Dataset Highlights:

1. Diverse Image Collection: Our dataset encompasses a wide range of general images covering various categories such as objects, scenes, people, and more. The images are carefully curated to offer a rich source of visual data.

2. Sindhi Language Titles: One of the distinctive features of our dataset is the inclusion of Sindhi language titles for each image.

3. Annotations in YOLO Format: To facilitate your object detection tasks, we have meticulously annotated the images in YOLO format, making it compatible with the YOLOv3 or YOLOv4 models. This ensures that you can jump right into training your model without the hassle of converting annotations.

4. Comprehensive Metadata: Each image in the dataset is accompanied by a YAML file providing additional metadata, including information about the image source, date of capture, and any relevant context that may be useful for your research.

By publishing this YOLO-style dataset with Sindhi language titles, we aim to contribute to the machine learning and computer vision community, fostering innovation and inclusivity in the field. We encourage you to explore, experiment, and create cutting-edge models using this dataset, and we look forward to seeing the incredible projects that emerge from it.

Mechanical Parts Dataset

The dataset consists of a total of 2250 images obtained by downloading from various internet platforms. Among the images in the dataset, there are 714 images with bearings, 632 images with bolts, 616 images with gears and 586 images with nuts. A total of 10597 manual labeling processes were carried out in the dataset, including 2099 labels belonging to the bearing class, 2734 labels belonging to the bolt class, 2662 labels belonging to the gear class and 3102 labels belonging to the nut class.

Folder Content

The created dataset is divided into 3 as 80% train, 10% validation and 10% test. In the "Mechanical Parts Dataset" folder, there are three separate folders as "train", "test" and "val". In each of these three folders there are folders named "images" and "labels". Images are kept in the "images" folder and tag information is kept in the "labels" folder.

Finally, inside the folder there is a yaml file named "mech_parts_data" for the Yolo algorithm. This file contains the number of classes and class names.

Images and Labels

The dataset was prepared in accordance with the Yolov5 algorithm.
For example, the tag information of the image named "2a0xhkr_jpg.rf.45a11bf63c40ad6e47da384fdf6bb7a1.jpg" is stored in the txt file with the same name. The tag information (coordinates) in the txt file are as follows: "class x_center y_center width height".

Update 05.01.2023

***Pascal voc and coco json formats have been added.***

Related paper: doi.org/10.5281/zenodo.7496767

The Fish Detection AI project aims to improve the efficiency of fish monitoring around marine energy facilities to comply with regulatory requirements. Despite advancements in computer vision, there is limited focus on sonar images, identifying small fish with unlabeled data, and methods for underwater fish monitoring for marine energy. A YOLO (You Only Look Once) computer vision model was developed using the Eyesea dataset (optical) and sonar images from Alaska Fish and Games to identify fish in underwater environments. Supervised methods were used within YOLO to detect fish based on training using labeled data of fish. These trained models were then applied to different unseen datasets, aiming to reduce the need for labeling datasets and training new models for various locations. Additionally, hyper-image analysis and various image preprocessing methods were explored to enhance fish detection. In this research we achieved: 1. Enhanced YOLO Performance, as compared to a published article (Xu, Matzner 2018) using earlier yolo versions for fish object identification. Specifically, we achieved a best mean Average Precision (mAP) of 0.68 on the Eyesea optical dataset using YOLO v8 (medium-sized model), surpassing previous YOLO v3 benchmarks from that previous article publication. We further demonstrated up to 0.65 mAP on unseen sonar domains by leveraging a hyper-image approach (stacking consecutive frames), showing promising cross-domain adaptability. This submission of data includes: - The actual best-performing trained YOLO model neural network weights, which can be applied to do object detection (PyTorch files, .pt). These are found in the Yolo_models_downloaded zip file - Documentation file to explain the upload and the goals of each of the experiments 1-5, as detailed in the word document (named "Yolo_Object_Detection_How_To_Document.docx") - Coding files, namely 5 sub-folders of python, shell, and yaml files that were used to run the experiments 1-5, as well as a separate folder for yolo models. Each of these is found in their own zip file, named after each experiment - Sample data structures (sample1 and sample2, each with their own zip file) to show how the raw data should be structured after running our provided code on the raw downloaded data - link to the article that we were replicating (Xu, Matzner 2018) - link to the Yolo documentation site from the original creators of that model (ultralytics) - link to the downloadable EyeSea data set from PNNL (instructions on how to download and format the data in the right way to be able to replicate these experiments is found in the How To word document)

:triangular_flag_on_post: Details

The dataset includes image files and appropriate annotations to train YOLO v5 detector. It is separated into two versions: 1. with 4 classes only 1. and with all 43 classes

Before training, edit dataset.yaml file and specify there appropriate path 👇

# The root directory of the dataset
# (!) Update the root path according to your location
path: ..\..\Downloads\ts_yolo_v5_format\ts4classes

train: images\train\   # train images (relative to 'path')
val: images\validation\  # val images (relative to 'path')
test: images\test\    # test images (relative to 'path')

# Number of classes and their names
nc: 4
names: [ 'prohibitory', 'danger', 'mandatory', 'other']

🎥 Watch video about YOLO format 👇

https://www.youtube.com/watch?v=-bU0ZBbG8l4" alt="">

🎓 YOLO v5: Label, Train and Test. Join the course! 👇

https://www.udemy.com/course/yolo-v5-label-train-and-test

Have a look at the abilities that you will obtain:
📢 Run YOLO v5 to detect objects on image, video and in real time by camera in the first lectures.
📢 Label-Create-Convert own dataset in YOLO format.
📢 Train & Test both: in your local machine and in the cloud machine (with custom data and by few lines of the code).

Concept map of the YOLO v5 course 👇

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3400968%2Fac1893f68be61efb21e376b3c405147c%2Fconcept_map_YOLO_v5.png?generation=1701165575909796&alt=media" alt="Concept map of the YOLO v5 course">

Join the course! 👇

https://www.udemy.com/course/yolo-v5-label-train-and-test

Acknowledgements

Initial data is The German Traffic Sign Recognition Benchmarks (GTSRB).

📦 MP-IDB-YOLO – Ultralytics YOLO-Formatted Malaria Parasite Image Dataset for Instance Segmentation and Object Detection

This dataset is a repackaged version of the original MP-IDB (The Malaria Parasite Image Database for Image Processing and Analysis), formatted for Ultralytics YOLO (You Only Look Once) instance segmentation annotation. The goal of this release is to make it easier for researchers and practitioners to apply state-of-the-art instance segmentation or object detection techniques to malaria cell detection and classification tasks.

⚠️ This dataset is a derivative work. All original images and annotations belong to the original MP-IDB authors. This version only converts them into Ultralytics YOLO-compatible format.

📚 About the Original Dataset

The original MP-IDB dataset was created and released by Andrea Loddo, Cecilia Di Ruberto, Michel Kocher, and Guy Prod’Hom, and is described in the following publication:

MP-IDB: The Malaria Parasite Image Database for Image Processing and Analysis
In Processing and Analysis of Biomedical Information, Springer, 2019.
DOI: 10.1007/978-3-030-13835-6_7

The dataset includes annotated microscopic blood smear images of four malaria species:

• Plasmodium falciparum
• Plasmodium malariae
• Plasmodium ovale
• Plasmodium vivax

Each image contains cells in one or more of the following parasite life stages, indicated in filenames:

• R: Ring
• T: Trophozoite
• S: Schizont
• G: Gametocyte

Expert pathologists provided the ground truth for each image.

🛠️ What’s Included in This YOLO Version

This version of the dataset includes:

• ✅ Original images from MP-IDB
• ✅ Instance Segmentation annotations in Ultralytics YOLO format (.txt files)
• ✅ Class definitions matching parasite species and life stages
• ✅ A YAML file ready for training using the Ultralytics Package

This reformatting is designed to save time for those building instance segmentation or object detection models for medical imaging and accelerate prototyping using YOLO and the Ultralytics Package.

📜 License and Attribution

The original MP-IDB dataset is released under the MIT License by Andrea Loddo and contributors. Please make sure to cite the original work if you use this dataset in your own research or application:

🐘 About the Wild Elephant YOLO Format Dataset

The Wild Elephant YOLO Format Dataset contains over 40,000 annotated images of wild elephants captured in natural environments. This dataset is designed for use in computer vision applications, especially object detection models trained with the YOLO (You Only Look Once) format.

Each image is labeled with bounding boxes identifying elephant instances, making it ideal for wildlife monitoring, conservation AI systems, and real-time elephant detection.

📁 Structure:

Organized in YOLOv5-friendly format

Includes images/, labels/, and data.yaml files

Clean, high-resolution samples from varied lighting and angles

💡 Use Cases:

Human-elephant conflict mitigation systems

Wildlife conservation research

Custom object detection model training

🔖 License: Open-source (please credit if used in research or products)

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/).

The dataset has undergone format conversion based on URPC2021_Sonar_images_data

The Fish Detection AI project aims to improve the efficiency of fish monitoring around marine energy facilities to comply with regulatory requirements. Despite advancements in computer vision, there is limited focus on sonar images, identifying small fish with unlabeled data, and methods for underwater fish monitoring for marine energy. A YOLO (You Only Look Once) computer vision model was developed using the Eyesea dataset (optical) and sonar images from Alaska Fish and Games to identify fish in underwater environments. Supervised methods were used within YOLO to detect fish based on training using labeled data of fish. These trained models were then applied to different unseen datasets, aiming to reduce the need for labeling datasets and training new models for various locations. Additionally, hyper-image analysis and various image preprocessing methods were explored to enhance fish detection. In this research we achieved: 1. Enhanced YOLO Performance, as compared to a published article (Xu, Matzner 2018) using earlier yolo versions for fish object identification. Specifically, we achieved a best mean Average Precision (mAP) of 0.68 on the Eyesea optical dataset using YOLO v8 (medium-sized model), surpassing previous YOLO v3 benchmarks from that previous article publication. We further demonstrated up to 0.65 mAP on unseen sonar domains by leveraging a hyper-image approach (stacking consecutive frames), showing promising cross-domain adaptability. This submission of data includes: - The actual best-performing trained YOLO model neural network weights, which can be applied to do object detection (PyTorch files, .pt). These are found in the Yolo_models_downloaded zip file - Documentation file to explain the upload and the goals of each of the experiments 1-5, as detailed in the word document (named "Yolo_Object_Detection_How_To_Document.docx") - Coding files, namely 5 sub-folders of python, shell, and yaml files that were used to run the experiments 1-5, as well as a separate folder for yolo models. Each of these is found in their own zip file, named after each experiment - Sample data structures (sample1 and sample2, each with their own zip file) to show how the raw data should be structured after running our provided code on the raw downloaded data - link to the article that we were replicating (Xu, Matzner 2018) - link to the Yolo documentation site from the original creators of that model (ultralytics) - link to the downloadable EyeSea data set from PNNL (instructions on how to download and format the data in the right way to be able to replicate these experiments is found in the How To word document)

This dataset is designed for training and evaluating object detection models, specifically for detecting plastic bottles and classifying them based on the presence or absence of a label. It is structured to work seamlessly with YOLOv8 and follows the standard YOLO format.

🔍 Classes: 0: Bottle with Label

1: Bottle without Label

📁 Folder Structure: images/: Contains all image files

labels/: Corresponding YOLO-format annotation files

data.yaml: Configuration file for training with YOLOv8

🛠 Use Case: This dataset is ideal for real-time detection systems, quality control applications, recycling automation, and projects focused on object classification in cluttered or real-world environments.

Context

Dataset of impact craters on the Moon derived by LU3M6TGT global catalog. It was created by a deep learning model YOLOLens, which also increases the spatial image resolution allowing crater detection down to sizes as small as 0.5 km. The full database encompasses more than 3.5 million craters, a value three times larger with respect to other lunar catalogues currently available, and this release represents a YOLO format version of a subset of LU3M6TGT catalog useful to train the deep learning model in easy way.

Content

• The dataset is created by YOLOLens model (see Reference) using the sources of the Lunar Reconnaissance Orbiter Camera (LROC).
• Splitted into Train/val test (1545 val img and 8756 train img), 3x416x416 of shape, 0-255 bit depth.
• YOLO format release of a subset of LU3M6TGT catalog (The full catalog in format science (coordinates system) can be found (see LU5M812TGT dataset). soon after journal publication at Zenodo repository).
• The Global Mosaic Map used as main data source can be found in the following link: https://astrogeology.usgs.gov/search?pmi-target=moon under the name: Moon LRO LROC WAC Global Morphology Mosaic 100m v3.
• The resulting dataset images & labels are converted using the orthographic projection useful to avoid dilatation images due to the main mosaic map given into the cylindrical projection.

Technical details: 1. Change the absolute path inside the data.yaml file. 2. At first use delete labels.cache inside the val folder (if you'll use yolo model it will create for you again using your new absolute path). 3. The dilatation offsets contains the shifting in x-axis for each image row due to the projection changing. You can ignore this folder.

BibTeX Citation

If you use the dataset in a scientific publication, I would appreciate using the following citation:

@article{la2023yololens, title={YOLOLens: A Deep Learning Model Based on Super-Resolution to Enhance the Crater Detection of the Planetary Surfaces}, author={La Grassa, Riccardo and Cremonese, Gabriele and Gallo, Ignazio and Re, Cristina and Martellato, Elena}, journal={Remote Sensing}, volume={15}, number={5}, pages={1171}, year={2023}, publisher={MDPI} }

Contact email: riccardo.lagrassa@inaf.it

📄 Description

Overview This dataset contains annotated images of 7 types of kitchen utensils — fork, butter knife, kitchen knife, peeler, spoon, tongs, and wooden spoon — organized into train/ and val/ sets. Each split includes subfolders images/ (JPEG/PNG files) and labels/ (YOLO-format .txt files), along with a classes.txt listing the class names mapped to indices 0–6.

Dataset Contents

• train/images/ & val/images/: Raw utensil photos
• train/labels/ & val/labels/: YOLO-format .txt annotations (one line per object: class_id x_center y_center width height, all normalized)

• classes.txt:

  fork
  butter knife
  kitchen knife
  peeler
  spoon
  tongs
  wooden spoon
  

Use Cases

• Train or fine-tune object detection models (e.g., YOLOv8, YOLOv5) on kitchen utensil recognition
• Benchmark multi‑class detection performance in indoor/home environments
• Serve as a starting point for kitchen inventory automation, robotics, and smart cooking applications

Structure and Labeling Standards

• 2, XXX images total — pre-split into train/validation
• Each image’s annotation file shares its base name and contains bounding boxes in YOLO format
• Class indices align with entries in classes.txt, ensuring compatibility with common detection frameworks

Getting Started

1. Clone or download this dataset

2. Reference the folder paths in your data.yaml:

   train: train/images
   val:  val/images
   nc: 7
   names:
    0: fork
    1: butter knife
    2: kitchen knife
    3: peeler
    4: spoon
    5: tongs
    6: wooden spoon
   

3. Train a YOLOv8 model:

   model.train(data='data.yaml', epochs=50, imgsz=640)
   

Recommended Citation / Acknowledgment If you publish research using this dataset, please mention:

“Kitchen utensil detection dataset uploaded via Kaggle by Raunak gola.”

Future Extensions

• Expand with more utensil types or larger image sets
• Support segmentation annotations
• Add real-world kitchen scene backgrounds or occluded images