,405 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).

TACO: Trash Annotations in Context Dataset

From: Pedro F. Proença; Pedro Simões

• For more information, go to: http://tacodataset.org
• https://github.com/pedropro/TACO https://raw.githubusercontent.com/wiki/pedropro/TACO/images/logonav.png" alt="TACO Logo">

TACO is a growing image dataset of trash in the wild. It contains segmented images of litter taken under diverse environments: woods, roads and beaches. These images are manually labeled according to an hierarchical taxonomy to train and evaluate object detection algorithms. Annotations are provided in a similar format to COCO dataset.

The model in action:

https://raw.githubusercontent.com/wiki/pedropro/TACO/images/teaser.gif" alt="Gif of the model running inference">

Examples images from the dataset:

https://raw.githubusercontent.com/wiki/pedropro/TACO/images/2.png" alt="Example Image #2 from the Dataset"> https://raw.githubusercontent.com/wiki/pedropro/TACO/images/5.png" alt="Example Image #5 from the Dataset">

For more details and to cite the authors:

• Paper: https://arxiv.org/abs/2003.06975
• Paper Citation: @article{taco2020, title={TACO: Trash Annotations in Context for Litter Detection}, author={Pedro F Proença and Pedro Simões}, journal={arXiv preprint arXiv:2003.06975}, year=