Small Test Set

## Overview

Small Test Set is a dataset for object detection tasks - it contains Test Photos annotations for 332 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).

The dataset is structured for person object detection tasks, containing separate directories for training, validation, and testing. Each split has an images folder with corresponding images and a labels folder with annotation files.

Train Set: Contains images and annotations for model training.

Validation Set: Includes images and labels for model evaluation during training.

Test Set: Provides unseen images and labels for final model performance assessment.

Each annotation file (TXT format) corresponds to an image and likely contains bounding box coordinates and class labels. This structure follows standard object detection dataset formats, ensuring easy integration with detection models like yolo,RT-DETR.

Dataset Structure

📂 dataset/ ├── 📁 train/ │ ├── 📂 images/ │ │ ├── 🖼 image1.jpg (Training image) │ │ ├── 🖼 image2.jpg (Training image) │ ├── 📂 labels/ │ │ ├── 📄 image1.txt (Annotation for image1.jpg) │ │ ├── 📄 image2.txt (Annotation for image2.jpg) │ ├── 📁 val/ │ ├── 📂 images/ │ │ ├── 🖼 image3.jpg (Validation image) │ │ ├── 🖼 image4.jpg (Validation image) │ ├── 📂 labels/ │ │ ├── 📄 image3.txt (Annotation for image3.jpg) │ │ ├── 📄 image4.txt (Annotation for image4.jpg) │ ├── 📁 test/ │ ├── 📂 images/ │ │ ├── 🖼 image5.jpg (Test image) │ │ ├── 🖼 image6.jpg (Test image) │ ├── 📂 labels/ │ │ ├── 📄 image5.txt (Annotation for image5.jpg) │ │ ├── 📄 image6.txt (Annotation for image6.jpg)

Test Set

## Overview

Test Set is a dataset for object detection tasks - it contains Solar Panel annotations for 404 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.

• Dataset Composition:

– 1000 images, each containing airplane instances.

– Annotations in YOLO’s bounding box format (class index, x_center, y_center, width, height).

• Data Split:

– Training Set: 75% (750 images) for model learning.

– Validation Set: 20% (200 images) for tuning hyperparameters and checking for overfitting.

– Test Set: 5% (50 images) for final performance evaluation.

This arrangement helps ensure effective training, validation, and unbiased testing of the airplane detection model.

Test Set Ground Truth

## Overview

Test Set Ground Truth is a dataset for object detection tasks - it contains Test Set Ground Truth annotations for 259 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).

Context

The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 164K images.

This is the original version from 2014 made available here for easy access in Kaggle and because it does not seem to be still available on the COCO Dataset website. This has been retrieved from the mirror that Joseph Redmon has setup on this own website.

Content

The 2014 version of the COCO dataset is an excellent object detection dataset with 80 classes, 82,783 training images and 40,504 validation images. This dataset contains all this imagery on two folders as well as the annotation with the class and location (bounding box) of the objects contained in each image.

The initial split provides training (83K), validation (41K) and test (41K) sets. Since the split between training and validation was not optimal in the original dataset, there is also two text (.part) files with a new split with only 5,000 images for validation and the rest for training. The test set has no labels and can be used for visual validation or pseudo-labelling.

Acknowledgements

This is mostly inspired by Erik Linder-Norén and [Joseph Redmon](https://pjreddie.com/darknet/yolo

Give Machines the Power to See People.

This isn’t just a dataset — it’s a foundation for building the future of human-aware technology. Carefully crafted and annotated with precision, the People Detection dataset enables AI systems to recognize and understand human presence in dynamic, real-world environments.

Whether you’re building smart surveillance, autonomous vehicles, crowd analytics, or next-gen robotics, this dataset gives your model the eyes it needs.

What Makes This Dataset Different?

• Real-World Images – Diverse environments, realistic lighting, and real human motion
• High-Quality Annotations – Every person labeled with clean YOLO-format bounding boxes
• Plug-and-Play – Comes with pre-split training, validation, and test sets — no extra prep needed
• Speed-Optimized – Perfect for real-time object detection applications

Built for Visionaries

• Detect people instantly — in cities, offices, or crowds
• Build systems that respond to human presence
• Train intelligent agents to navigate human spaces safely and smartly

Created using Roboflow. Optimized for clarity, performance, and scale. Source Dataset on Roboflow →

This is more than a dataset. It’s a step toward a smarter world — One where machines can understand people.

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 TXT (YOLO) 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.

OAOD TEST SET

## Overview

OAOD TEST SET is a dataset for object detection tasks - it contains Guns annotations for 1,549 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).

Datasets and directories are structured similar to the PASCAL VOC dataset, avoiding the need to change scripts already available, with the detection frameworks ready to parse PASCAL VOC annotations into their format.

The sub-directory JPEGImages consist of 1730 images (612x512 pixels) used for train, test and validation. Each image has at least one annotated fruit. The sub-directory Annotations consists of all the annotation files (record of bounding box coordinates for each image) in xml format and have the same name as the image name. The sub-directory Main consists of the text file that contains image names (without extension) used for train, test and validation. Training set (train.txt) lists 1300 train images Validation set (val.txt) lists 130 validation images Test set (test.txt) lists 300 test images

Each image has an XML annotation file (filename = image name) and each image set (training validation and test set) has associated text files (train.txt, val.txt and test.txt) containing the list of image names to be used for training and testing. The XML annotation file contains the image attributes (name, width, height), the object attributes (class name, object bounding box co-ordinates (xmin, ymin, xmax, ymax)). (xmin, ymin) and (xmax, ymax) are the pixel co-ordinates of the bounding box’s top-left corner and bottom-right corner respectively.

TeslaMY Test Set

## Overview

TeslaMY Test Set is a dataset for object detection tasks - it contains Car Components annotations for 386 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).

The dataset comprises 16.7k images and 2 annotation files, each in a distinct format. The first file, labeled "Label," contains annotations with the original scale, while the second file, named "yolo_format_labels," contains annotations in YOLO format. The dataset was obtained by employing the OIDv4 toolkit, specifically designed for scraping data from Google Open Images. Notably, this dataset exclusively focuses on face detection.

This dataset offers a highly suitable resource for training deep learning models specifically designed for face detection tasks. The images within the dataset exhibit exceptional quality and have been meticulously annotated with bounding boxes encompassing the facial regions. The annotations are provided in two formats: the original scale, denoting the pixel coordinates of the bounding boxes, and the YOLO format, representing the bounding box coordinates in normalized form.

The dataset was meticulously curated by scraping relevant images from Google Open Images through the use of the OIDv4 toolkit. Only images that are pertinent to face detection tasks have been included in this dataset. Consequently, it serves as an ideal choice for training deep learning models that specifically target face detection tasks.

Bangla LPDB - A (YOLO Ready)

This dataset is a reformatted and enhanced version of the Bangla LPDB - A dataset, originally published by Ataher Sams and Homaira Huda Shomee. It has been meticulously prepared to be plug-and-play for YOLO (You Only Look Once) object detection models, making it incredibly easy for researchers and developers to use for license plate detection tasks in Bangladeshi vehicles.

Original Dataset Acknowledgment

This dataset is built upon

Ataher Sams, & Homaira Huda Shomee. (2021). Bangla LPDB - A (Version v1) [Data set]. International Conference on Digital Image Computing: Techniques and Applications (IEEE DICTA), Gold Coast, Queensland Australia. Zenodo. https://doi.org/10.5281/zenodo.4718238

We extend our sincerest gratitude to them for creating such a comprehensive and vital resource for the research community.

Key Enhancements

While the original Bangla LPDB - A dataset is an excellent collection, this version provides significant improvements for immediate use with YOLO models:

• YOLO Dataset Structure: The dataset has been meticulously restructured to follow the standard YOLO format, including all necessary annotation files.
• dataset.yaml Included: A dataset.yaml file is provided for seamless integration with popular deep learning frameworks like Ultralytics YOLO.
• Class Names: Separate files for both Bengali and English class names are included, offering flexibility for various applications.
• Pre-split Data: The dataset is pre-split into optimal ratios for training, validation, and testing:
  • Training Set: 70%
  • Validation Set: 20%
  • Testing Set: 10%

Preview

Vehicle to License Plate https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F24265111%2F3976ff0d5a259dbd70dc017964bf7d47%2Fvehicle-to-license-plate.png?generation=1753064814234359&alt=media" alt="">

License Plate to Text https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F24265111%2F8f5461031162387a7aba8ed7f31c6eda%2Flicense-plate-to-text.png?generation=1753064874309479&alt=media" alt="">

The Original Paper

H. H. Shomee and A. Sams, "License Plate Detection and Recognition System for All Types of Bangladeshi Vehicles Using Multi-step Deep Learning Model," 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia, 2021, pp. 01-07, https://doi.org/10.1109/DICTA52665.2021.9647284.

Dataset Citation

Users of this dataset are required to cite the original research paper, which introduces the Bangla LPDB - A dataset and its applications. Please use the following citation:

@INPROCEEDINGS{9647284,
 author={Shomee, H. H. and Sams, A.},
 booktitle={2021 Digital Image Computing: Techniques and Applications (DICTA)},
 title={License Plate Detection and Recognition System for All Types of Bangladeshi Vehicles Using Multi-step Deep Learning Model},
 year={2021},
 pages={01-07},
 doi={10.1109/DICTA52665.2021.9647284}
}

Modified by

Ashikur Rahman Shad

ashikur-rahman-shad (Ashikur Rahman Shad) · GitHub

This dataset contains 11027 labeled images for the detection of fire and smoke instances in diverse real-world scenarios. The annotations are provided in YOLO format with bounding boxes and class labels for two classes: fire and smoke. The dataset is divided into an 80% training set with 10,090 fire instances and 9724 smoke instances, a 10% Validation set with 1,255 fire and 1,241 smoke instances, and a 10% Test set with 1,255 fire and 1,241 smoke instances. This dataset is suitable for training and evaluating fire and smoke detection models, such as YOLOv8, YOLOv9, and similar deep learning-based frameworks in the context of emergency response, wildfire monitoring, and smart surveillance.

ODDS Smart Building Depth Dataset

Introduction:

The goal of this dataset is to facilitate research focusing on recognizing objects in smart buildings using the depth sensor mounted at the ceiling. This dataset contains annotations of depth images for eight frequently seen object classes. The classes are: person, backpack, laptop, gun, phone, umbrella, cup, and box.

Data Collection:

We collected data from two settings. We had Kinect mounted at a 9.3 feet ceiling near to a 6 feet wide door. We also used a tripod with a horizontal extender holding the kinect at a similar height looking downwards. We asked about 20 volunteers to enter and exit a number of times each in different directions (3 times walking straight, 3 times walking towards left side, 3 times walking towards right side) holding objects in many different ways and poses underneath the Kinect. Each subject was using his/her own backpack, purse, laptop, etc. As a result, we considered varieties within the same object, e.g., for laptops, we considered Macbooks, HP laptops, Lenovo laptops of different years and models, and for backpacks, we considered backpacks, side bags, and purse of women. We asked the subjects to walk while holding it in many ways, e.g., for laptop, the laptop was fully open, partially closed, and fully closed while carried. Also, people hold laptops in front and side of their bodies, and underneath their elbow. The subjects carried their backpacks in their back, in their side at different levels from foot to shoulder. We wanted to collect data with real guns. However, bringing real guns to the office is prohibited. So, we obtained a few nerf guns and the subjects were carrying these guns pointing it to front, side, up, and down while walking.

Annotated Data Description:

The Annotated dataset is created following the structure of Pascal VOC devkit, so that the data preparation becomes simple and it can be used quickly with different with object detection libraries that are friendly to Pascal VOC style annotations (e.g. Faster-RCNN, YOLO, SSD). The annotated data consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the eight classes present in the image. Multiple objects from multiple classes may be present in the same image. The dataset has 3 main directories:

1)DepthImages: Contains all the images of training set and validation set.

2)Annotations: Contains one xml file per image file, (e.g., 1.xml for image file 1.png). The xml file includes the bounding box annotations for all objects in the corresponding image.

3)ImagesSets: Contains two text files training_samples.txt and testing_samples.txt. The training_samples.txt file has the name of images used in training and the testing_samples.txt has the name of images used for testing. (We randomly choose 80%, 20% split)

UnAnnotated Data Description:

The un-annotated data consists of several set of depth images. No ground-truth annotation is available for these images yet. These un-annotated sets contain several challenging scenarios and no data has been collected from this office during annotated dataset construction. Hence, it will provide a way to test generalization performance of the algorithm.

Citation:

If you use ODDS Smart Building dataset in your work, please cite the following reference in any publications: @inproceedings{mithun2018odds, title={ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs}, author={Niluthpol Chowdhury Mithun and Sirajum Munir and Karen Guo and Charles Shelton}, booktitle={ ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN)}, year={2018}, }

This dataset contains annotated images of Polish roads, specifically curated for object detection tasks. The data was collected using a car camera on roads in Poland, primarily in Kraków. The images capture a diverse range of scenarios, including different road types and various lighting conditions (day and night).

Classes:

• Car (Vehicles without a trailer)
• Different-Traffic-Sign (Other traffic signs than warning and prohibition signs, mostly information and order signs)
• Green-Traffic-Light (Green traffic lights for cars only; green lights for pedestrians are not annotated)
• Motorcycle
• Pedestrian (People and cyclists)
• Pedestrian-Crossing (Pedestrian crossings)
• Prohibition-Sign (All prohibition signs)
• Red-Traffic-Light (Red traffic lights for cars only; lights for pedestrians are not annotated)
• Speed-Limit-Sign (Speed limit signs)
• Truck (Vehicles with a trailer)
• Warning-Sign (Warning signs)

Annotation Process:

Annotations were carried out using Roboflow. A total of 2,000 images were manually labeled, while an additional 9,000 images were generated through data augmentation. The labeled techniques applied were crop, saturation, brightness, and exposure adjustments.

Image Statistics Before Data Augmentation:

Approximately

• 400 cars per 100 photos
• 30 different-traffic-signs per photos
• 80 red-traffic-lights per photos
• 70 pedestrians per photos
• 50 warning signs per photos
• 50 pedestrian-crossings per photos
• 40 green-traffic-lights per photos
• 40 prohibition signs per photos
• 40 trucks per photos
• 20 speed-limit-signs per photos
• 2 motorcycles per photos

The photos were taken on both normal roads and highways, under various conditions, including day and night. All photos were initially 1920x1080 pixels. After cropping, some images may be slightly smaller. No preprocessing steps were applied to the photos.

Annotations are provided in YOLO format.

Image Statistics Before Data Augmentation:

Image Statistics After Data Augmentation:

Overview:

The FracAtlas dataset is designed for the task of object detection, specifically focusing on detecting fractures in X-ray images. This dataset contains radiographs of various body parts, including hand, leg, hip, and shoulder regions. Each image in the dataset may contain fractures, and the dataset is annotated with bounding boxes around the fractured regions for object detection purposes.

Dataset Contents:

- Training Set:

Number of Images: 574 Description: This subset contains 80% of the fractured X-ray images, used for training fracture detection models.

- Validation Set:

Number of Images: 82 Description: This subset contains 12% of the fractured X-ray images, used for hyperparameter tuning and model validation.

- Test Set:

Number of Images: 61 Description: This subset contains 8% of the fractured X-ray images, used for evaluating the performance of trained models on unseen data.

Annotation Format:

The dataset annotations are provided in YOLO format, with bounding boxes around the fractured regions in each X-ray image.

License: The Fractured X-ray Images dataset is provided under the Open Data Commons Attribution License (ODC-By) v1.0. Users are free to use, share, and modify the dataset, provided proper attribution is given to the dataset creator.

Test Set LPR

## Overview

Test Set LPR is a dataset for object detection tasks - it contains License_plate annotations for 643 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).

FASDD is a largest and most generalized Flame And Smoke Detection Dataset for object detection tasks, characterized by the utmost complexity in fire scenes, the highest heterogeneity in feature distribution, and the most significant variations in image size and shape. FASDD serves as a benchmark for developing advanced fire detection models, which can be deployed on watchtowers, drones, or satellites in a space-air-ground integrated observation network for collaborative fire warning. This endeavor provides valuable insights for government decision-making and fire rescue operations. FASDD contains fire, smoke, and confusing non-fire/non-smoke images acquired at different distances (near and far), different scenes (indoor and outdoor), different light intensities (day and night), and from various visual sensors (surveillance cameras, UAVs, and satellites). FASDD consists of three sub-datasets, a Computer Vision (CV) dataset (i.e. FASDD_CV), a Unmanned Aerial Vehicle (UAV) dataset (i.e. FASDD_UAV), and an Remote Sensing (RS) dataset (i.e. FASDD_RS). FASDD comprises 122,634 samples, with 70,581 annotated as positive samples and 52,073 labeled as negative samples. There are 113,154 instances of flame objects and 73,072 instances of smoke objects in the entire dataset. FASDD_CV contains 95,314 samples for general computer vision, while FASDD_UAV consists of 25,097 samples captured by UAV, and FASDD_RS comprises 2,223 samples from satellite imagery. FASDD_CV contains 73,297 fire instances and 53,080 smoke instances. The CV dataset exhibits considerable variation in image size, ranging from 78 to 10,600 pixels in width and 68 to 8,858 pixels in height. The aspect ratios of the images also vary significantly, ranging from 1:6.6 to 1:0.18. FASDD_UAV contains 36,308 fire instances and 17,222 smoke instances, with image aspect ratios primarily distributed between 4:3 and 16:9. In FASDD_RS, there are 2,770 smoke instances and 3,549 flame instances. The sizes of remote sensing images are predominantly around 1,000×1,000 pixels.FASDD is provided in three compressed files: FASDD_CV.zip, FASDD_UAV.zip, and FASDD_RS.zip, which correspond to the CV dataset, the UAV dataset, and the RS dataset, respectively. Additionally, there is a FASDD_RS_SWIR. zip folder storing pseudo-color images for detecting flame objects in remote sensing imagery. Each zip file contains two folders: "images" for storing the source data and "annotations" for storing the labels. The "annotations" folder consists of label files in four formats: YOLO, VOC, COCO, and TDML. The dataset is divided randomly into training, validation, and test sets, with a ratio of 1/2, 1/3, and 1/6, respectively, within each label format. In FASDD_CV, FASDD_UAV, and FASDD_RS, images and their corresponding annotation files have been individually sorted starting from 0. The flame and smoke objects in FASDD are given the labels "fire" and "smoke" for the object detection task, respectively. The names of all images and annotation files are prefixed with "Fire", "Smoke", "FireAndSmoke", and "NeitherFireNorSmoke", representing different categories for scene classification tasks.When using this dataset, please cite the following paper. Thank you very much for your support and cooperation：################################################################################使用数据集请引用对应论文，非常感谢您的关注和支持：Wang, M., Yue, P., Jiang, L., Yu, D., Tuo, T., & Li, J. (2024). An open flame and smoke detection dataset for deep learning in remote sensing based fire detection. Geo-spatial Information Science, 1-16.################################################################################

The FlameVision dataset is a comprehensive aerial image dataset designed specifically for detecting and classifying wildfires. It consists of a total of 8600 high-resolution images, with 5000 images depicting fire and the remaining 3600 images depicting non-fire scenes. The images are provided in PNG format for classification tasks and JPG format for detection tasks. The dataset is organized into two primary folders, one for detection and the other for classification, with further subdivisions into train, validation, and test sets for each folder. To facilitate accurate object detection, the dataset also includes 4500 image annotation files. These annotation files contain manual annotations in XML format, which specify the exact positions of objects and their corresponding labels within the images. The annotations were performed using Roboflow, ensuring high quality and consistency across the dataset. One of the notable features of the FlameVision dataset is its compatibility with various convolutional neural network (CNN) architectures, including EfficientNet, DenseNet, VGG-16, ResNet50, YOLO, and R-CNN. This makes it a versatile and valuable resource for researchers and practitioners in the field of wildfire detection and classification, enabling the development and evaluation of sophisticated ML models.