Generic Object Decoding (fMRI on ImageNet)

Original paper

Horikawa, T. & Kamitani, Y. (2017). Generic decoding of seen and imagined objects using hierarchical visual features. Nature Communications 8:15037. https://www.nature.com/articles/ncomms15037

Overview

In this study, fMRI data was recorded while subjects were viewing object images (image presentation experiment) or were imagining object images (imagery experiment). The image presentation experiment consisted of two distinct types of sessions: training image sessions and test image sessions. In the training image session, a total of 1,200 images from 150 object categories (8 images from each category) were each presented only once (24 runs). In the test image session, a total of 50 images from 50 object categories (1 image from each category) were presented 35 times each (35 runs). All images were taken from ImageNet (http://www.image-net.org/, Fall 2011 release), a large-scale hierarchical image database. During the image presentation experiment, subjects performed one-back image repetition task (5 trials in each run). In the imagery experiment, subjects were required to visually imagine images from 1 of the 50 categories (20 runs; 25 categories in each run; 10 samples for each category) that were presented in the test image session of the image presentation experiment. fMRI data in the training image sessions were used to train models (decoders) which predict visual features from fMRI patterns, and those in the test image sessions and the imagery experiment were used to evaluate the model performance. Predicted features for the test image sessions and imagery experiment are used to identify seen/imagined object categories from a set of computed features for numerous object images.

Analysis demo code is available at GitHub (KamitaniLab/GenericObjectDecoding).

Dataset

MRI files

The present dataset contains fMRI data from five subjects ('sub-01', 'sub-02', 'sub-03', 'sub-04', and 'sub-05'). Each subject data contains three types of MRI data each of which was collected over multiple scanning sessions.

• 'ses-perceptionTraining': fMRI data from the training image sessions in the image presentation experiment (24 runs; 3-5 scanning sessions)
• 'ses-perceptionTest': fMRI data from the test image sessions in the image presentation experiment (35 runs; 4-6 scanning sessions)
• 'ses-imageryTest': fMRI data from the imagery experiment (20 runs; 3-5 scanning sessions)

Each scanning session consisted of functional (EPI) and anatomical (inplane T2) data. The functional EPI images covered the entire brain (TR, 3000 ms; TE, 30 ms; flip angle, 80°; voxel size, 3 × 3 × 3 mm; FOV, 192 × 192 mm; number of slices, 50, slice gap, 0 mm) and inplane T2-weighted anatomical images were acquired with the same slices used for the EPI (TR, 7020 ms; TE, 69 ms; flip angle, 160°; voxel size, 0.75 × 0.75 × 3.0 mm; FOV, 192 × 192 mm). The dataset also includes a T1-weighted anatomical reference image for each subject (TR, 2250 ms; TE, 3.06 ms; TI, 900 ms; flip angle, 9°; voxel size, 1.0 × 1.0 × 1.0 mm; FOV, 256 × 256 mm). The T1-weighted images were scanned only once for each subject in a separate scanning session and are stored in 'ses-anatomy' directories. The T1-weighted images were defaced by pydeface (https://pypi.python.org/pypi/pydeface). All DICOM files are converted to Nifti-1 files by mri_convert in FreeSurfer. In addition, the dataset contains mask images of manually defined ROIs for each subject in 'sourcedata' directory (See 'README' in 'sourcedata' for more details).

Task event files

Task event files (‘sub-*_ses-*_task-*_run-*_events.tsv’) contains recorded event (stimuli presentation, subject responses, etc.) during fMRI runs. In task event files for perception task (‘ses-perceptionTraining' and 'ses-perceptionTest'), each column represents:

• 'onset': onset time (sec) of an event
• 'duration': duration (sec) of the event
• 'trial_no': trial (block) number of the event
• 'event_type': type of the event ('rest': Rest block without visual stimulus, 'stimulus': Stimulus presentation block)
• ‘stim_id’: stimulus ID of the image presented in a stimulus block ('n/a' in rest blocks)
• 'response_time': time of button press at the block, elapsed time (sec) from the beginning of each run ('0' means that a subject did not press the button in the block)

The name of a stimulus image file is formatted like as 'n03626115_19498.JPEG' where 'n03626115' is ImageNet/WorNet ID for a synset (category) and '19498' is image ID. Because of copyright, we do not include the stimulus images in the dataset. A script downloading the images from ImageNet is available at https://github.com/KamitaniLab/GenericObjectDecoding. Image features (CNN unit responses, HMAX, GIST, and SIFT) used in the original study are available at http://brainliner.jp/data/brainliner/Generic_Object_Decoding.

In task event files for imagery task ('ses-imageryTest'), each column in represents:

• 'onset': onset time (sec) of an event
• 'duration': duration (sec) of the event
• 'trial_no': trial (block) number of the event
• 'event_type': type of the event ('rest' and 'inter_rest': rest period, 'cue': cue presentation period, 'imagery': imagery period, 'evaluation': evaluation of imagery quality period)
• 'category_id': ImageNet/WordNet synset ID of a synset (category) which the subject was instructed to imagine at the block
• 'response_time': time of button press for imagery quality evaluation at the block, elapsed time (sec) from the beginning of each run
• 'evaluation': vividness of their mental imagery evaluated by the subject (very vivid, fairly vivid, rather vivid, not vivid, or cannot recognize the target)

Household Object Detection

## Overview

Household Object Detection is a dataset for object detection tasks - it contains Household annotations for 8,959 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).

Overview

The Open Poetry Vision dataset is a synthetic dataset created by Roboflow for OCR tasks.

It combines a random image from the Open Images Dataset with text primarily sampled from Gwern's GPT-2 Poetry project. Each image in the dataset contains between 1 and 5 strings in a variety of fonts and colors randomly positioned in the 512x512 canvas. The classes correspond to the font of the text.

Example Image: https://i.imgur.com/sZT516a.png" alt="Example Image">

Use Cases

A common OCR workflow is to use a neural network to isolate text for input into traditional optical character recognition software. This dataset could make a good starting point for an OCR project like business card parsing or automated paper form-processing.

Alternatively, you could try your hand using this as a neural font identification dataset. Nvidia, amongst others, have had success with this task.

Using this Dataset

Use the fork button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

Version 5 of this dataset (classes_all_text-raw-images) has all classes remapped to be labeled as "text." This was accomplished by using Modify Classes as a preprocessing step.

Version 6 of this dataset (classes_all_text-augmented-FAST) has all classes remapped to be labeled as "text." and was trained with Roboflow's Fast Model.

Version 7 of this dataset (classes_all_text-augmented-ACCURATE) has all classes remapped to be labeled as "text." and was trained with Roboflow's Accurate Model.

• Introducing the New Roboflow Train
• What to Think About When Choosing Model Sizes

About Roboflow

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

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

DoPose (Dortmund Pose)is a dataset of highly cluttered and closely stacked objects. The dataset is saved in the BOP format. The dataset includes RGB images, Depth images, 6D Pose of objects, segmentation mask (all and visible), COCO Json annotation, camera transformations, and 3D model of all objects. The dataset contains 2 different types of scenes (table and bin). Each scene contains different view angles. For the bin scenes, the data contains 183 scenes with 2150 image views. In those 183 scenes 35 scenes contain 2 views, 20 contains 3 views and 128 contains 16 views. And for table scenes, the data contains 118 scenes with 1175 image views. in Those 118 scenes, 20 scenes contain 3 views, 50 scenes with 6 images, and 48 scenes with 17 images. So in total, our data contains 301 scenes and 3325 view images. Most of the scenes contain mixed objects. The dataset contains 19 objects in total.

For more info about the dataset content and collection process please refer to our Arxiv preprint

If you have any questions about the dataset, please contact anas.gouda@tu-dortmund.de

Work Zone Object Detection

## Overview

Work Zone Object Detection is a dataset for object detection tasks - it contains Work Zone Indicators annotations for 3,532 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).

This is an open source object detection model by TensorFlow in TensorFlow Lite format. While it is not recommended to use this model in production surveys, it can be useful for demonstration purposes and to get started with smart assistants in ArcGIS Survey123. You are responsible for the use of this model. When using Survey123, it is your responsibility to review and manually correct outputs.This object detection model was trained using the Common Objects in Context (COCO) dataset. COCO is a large-scale object detection dataset that is available for use under the Creative Commons Attribution 4.0 License.The dataset contains 80 object categories and 1.5 million object instances that include people, animals, food items, vehicles, and household items. For a complete list of common objects this model can detect, see Classes.The model can be used in ArcGIS Survey123 to detect common objects in photos that are captured with the Survey123 field app. Using the modelFollow the guide to use the model. You can use this model to detect or redact common objects in images captured with the Survey123 field app. The model must be configured for a survey in Survey123 Connect.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputCamera feed (either low-resolution preview or high-resolution capture).OutputImage with common object detections written to its EXIF metadata or an image with detected objects redacted.Model architectureThis is an open source object detection model by TensorFlow in TensorFlow Lite format with MobileNet architecture. The model is available for use under the Apache License 2.0.Sample resultsHere are a few results from the model.

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.

Total number of non-empty WordNet synsets: 21841 Total number of images: 14197122 Number of images with bounding box annotations: 1,034,908 Number of synsets with SIFT features: 1000 Number of images with SIFT features: 1.2 million

Aircraft Object Detection

## Overview

Aircraft Object Detection is a dataset for object detection tasks - it contains Airplanes annotations for 8,525 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).

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:

Small Object Detection

## Overview

Small Object Detection is a dataset for object detection tasks - it contains Cars Persons Signals annotations for 923 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).

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This dataset contains the results of an automated FAIR assessment for digital objects performed by the F-UJI tool, version 3.4.0 In this dataset, the digital resource identified by 10.13127/epica.1.1 was checked for FAIRness using the FAIRsFAIRmetrics_v0.5 metric. The results may have been influenced by a variety of factors, in particular the accessibility of the resource itself and the availability of external web services at the time of the assessment.

The Deformable Object Tracking Dataset, DOT, is a large real-world dataset for tracking deformable objects with little or no texture. The key technology is to use UV fluorescent markers to provide features for correspondence tracking while maintain the object's original appearance. DOT has about one million video frames of four types of deformable objects: paper, cloth, rope and hands. For each motion, DOT provideS 2D videos with and without markers from multiple viewpoints, 3D models of the deformed object, and tracked ground truth correspondences in both 2D and 3D.

Object detection is a vital part of any autonomous vision system and to obtain a high performing object detector data is needed. The object detection task aims to detect and classify different objects using camera input and getting bounding boxes containing the objects as output. This is usually done by utilizing deep neural networks.

When training an object detector a large amount of data is used, however it is not always practical to collect large amounts of data. This has led to multiple different techniques which decreases the amount of data needed. Examples of such techniques are transfer learning and domain adaptation. Working with construction equipment is a time consuming process and we wanted to examine if it was possible to use scale-model data to train a network and then used that network to detect real objects with no additional training.

This small dataset contains training and validation data of a scale dump truck in different environments while the test set contains images of a full size dump truck of similar model. The aim of the dataset is to train a network to classify wheels, cabs and tipping bodies of a scale-model dump truck and use that to classify the same classes on a full-scale dump truck.

The label structure of the dataset is the YOLO v3 structure, where the classes corresponds to a integer value, such that: Wheel: 0 Cab: 1 Tipping body: 2

Discover the Remote Sensing Object Segmentation Dataset Perfect for GIS, AI driven environmental studies, and satellite image analysis.

The Object Detection for Olfactory References (ODOR) Dataset

Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes.

Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories.

It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas.

Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.

How to use

The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes or image descriptions, as well as formal annotations, such as artist, license, or creation year.

In addition to a zip containing the dataset images, we provide links to their source collections in the metadata file and a Python script to conveniently download the artwork images (download_imgs.py).

The mapping between the images array of the annotations.json and the metadata.csv file can be accomplished via the file_name attribute of the elements of the images array and the unique File Name column of the metadata.csv file, respectively.

hf-internal-testing/tiny-object-detection-aquarium-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains the results of an automated FAIR assessment for digital objects performed by the F-UJI tool, version v1.3.8 In this dataset, the digital resource identified by https://github.com/usnistgov/opensource-repo was checked for FAIRness using the FAIRsFAIRmetrics_v0.4 metric. The results may have been influenced by a variety of factors, in particular the accessibility of the resource itself and the availability of external web services at the time of the assessment.

Valorant Object Detection

## Overview

Valorant Object Detection is a dataset for object detection tasks - it contains Agents And In Game Objects annotations for 10,305 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).

Tool Object Tracking

## Overview

Tool Object Tracking is a dataset for object detection tasks - it contains Tool annotations for 8,153 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).