This database contains 4976 planetary images of boulder fields located on Earth, Mars and Moon. The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024. The data was already splitted into train, validation and test datasets, but feel free to re-organize the labels at your convenience.

For each image, all of the boulder outlines within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013). This dataset differs from the previous dataset included along with the manuscript https://zenodo.org/records/8171052, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). In addition, the boulder outlines were also pre-processed so that it can be ingested directly in YOLOv8.

A description of what is what is given in the README.txt file (in addition in how to load the custom datasets in Detectron2 and YOLO). Most of the other files are mostly self-explanatory. Please see previous dataset or manuscript for more information. If you want to have more information about specific lunar and martian planetary images, the IDs of the images are still available in the name of the file. Use this ID to find more information (e.g., M121118602_00875_image.png, ID M121118602 ca be used on https://pilot.wr.usgs.gov/). I will also upload the raw data from which this pre-processed dataset was generated (see https://zenodo.org/records/14250970).

Thanks to this database, you can easily train a Detectron2 Mask R-CNN or YOLO instance segmentation models to automatically detect boulders.

How to cite:

Please refer to the "how to cite" section of the readme file of https://github.com/astroNils/YOLOv8-BeyondEarth.

Structure:

. └── boulder2024/ ├── jupyter-notebooks/ │ └── REGISTERING_BOULDER_DATASET_IN_DETECTRON2.ipynb ├── test/ │ └── images/ │ ├── _image.png │ ├── ... │ └── labels/ │ ├── _image.txt │ ├── ... ├── train/ │ └── images/ │ ├── _image.png │ ├── ... │ └── labels/ │ ├── _image.txt │ ├── ... ├── validation/ │ └── images/ │ ├── _image.png │ ├── ... │ └── labels/ │ ├── _image.txt │ ├── ... ├── detectron2_inst_seg_boulder_dataset.json ├── README.txt ├── yolo_inst_seg_boulder_dataset.yaml

detectron2_inst_seg_boulder_dataset.json

is a json file containing the masks as expected by Detectron2 (see https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html for more information on the format). In order to use this custom dataset, you need to register the dataset before using it in the training. There is an example how to do that in the jupyter-notebooks folder. You need to have detectron2, and all of its depedencies installed.

yolo_inst_seg_boulder_dataset.yaml

can be used as it is, however you need to update the paths in the .yaml file, to the test, train and validation folders. More information about the YOLO format can be found here (https://docs.ultralytics.com/datasets/segment/).

This collection contains the trained models and object detection results of 2 architectures found in the Detectron2 library, on the MS COCO val2017 dataset, under different JPEG compresion level Q = {5, 12, 19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89, 96} (14 levels per trained model). Architectures: F50 – Faster R-CNN on ResNet-50 with FPN R50 – RetinaNet on ResNet-50 with FPN Training type: D2 – Detectron2 Model ZOO pre-trained 1x model (90.000 iterations, batch 16) STD – standard 1x training (90.000 iterations) on original train2017 dataset Q20 – 1x training (90.000 iterations) on train2017 dataset degraded to Q=20 Q40 – 1x training (90.000 iterations) on train2017 dataset degraded to Q=40 T20 – extra 1x training on top of D2 on train2017 dataset degraded to Q=20 T40 – extra 1x training on top of D2 on train2017 dataset degraded to Q=40 Model and metrics files models_FasterRCNN.tar.gz (F50-STD, F50-Q20, …) models_RetinaNet.tar.gz (R50-STD, R50-Q20, …) For every model there are 3 files: config.yaml – the Detectron2 config of the model. model_final.pth – the weights (training snapshot) in PyTorch format. metrics.json – training metrics (like time, total loss, etc.) every 20 iterations. The D2 models were not included, because they are available from the Detectron2 Model ZOO, as faster_rcnn_R_50_FPN_1x (F50-D2) and retinanet_R_50_FPN_1x (R50-D2). Result files F50-results.tar.gz – results for Faster R-CNN models (inluding D2). R50-results.tar.gz – results for RetinaNet models (inluding D2). For every model there are 14 subdirectories, e.g. evaluator_dump_R50x1_005 through evaluator_dump_R50x1_096, for each of the JPEG Q values. Each such folder contains: coco_instances_results.json – all detected objects (image id, bounding box, class index and confidence). results.json – AP metrics as computed by COCO API. Source code for processing the data The data can be processed using our code, published at: https://github.com/tgandor/urban_oculus. Additional dependencies for the source code: COCO API Detectron2

This dataset includes all letters A through Z in American Sign Language labeled with polygon labels. See this blog post for how to train with Detectron2: https://blog.roboflow.com/p/4482cb2b-f378-48f6-bd58-df2b784670cf/

This project aims to train Custam Plant disease dataset on Faster RCNN using Detectron2

In the context of this project, the samples for µ-FTIR analysis contained up to a few thousands particles. The integrated particle detection tool (Particle Wizard - OMNIC Picta) gave poor performances and an AI segmentation tool was needed. Using this dataset, we trained a Detectron2 neural network that was used within GEPARD, an open source software used to improve Raman and FTIR target acquisition and data analysis. With Roboflow, it is possible to export this dataset to various format and use these data to train different architecture of segmentation neural networks.

Description: The Balloon Object Detection Dataset is a curated collection of images designed specifically for object detection tasks, focusing on balloons in various contexts. This dataset is formatted according to the COCO (Common Objects in Context) format, making it compatible with popular object detection frameworks and tools.

Dataset Contents: 1. Images: The dataset contains a diverse set of images featuring balloons in different settings such as parties, festivals, outdoor events, and indoor environments. Images are captured under various lighting conditions and perspectives to ensure the robustness of object detection models.

1. Annotations: Each image in the dataset is annotated with bounding boxes delineating the location of balloons. These annotations are provided in the COCO format, including class labels and bounding box coordinates. Additionally, annotations may include attributes such as balloon color, shape, and size to enrich the dataset for more advanced object detection tasks.

2. Metadata: Supplementary metadata may be included with the dataset, providing additional information about the images such as camera settings, image resolution, and any relevant contextual details.

Applications: - Object Detection: The Balloon Object Detection Dataset can be utilized to train and evaluate object detection models for detecting balloons in real-world scenarios. Applications include balloon counting, event monitoring, and safety surveillance. - Augmented Reality: Developers can use this dataset to create augmented reality applications where virtual balloons are overlaid onto real-world environments accurately. - Retail Analytics: Retailers and marketers can leverage object detection models trained on this dataset to track the presence and popularity of balloons in retail spaces, enabling data-driven decision-making for product placement and marketing strategies.

Compatibility: This dataset is provided in the COCO format, ensuring compatibility with a wide range of object detection frameworks and libraries, including but not limited to TensorFlow Object Detection API, Detectron2, and YOLO (You Only Look Once).

Acknowledgments: We would like to acknowledge the contributors and data sources that made this dataset possible. The images in this dataset are sourced from various publicly available datasets, and annotations are generated by a team of expert annotators to ensure accuracy and consistency.

Citation: If you use the Balloon Object Detection Dataset in your research or projects, please consider citing the dataset using the provided citation information to acknowledge the efforts of the creators and contributors. This helps support the ongoing maintenance and improvement of the dataset for the benefit of the community.

Note: This dataset is provided for research and educational purposes only. Users are encouraged to adhere to the terms of use and licensing agreements associated with the dataset and any included resources. Additionally, please respect the privacy and rights of individuals depicted in the images.