https://i.imgur.com/ztezlER.png" alt="Image example">

Overview

This dataset contains 627 images of various vehicle classes for object detection. These images are derived from the Open Images open source computer vision datasets.

This dataset only scratches the surface of the Open Images dataset for vehicles!

https://i.imgur.com/4ZHN8kk.png" alt="Image example">

Use Cases

• Train object detector to differentiate between a car, bus, motorcycle, ambulance, and truck.
• Checkpoint object detector for autonomous vehicle detector
• Test object detector on high density of ambulances in vehicles
• Train ambulance detector
• Explore the quality and range of Open Image dataset

Tools Used to Derive Dataset

https://i.imgur.com/1U0M573.png" alt="Image example">

These images were gathered via the OIDv4 Toolkit This toolkit allows you to pick an object class and retrieve a set number of images from that class with bound box lables.

We provide this dataset as an example of the ability to query the OID for a given subdomain. This dataset can easily be scaled up - please reach out to us if that interests you.

So I have a knack of photography and travelling. I wanted to create a model for myself which can classify my own pictures. But to be honest, a Data Scientist should always know how to collect data. So I scraped data from google images using a Python Script and using other open-source data sources from MIT, Kaggle itself, etc. Request everyone to give a try. I'll update the no. of images in validation set as time goes on.

The link to the scripting file is here: https://github.com/debadridtt/Scraping-Google-Images-using-Python

The images belong typically to 4 classes:

• Art & Culture
• Architecture
• Food and Drinks
• Travel and Adventure

Context

It consists of the pictures in the opencv github account to make case studies on opencv. Source: https://github.com/opencv/opencv/tree/master/samples/data

Open Source Computer Vision Library https://opencv.org

This is an open source - publicly available dataset which can be found at https://shahariarrabby.github.io/ekush/ . We split the dataset into three sets - train, validation, and test. For our experiments, we created two other versions of the dataset. We have applied 10-fold cross validation on the train set and created ten folds. We also created ten bags of datasets using bootstrap aggregating method on the train and validation sets. Lastly, we created another dataset using pre-trained ResNet50 model as feature extractor. On the features extracted by ResNet50 we have applied PCA and created a tabilar dataset containing 80 features. pca_features.csv is the train set and pca_test_features.csv is the test set. Fold.tar.gz contains the ten folds of images described above. Those folds are also been compressed. Similarly, Bagging.tar.gz contains the ten compressed bags of images. The original train, validation, and test sets are in Train.tar.gz, Validation.tar.gz, and Test.tar.gz, respectively. The compression has been performed for speeding up the upload and download purpose and mostly for the sake of convenience. If anyone has any question about how the datasets are organized please feel free to ask me at shiblygnr@gmail.com .I will get back to you in earliest time possible.

Image generated by https://ideogram.ai/

We introduce Vision-Flan, the largest human-annotated visual instruction tuning dataset that consists of 200+ diverse vision-language tasks derived from 101 open-source computer vision datasets. Each task is equipped with an expert written instruction and carefully designed templates for the inputs and outputs. The dataset encompasses a wide range of tasks such as image captioning, visual question-answering, and visual understanding. Vision-Flan is… See the full description on the dataset page: https://huggingface.co/datasets/Vision-Flan/vision-flan.

Vision plays an important role when transitioning between different locomotor tasks (e.g.

PLEASE UPVOTE IF YOU FOUND THIS DATASET USEFUL

The Open Images Dataset is a vast collection of annotated images designed for computer vision research. It contains millions of images labeled with thousands of object categories, bounding boxes, and relationship annotations, making it a valuable resource for training and evaluating machine learning models in object detection, image segmentation, and scene understanding.

Provenance:
- Source: The dataset was initially released by Google Research and is now maintained for public access.
- Methodology: Images were sourced from various locations across the web and annotated using a combination of machine learning models and human verification. The dataset follows a structured labeling pipeline to ensure high-quality annotations.

For more information and dataset access, visit: https://storage.googleapis.com/openimages/web/index.html.

The Remote Sensing Object Segmentation Dataset is a key asset for the remote sensing field, combining images from the DOTA open dataset and additional internet sources. With resolutions ranging from 451 × 839 to 6573 × 3727 pixels for standard images and up to 25574 × 15342 pixels for uncut large images, this dataset includes diverse categories like playgrounds, vehicles, and sports courts, all annotated for instance and semantic segmentation.

https://i.imgur.com/4LoSPQh.png" alt="Image example">

Overview

This dataset contains 581 images of various shellfish classes for object detection. These images are derived from the Open Images open source computer vision datasets.

This dataset only scratches the surface of the Open Images dataset for shellfish!

https://i.imgur.com/oMK91v6.png" alt="Image example">

Use Cases

• Train object detector to differentiate between a lobster, shrimp, and crab.
• Train object dector to differentiate between shellfish
• Object detection dataset across different sub-species
• Object detection among related species
• Test object detector on highly related objects
• Train shellfish detector
• Explore the quality and range of Open Image dataset

Tools Used to Derive Dataset

https://i.imgur.com/1U0M573.png" alt="Image example">

These images were gathered via the OIDv4 Toolkit This toolkit allows you to pick an object class and retrieve a set number of images from that class with bound box lables.

We provide this dataset as an example of the ability to query the OID for a given subdomain. This dataset can easily be scaled up - please reach out to us if that interests you.

This dataset consists of 5000 images (20% of the original dataset's images) from the Unsplash Lite Open-Source Dataset. All credit goes to Unsplash for this astounding dataset. The user of this dataset may use this dataset as they see fit. However, the lack of labels indicates that the dataset is for unsupervised learning or image-to-image problems.

Dataset Info

• The dataset is split into train, dev and test sets.
• The training set contains 4000 (80%) images of the dataset.
• The dev and test sets each contain 500 (10%) images of the dataset.
• The images in this dataset contained within the images folder have a width of 1920 from the original dataset.
• The images were named using the 'Img_' tag followed by the index from the original dataset.

Acknowledgements:

• Dataset page: Unsplash Dataset Page
• GitHub page: Unsplash Dataset GitHub Page

Robot Vision Dataset Services for Space Market Outlook

According to our latest research, the global Robot Vision Dataset Services for Space market size reached USD 1.21 billion in 2024, driven by the rapid adoption of AI-driven visual analytics in space missions. The market is projected to grow at a robust CAGR of 18.7% from 2025 to 2033, reaching a forecasted value of USD 6.27 billion by 2033. This remarkable growth is fueled by increasing investments in space exploration, advancements in autonomous robotics, and the critical need for high-quality, annotated datasets to enable reliable and accurate machine vision in complex extraterrestrial environments.

The primary growth factor for the Robot Vision Dataset Services for Space market is the exponential rise in demand for autonomous space robotics and spacecraft. As missions become increasingly complex—ranging from satellite maintenance to planetary exploration—there is a heightened need for robust, annotated datasets that can train AI models to interpret and act on visual information in real time. The integration of deep learning and computer vision technologies into space robotics has amplified the requirement for diverse, high-resolution datasets that can simulate the unpredictable conditions of space, such as varied lighting, terrain, and object recognition scenarios. As a result, space agencies and commercial space enterprises are investing heavily in dataset services that support the development of reliable and intelligent robotic systems.

Another significant driver is the proliferation of commercial space activities and the entry of private players into satellite launches, orbital servicing, and extraterrestrial mining. These commercial entities are leveraging robot vision dataset services to accelerate the development and deployment of autonomous systems that can perform complex tasks without human intervention. The need for precision in navigation, object detection, and manipulation in the harsh space environment necessitates the use of meticulously curated and validated datasets. Additionally, the rise of NewSpace companies and the ongoing miniaturization of satellites have further expanded the scope of applications for robot vision datasets, fostering a competitive ecosystem that encourages innovation and service improvement.

Technological advancements in imaging sensors, multispectral and hyperspectral data acquisition, and cloud-based data processing have also contributed to the market’s robust growth. The ability to capture, annotate, and preprocess vast amounts of data in various formats—including image, video, and spectral data—has enabled service providers to offer highly customized solutions for specific mission requirements. Furthermore, the increasing collaboration between space agencies, research institutions, and commercial vendors has led to the establishment of shared data repositories and open-source initiatives, enhancing the accessibility and quality of robot vision datasets. These collaborative efforts are expected to further accelerate market growth and drive innovation in the coming years.

From a regional perspective, North America currently dominates the Robot Vision Dataset Services for Space market, owing to the presence of leading space agencies such as NASA, a vibrant commercial space sector, and a strong ecosystem of AI and machine vision technology providers. Europe and Asia Pacific are also witnessing substantial growth, fueled by increased government investments in space research and the emergence of regional commercial space ventures. The Middle East & Africa and Latin America, while still nascent, are expected to experience accelerated growth over the forecast period as regional governments and private players increase their focus on space technologies and autonomous robotics.

Service Type Analysis

The service type segment of the Robot Vision Dataset Services for Space market is comprised of dataset collection, annotation, preprocessing, validation, and other ancillary services. Dataset collection forms the foundational layer, involving the gathering of raw visual data from a variety of sources such as satellites, rovers, and space telescopes. Given the complexity of space environments, this process requires sophisticated hardware and software integration to ensure data accuracy and completeness. Service providers are leveraging advanced imaging technologies and remote sensing equipment to capture high-resolution images

General

For more details and the most up-to-date information please consult our project page: https://kainmueller-lab.github.io/fisbe.

Summary

• A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains
  • 30 completely labeled (segmented) images
  • 71 partly labeled images
  • altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)
• To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects
• A set of metrics and a novel ranking score for respective meaningful method benchmarking
• An evaluation of three baseline methods in terms of the above metrics and score

Abstract

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.

Dataset documentation:

We provide a detailed documentation of our dataset, following the Datasheet for Datasets questionnaire:

>> FISBe Datasheet

Our dataset originates from the FlyLight project, where the authors released a large image collection of nervous systems of ~74,000 flies, available for download under CC BY 4.0 license.

Files

• fisbe_v1.0_{completely,partly}.zip
  • contains the image and ground truth segmentation data; there is one zarr file per sample, see below for more information on how to access zarr files.
• fisbe_v1.0_mips.zip
  • maximum intensity projections of all samples, for convenience.
• sample_list_per_split.txt
  • a simple list of all samples and the subset they are in, for convenience.
• view_data.py
  • a simple python script to visualize samples, see below for more information on how to use it.
• dim_neurons_val_and_test_sets.json
  • a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.
• Readme.md
  • general information

How to work with the image files

Each sample consists of a single 3d MCFO image of neurons of the fruit fly.
For each image, we provide a pixel-wise instance segmentation for all separable neurons.
Each sample is stored as a separate zarr file (zarr is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").
The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.
The segmentation mask for each neuron is stored in a separate channel.
The order of dimensions is CZYX.

We recommend to work in a virtual environment, e.g., by using conda:

conda create -y -n flylight-env -c conda-forge python=3.9
conda activate flylight-env

How to open zarr files

1. Install the python zarr package:

   pip install zarr

2. Opened a zarr file with:
   

   import zarr
raw = zarr.open(
seg = zarr.open(

   # optional:
import numpy as np
raw_np = np.array(raw)

Zarr arrays are read lazily on-demand.
Many functions that expect numpy arrays also work with zarr arrays.
Optionally, the arrays can also explicitly be converted to numpy arrays.

How to view zarr image files

We recommend to use napari to view the image data.

1. Install napari:

   pip install "napari[all]"

2. Save the following Python script:
   

   import zarr, sys, napari

   raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")
gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")

   viewer = napari.Viewer(ndisplay=3)
for idx, gt in enumerate(gts):
viewer.add_labels(
gt, rendering='translucent', blending='additive', name=f'gt_{idx}')
viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')
viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')
viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')
napari.run()

3. Execute:

   python view_data.py 

Metrics

• S: Average of avF1 and C
• avF1: Average F1 Score
• C: Average ground truth coverage
• clDice_TP: Average true positives clDice
• FS: Number of false splits
• FM: Number of false merges
• tp: Relative number of true positives

For more information on our selected metrics and formal definitions please see our paper.

Baseline

To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely PatchPerPix (ppp), Flood Filling Networks (FFN) and a non-learnt application-specific color clustering from Duan et al..
For detailed information on the methods and the quantitative results please see our paper.

License

The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Citation

If you use FISBe in your research, please use the following BibTeX entry:

@misc{mais2024fisbe,
 title =    {FISBe: A real-world benchmark dataset for instance
         segmentation of long-range thin filamentous structures},
 author =    {Lisa Mais and Peter Hirsch and Claire Managan and Ramya
         Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena
         Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller},
 year =     2024,
 eprint =    {2404.00130},
 archivePrefix ={arXiv},
 primaryClass = {cs.CV}
}

Acknowledgments

We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable
discussions.
P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.
This work was co-funded by Helmholtz Imaging.

Changelog

There have been no changes to the dataset so far.
All future change will be listed on the changelog page.

Contributing

If you would like to contribute, have encountered any issues or have any suggestions, please open an issue for the FISBe dataset in the accompanying github repository.

All contributions are welcome!

ABSTRACT An original dataset of thermal videos and images that simulate illegal movements around the border and in protected areas and are designed for training machines and deep learning models. The videos are recorded in areas around the forest, at night, in different weather conditions – in the clear weather, in the rain, and in the fog, and with people in different body positions (upright, hunched) and movement speeds (regu- lar walking, running) at different ranges from the camera. In addition to using standard camera lenses, telephoto lenses were also used to test their impact on the quality of thermal images and person detection in different weather conditions and distance from the camera. The obtained dataset comprises 7412 manually labeled images extracted from video frames captured in the long-wave infrared (LWIR) a segment of the electromagnetic (EM) spectrum.

Instructions:

About 20 minutes of recorded material from the clear weather scenario, 13 minutes from the fog scenario, and about 15 minutes from rainy weather were processed. The longer videos were cut into sequences and from these sequences individual frames were extracted, resulting in 11,900 images for the clear weather, 4,905 images for the fog, and 7,030 images for the rainy weather scenarios.

A total of 6,111 frames were manual annotated so that could be used to train the supervised model for person detection. When selecting the frames, it was taken into account that the selected frames include different weather conditions so that in the set there were 2,663 frames shot in clear weather conditions, 1,135 frames of fog, and 2,313 frames of rain.

The annotations were made using the open-source Yolo BBox Annotation Tool that can simultaneously store annotations in the three most popular machine learning annotation formats YOLO, VOC, and MS COCO so all three annotation formats are available. The image annotation consists of a centroid position of the bounding box around each object of interest, size of the bounding box in terms of width and height, and corresponding class label (Human or Dog).

Activities of Daily Living Object DatasetOverviewThe ADL (Activities of Daily Living) Object Dataset is a curated collection of images and annotations specifically focusing on objects commonly interacted with during daily living activities. This dataset is designed to facilitate research and development in assistive robotics in home environments.Data Sources and LicensingThe dataset comprises images and annotations sourced from four publicly available datasets:COCO DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context. European Conference on Computer Vision (ECCV), 740–755.Open Images DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Duerig, T., & Ferrari, V. (2020). The Open Images Dataset V6: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale. International Journal of Computer Vision, 128(7), 1956–1981.LVIS DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Gupta, A., Dollar, P., & Girshick, R. (2019). LVIS: A Dataset for Large Vocabulary Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5356–5364.Roboflow UniverseLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation: The following repositories from Roboflow Universe were used in compiling this dataset:Work, U. AI Based Automatic Stationery Billing System Data Dataset. 2022. Accessible at: https://universe.roboflow.com/university-work/ai-based-automatic-stationery-billing-system-data (accessed on 11 October 2024).Destruction, P.M. Pencilcase Dataset. 2023. Accessible at: https://universe.roboflow.com/project-mental-destruction/pencilcase-se7nb (accessed on 11 October 2024).Destruction, P.M. Final Project Dataset. 2023. Accessible at: https://universe.roboflow.com/project-mental-destruction/final-project-wsuvj (accessed on 11 October 2024).Personal. CSST106 Dataset. 2024. Accessible at: https://universe.roboflow.com/personal-pgkq6/csst106 (accessed on 11 October 2024).New-Workspace-kubz3. Pencilcase Dataset. 2022. Accessible at: https://universe.roboflow.com/new-workspace-kubz3/pencilcase-s9ag9 (accessed on 11 October 2024).Finespiralnotebook. Spiral Notebook Dataset. 2024. Accessible at: https://universe.roboflow.com/finespiralnotebook/spiral_notebook (accessed on 11 October 2024).Dairymilk. Classmate Dataset. 2024. Accessible at: https://universe.roboflow.com/dairymilk/classmate (accessed on 11 October 2024).Dziubatyi, M. Domace Zadanie Notebook Dataset. 2023. Accessible at: https://universe.roboflow.com/maksym-dziubatyi/domace-zadanie-notebook (accessed on 11 October 2024).One. Stationery Dataset. 2024. Accessible at: https://universe.roboflow.com/one-vrmjr/stationery-mxtt2 (accessed on 11 October 2024).jk001226. Liplip Dataset. 2024. Accessible at: https://universe.roboflow.com/jk001226/liplip (accessed on 11 October 2024).jk001226. Lip Dataset. 2024. Accessible at: https://universe.roboflow.com/jk001226/lip-uteep (accessed on 11 October 2024).Upwork5. Socks3 Dataset. 2022. Accessible at: https://universe.roboflow.com/upwork5/socks3 (accessed on 11 October 2024).Book. DeskTableLamps Material Dataset. 2024. Accessible at: https://universe.roboflow.com/book-mxasl/desktablelamps-material-rjbgd (accessed on 11 October 2024).Gary. Medicine Jar Dataset. 2024. Accessible at: https://universe.roboflow.com/gary-ofgwc/medicine-jar (accessed on 11 October 2024).TEST. Kolmarbnh Dataset. 2023. Accessible at: https://universe.roboflow.com/test-wj4qi/kolmarbnh (accessed on 11 October 2024).Tube. Tube Dataset. 2024. Accessible at: https://universe.roboflow.com/tube-nv2vt/tube-9ah9t (accessed on 11 October 2024). Staj. Canned Goods Dataset. 2024. Accessible at: https://universe.roboflow.com/staj-2ipmz/canned-goods-isxbi (accessed on 11 October 2024).Hussam, M. Wallet Dataset. 2024. Accessible at: https://universe.roboflow.com/mohamed-hussam-cq81o/wallet-sn9n2 (accessed on 14 October 2024).Training, K. Perfume Dataset. 2022. Accessible at: https://universe.roboflow.com/kdigital-training/perfume (accessed on 14 October 2024).Keyboards. Shoe-Walking Dataset. 2024. Accessible at: https://universe.roboflow.com/keyboards-tjtri/shoe-walking (accessed on 14 October 2024).MOMO. Toilet Paper Dataset. 2024. Accessible at: https://universe.roboflow.com/momo-nutwk/toilet-paper-wehrw (accessed on 14 October 2024).Project-zlrja. Toilet Paper Detection Dataset. 2024. Accessible at: https://universe.roboflow.com/project-zlrja/toilet-paper-detection (accessed on 14 October 2024).Govorkov, Y. Highlighter Detection Dataset. 2023. Accessible at: https://universe.roboflow.com/yuriy-govorkov-j9qrv/highlighter_detection (accessed on 14 October 2024).Stock. Plum Dataset. 2024. Accessible at: https://universe.roboflow.com/stock-qxdzf/plum-kdznw (accessed on 14 October 2024).Ibnu. Avocado Dataset. 2024. Accessible at: https://universe.roboflow.com/ibnu-h3cda/avocado-g9fsl (accessed on 14 October 2024).Molina, N. Detection Avocado Dataset. 2024. Accessible at: https://universe.roboflow.com/norberto-molina-zakki/detection-avocado (accessed on 14 October 2024).in Lab, V.F. Peach Dataset. 2023. Accessible at: https://universe.roboflow.com/vietnam-fruit-in-lab/peach-ejdry (accessed on 14 October 2024).Group, K. Tomato Detection 4 Dataset. 2023. Accessible at: https://universe.roboflow.com/kkabs-group-dkcni/tomato-detection-4 (accessed on 14 October 2024).Detection, M. Tomato Checker Dataset. 2024. Accessible at: https://universe.roboflow.com/money-detection-xez0r/tomato-checker (accessed on 14 October 2024).University, A.S. Smart Cam V1 Dataset. 2023. Accessible at: https://universe.roboflow.com/ain-shams-university-byja6/smart_cam_v1 (accessed on 14 October 2024).EMAD, S. Keysdetection Dataset. 2023. Accessible at: https://universe.roboflow.com/shehab-emad-n2q9i/keysdetection (accessed on 14 October 2024).Roads. Chips Dataset. 2024. Accessible at: https://universe.roboflow.com/roads-rvmaq/chips-a0us5 (accessed on 14 October 2024).workspace bgkzo, N. Object Dataset. 2021. Accessible at: https://universe.roboflow.com/new-workspace-bgkzo/object-eidim (accessed on 14 October 2024).Watch, W. Wrist Watch Dataset. 2024. Accessible at: https://universe.roboflow.com/wrist-watch/wrist-watch-0l25c (accessed on 14 October 2024).WYZUP. Milk Dataset. 2024. Accessible at: https://universe.roboflow.com/wyzup/milk-onbxt (accessed on 14 October 2024).AussieStuff. Food Dataset. 2024. Accessible at: https://universe.roboflow.com/aussiestuff/food-al9wr (accessed on 14 October 2024).Almukhametov, A. Pencils Color Dataset. 2023. Accessible at: https://universe.roboflow.com/almas-almukhametov-hs5jk/pencils-color (accessed on 14 October 2024).All images and annotations obtained from these datasets are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits sharing and adaptation of the material in any medium or format, for any purpose, even commercially, provided that appropriate credit is given, a link to the license is provided, and any changes made are indicated.Redistribution Permission:As all images and annotations are under the CC BY 4.0 license, we are legally permitted to redistribute this data within our dataset. We have complied with the license terms by:Providing appropriate attribution to the original creators.Including links to the CC BY 4.0 license.Indicating any changes made to the original material.Dataset StructureThe dataset includes:Images: High-quality images featuring ADL objects suitable for robotic manipulation.Annotations: Bounding boxes and class labels formatted in the YOLO (You Only Look Once) Darknet format.ClassesThe dataset focuses on objects commonly involved in daily living activities. A full list of object classes is provided in the classes.txt file.FormatImages: JPEG format.Annotations: Text files corresponding to each image, containing bounding box coordinates and class labels in YOLO Darknet format.How to Use the DatasetDownload the DatasetUnpack the Datasetunzip ADL_Object_Dataset.zipHow to Cite This DatasetIf you use this dataset in your research, please cite our paper:@article{shahria2024activities, title={Activities of Daily Living Object Dataset: Advancing Assistive Robotic Manipulation with a Tailored Dataset}, author={Shahria, Md Tanzil and Rahman, Mohammad H.}, journal={Sensors}, volume={24}, number={23}, pages={7566}, year={2024}, publisher={MDPI}}LicenseThis dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).License Link: https://creativecommons.org/licenses/by/4.0/By using this dataset, you agree to provide appropriate credit, indicate if changes were made, and not impose additional restrictions beyond those of the original licenses.AcknowledgmentsWe gratefully acknowledge the use of data from the following open-source datasets, which were instrumental in the creation of our specialized ADL object dataset:COCO Dataset: We thank the creators and contributors of the COCO dataset for making their images and annotations publicly available under the CC BY 4.0 license.Open Images Dataset: We express our gratitude to the Open Images team for providing a comprehensive dataset of annotated images under the CC BY 4.0 license.LVIS Dataset: We appreciate the efforts of the LVIS dataset creators for releasing their extensive dataset under the CC BY 4.0 license.Roboflow Universe:

This dataset offers a diverse collection of images curated to support the development of computer vision models for detecting and inspecting Fire Safety Equipment (FSE) and related components. Images were collected from a variety of public buildings in Germany, including university buildings, student dormitories, and shopping malls. The dataset consists of self-captured images using mobile cameras, providing a broad range of real-world scenarios for FSE detection.

In the journal paper associated with these image datasets, the open-source dataset FireNet (Boehm et al. 2019) was additionally utilized for training. However, to comply with licensing and distribution regulations, images from FireNet have been excluded from this dataset. Interested users can visit the FireNet repository directly to access and download those images if additional data is required. The provided weights (.pt), however, are trained on the provided self-made images and FireNet using YOLOv8.

The dataset is organized into six sub-datasets, each corresponding to a specific FSE-related machine learning service:

1. Service 1: FSE Detection - This sub-dataset provides the foundation for FSE inspection, focusing on the detection of primary FSE components like fire blankets, fire extinguishers, manual call points, and smoke detectors.

2. Service 2: FSE Marking Detection - Building on the first service, this sub-dataset includes images and annotations for detecting FSE marking signs.

3. Service 3: Condition Check - Modal - This sub-dataset addresses the inspection of FSE condition in a modal manner, focusing on instances where fire extinguishers might be blocked or otherwise non-compliant. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into 3_1_FSE Condition Check_modal_train_data (containing training images and annotations) and 3_1_FSE Condition Check_modal_val_data_and_weights (containing validation images, annotations and the best weights).

4. Service 4: Condition Check - Amodal - Extending the modal condition check, this sub-dataset involves amodal detection to identify and infer the state of FSE components even when they are partially obscured. This dataset includes semantic segmentation annotations of fire extinguishers. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into 4_1_FSE Condition Check_amodal_train_data (containing training images and annotations) and 4_1_FSE Condition Check_amodal_val_data_and_weights (containing validation images, annotations and the best weights).

5. Service 5: Details Extraction - Inspection Tags - This sub-dataset provides a detailed examination of the inspection tags on fire extinguishers. It includes annotations for extracting semantic information such as the next maintenance date, contributing to a thorough evaluation of FSE maintenance practices.

6. Service 6: Details Extraction - Fire Classes Symbols - The final sub-dataset focuses on identifying fire class symbols on fire extinguishers.

This dataset is intended for researchers and practitioners in the field of computer vision, particularly those engaged in building safety and compliance initiatives.

Image Dataset Market Outlook

According to our latest research, the global Image Dataset market size reached USD 2.91 billion in 2024, with a robust year-on-year growth trajectory. The market is anticipated to expand at a CAGR of 21.5% from 2025 to 2033, culminating in a projected market value of USD 20.2 billion by 2033. The primary growth drivers include the proliferation of artificial intelligence (AI) and machine learning (ML) applications across various industries, the increasing need for high-quality annotated data for model training, and the accelerated adoption of computer vision technologies. As per the latest research, the surge in demand for image datasets is fundamentally transforming industries such as healthcare, automotive, and retail, where visual data is pivotal to innovation and automation.

A key growth factor for the Image Dataset market is the exponential rise in AI-driven solutions that rely heavily on large, diverse, and accurately labeled datasets. The sophistication of deep learning algorithms, particularly convolutional neural networks (CNNs), has heightened the necessity for high-quality image datasets to ensure reliable and accurate model performance. Industries like healthcare utilize medical imaging datasets for diagnostics and treatment planning, while autonomous vehicles depend on vast and varied image datasets to enhance object detection and navigation capabilities. Furthermore, the growing trend of synthetic data generation is addressing data scarcity and privacy concerns, providing scalable and customizable datasets for training robust AI models.

Another critical driver is the rapid adoption of computer vision across multiple sectors, including security and surveillance, agriculture, and manufacturing. Organizations are increasingly leveraging image datasets to automate visual inspection, monitor production lines, and implement advanced safety systems. The retail and e-commerce segment has witnessed a significant uptick in demand for image datasets to power recommendation engines, virtual try-on solutions, and inventory management systems. The expansion of facial recognition technology in both public and private sectors, for applications ranging from access control to personalized marketing, further underscores the indispensable role of comprehensive image datasets in enabling innovative services and solutions.

The market is also witnessing a surge in partnerships and collaborations between dataset providers, research institutions, and technology companies. This collaborative ecosystem fosters the development of diverse and high-quality datasets tailored to specific industry requirements. The increasing availability of open-source and publicly accessible image datasets is democratizing AI research and innovation, enabling startups and academic institutions to contribute to advancements in computer vision. However, the market continues to grapple with challenges related to data privacy, annotation accuracy, and the ethical use of visual data, which are prompting the development of secure, compliant, and ethically sourced datasets.

Regionally, North America remains at the forefront of the Image Dataset market, driven by a mature AI ecosystem, significant investments in research and development, and the presence of major technology companies. Asia Pacific is rapidly emerging as a high-growth region, buoyed by expanding digital infrastructure, government initiatives promoting AI adoption, and a burgeoning startup landscape. Europe is also witnessing robust growth, particularly in sectors such as automotive, healthcare, and manufacturing, where regulatory frameworks emphasize data privacy and quality. The Middle East & Africa and Latin America are gradually catching up, with increasing investments in smart city projects and digital transformation initiatives fueling demand for image datasets.

Type Analysis

The Image Dataset market by type is segmented into Labeled, Unlabeled, and Synthetic datasets. Labeled datasets, which include images annotated with relevant metadata or tags, are fundamental to sup

We provide manually annotated bounding boxes of sandhill crane targets in thermal imagery for use in deep learning models. The dataset contains forty files, each file representing the manual annotations created for a single image. We used the open-source tool labelImg (https://pypi.org/project/labelImg/) to create annotations and saved them in PASCAL VOC format.

Overview

The original Udacity Self Driving Car Dataset is missing labels for thousands of pedestrians, bikers, cars, and traffic lights. This will result in poor model performance. When used in the context of self driving cars, this could even lead to human fatalities.

We re-labeled the dataset to correct errors and omissions. We have provided convenient downloads in many formats including VOC XML, COCO JSON, Tensorflow Object Detection TFRecords, and more.

Some examples of labels missing from the original dataset: https://i.imgur.com/A5J3qSt.jpg" alt="Examples of Missing Labels">

Stats

The dataset contains 97,942 labels across 11 classes and 15,000 images. There are 1,720 null examples (images with no labels).

All images are 1920x1200 (download size ~3.1 GB). We have also provided a version downsampled to 512x512 (download size ~580 MB) that is suitable for most common machine learning models (including YOLO v3, Mask R-CNN, SSD, and mobilenet).

Annotations have been hand-checked for accuracy by Roboflow.

https://i.imgur.com/bOFkueI.pnghttps://" alt="Class Balance">

Annotation Distribution: https://i.imgur.com/NwcrQKK.png" alt="Annotation Heatmap">

Use Cases

Udacity is building an open source self driving car! You might also try using this dataset to do person-detection and tracking.

Using this Dataset

Our updates to the dataset are released under the MIT License (the same license as the original annotations and images).

Note: the dataset contains many duplicated bounding boxes for the same subject which we have not corrected. You will probably want to filter them by taking the IOU for classes that are 100% overlapping or it could affect your model performance (expecially in stoplight detection which seems to suffer from an especially severe case of duplicated bounding boxes).

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

This dataset has been developed to support research in computer vision for urban infrastructure monitoring and waste management, as part of the project QR4Change: A Smart QR-Based Civic Grievance Reporting System. The project aims to provide a technology-driven platform where citizens can conveniently report civic issues through QR codes, while automated image analysis assists municipal authorities in prioritizing and addressing complaints.

The images were collected from diverse sources, including open-source repositories, government portals, and on-field surveys in Pune (covering regions such as Kondhwa, Bibewadi, Swargate, and Market Yard).

The dataset is organized into two major categories:

Pothole Dataset: A total of 2,966 images, consisting of 1,004 pothole images and 1,962 plain road (non-pothole) images.

Garbage Dataset: A total of 1,971 images, consisting of 712 garbage dump images and 1,259 non-garbage images.

This dataset not only underpins the QR4Change project but is also intended to serve the wider research community in developing and evaluating machine learning models for tasks such as image classification, object detection, and smart city civic issue analysis.

This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on GitHub.

The upload contains two datasets (version 1.0.0):

• urban_with_gnss_dead_zones (7.0 GB, ~16 minutes)
  • City streets
  • A building is passed through on two occasions which makes the GNSS location signal unavailable at times.
  • RTK Fix is acquired at times
• suburban_nature (10.6 GB, ~19 minutes)
  • The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation
  • The RTK solution is only Float or None most of the route.

Details on collecting the data:

• Software
  • The data was collected using this open-source recorder.
    • Can be easily replayed using SpectacularAI's SDK (sdk-examples/python/oak/vio_replay.py)
  • Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland.
  • Necessary files to implement SLAM are included in the dataset.
  • Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters.
• Hardware
  • OAK-D stereo depth + color camera (Luxonis)
  • C099-F9P GNSS module (u-blox)
  • ANN-MB-00 high precision GNSS antenna (u-blox)