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Convert Dataset Formats Download datasets; The fiftyone convert command; Convert CIFAR-10 dataset; Convert KITTI dataset; Cleanup; 330 E Liberty St Ann Arbor, MI Once we download the KITTI dataset, we can use the built-in dataset converter to convert them to TFRecords with the command: tlt-dataset-convert -d dataset conversion

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QA Dataset ConverterIn this repository, we release code from the paper What do Models Learn from Question Answering Datasets? by Priyanka Sen and Amir Saffari.These scripts convert four popular question answering datasets into a common format based on SQuAD 2.0 to allow for easier probing and experimentation. An example of a question in the SQuAD 2.0 format is shown below:{ "context": "The Normans were the people who in the 10th and 11th centuries..." "qas": [ { "question": "In what country is Normandy located?", "id": "56ddde6b9a695914005b9628", "answers": [ { "text": "France", "answer_start": 159 } ], "is_impossible": false }...In the following sections, we guide you through converting TriviaQA, Natural Question, QuAC, and NewsQA into a SQuAD 2.0 format.TriviaQAStep 1Clone this repo and go into the TriviaQA directory.cd qa-dataset-converter/triviaqaStep 2Download the TriviaQA dataset from This will include a qa directory with question-answer files and an evidence containing the documents for context.Step 3Clone the TriviaQA repo.git clone 4Move our triviaqa_to_squad.py script into the TriviaQA repo.mv triviaqa_to_squad.py triviaqa/Step 5Set --triviaqa_file to a file in your qa directory and --data_dir to the Wikipedia path in your evidence directory. Run:python triviaqa_to_squad.py --triviaqa_file qa/wikipedia-train.json --data_dir evidence/wikipedia/ --output_file triviaqa_train.jsonpython triviaqa_to_squad.py --triviaqa_file qa/wikipedia-dev.json --data_dir evidence/wikipedia/ --output_file triviaqa_dev.jsonThis will return two files triviaqa_train.json and triviaqa_dev.json in the SQuAD 2.0 format.Natural QuestionsStep 1Clone this repo and go into the Natural Questions directory.cd qa-dataset-converter/nqStep 2Download the Natural Questions dataset from This will download train and dev directories of jsonl.gz files.Step 3Set --nq_dir to your Natural Questions train or dev directory. Run:python nq_to_squad.py --nq_dir train/ --output_file nq_train.jsonpython nq_to_squad.py --nq_dir dev/ --output_file nq_dev.jsonThis will return two files nq_train.json and nq_dev.json in the SQuAD 2.0 format.QuACStep 1Clone this repo and go into the QuAC directorycd qa-dataset-converter/quacStep 2Download the QuAC dataset from 3Set --quac_file to the path of your QuAC train or dev file. Run:python quac_to_squad.py --quac_file train_v0.2.json --output_file quac_train.jsonpython quac_to_squad.py --quac_file val_v0.2.json --output_file quac_dev.jsonThis will return two files quac_train.json and quac_dev.json in the SQuAD 2.0 format.NewsQAStep 1Clone this repo and go into the NewsQA directorycd qa-dataset-converter/newsqaStep 2Follow the instructions at to build the NewsQA dataset. This will result in a directory called split_data with train, dev, and test CSVs.Step 3Note: If you used a Python 2.7 conda environment to set up NewsQA, make sure to deactivate your environment before this step.Set --newsqa_file to the path of a NewsQA file in the split_data directory. Run:python newsqa_to_squad.py --newsqa_file split_data/train.csv --output_file newsqa_train.jsonpython newsqa_to_squad.py --newsqa_file split_data/dev.csv --output_file newsqa_dev.jsonAcknowledgementsOur TriviaQA script modifies code released in TrivaiQA repo In particular, we take inspiration from convert_to_squad_format.py for all our scripts.We also use modified code from the Nautral Question browser script to process Natural Questions examples.We are thankful to the authors for making this code available.LicenseThis code is licensed under the Apache License, Version

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FOR IMMEDIATE RELEASEtetra4D Releases 3D PDF Converter Version 4.1Latest software release contains powerful enhancements for democratizing 3D dataBend, OR, USA – March 31, 2014 – tetra4D, a division of Tech Soft 3D, today announces the latest release of 3D PDF Converter, a powerful solution for creating 3D-based documents to support Model Based Definition (MBD). Customers can immediately take advantage of the most significant upgrade to the industry-leading software for democratizing 3D data yet delivered.This release is the result of many developer-years spent incorporating several hundred changes including new features, customer requests, and updated support for various 3D formats. New support has been added for Autodesk® Inventor® 2014, CATIA V5-6R2013 (R23), Rhino 4.5, Solid Edge ST6, SolidWorks 2014, and NX 9.0, among others. More details can be found in the Release Notes. Click here to download them.Customers on active maintenance and support receive this upgrade for free. Those customers without active maintenance contracts should contact their reseller partner to request upgrade pricing, or contact us at sales@tetra4d.com“This release of 3D PDF Converter has significant advantages for anyone who needs to adhere to ASME Y14.41 and other PMI standards,” says Bryan R. Fischer, President of Advanced Dimensional Management LLC and MBD360 LLC. “It will now be possible for organizations of any size to leverage 3D PDF for dynamic 3D annotation with visual feedback, without the need for traditional 2D drawings. This significantly reduces costs and complexity, and increases the communication index of the dataset. The dataset provides information in a manner that is an order of magnitude easier to understand.”“We are very pleased to announce this critical milestone for delivering on the commitment we have made to our customers to continue to lead the market with cost-effective solutions for deployment of 3D PDF-enabled workflows,” said Dave Opsahl, tetra4D President and Vice President of Corporate Development for Tech Soft 3D, tetra4D’s parent company. “With the widespread adoption we are now seeing across industries throughout their supply chains, we expect this release will accelerate 3D PDF use and adoption significantly.”FREE TRIAL3D PDF Converter and 3D PDF Converter Suite, which includes Adobe® Acrobat® XI Pro,

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Nutrition5k: A Comprehensive Nutrition Dataset Paper • Download Data • Dataset Contents • License & ContactNutrition5k is a dataset of visual and nutritional data for ~5k realistic plates of food captured from Google cafeterias using a custom scanning rig. We are releasing this dataset alongside our recent CVPR 2021 paper to help promote research in visual nutrition understanding. Please see the paper for more details on the dataset and follow-up experiments.Key Features Scans data for 5,006 plates of food, each containing: 4 rotating side-angle videos Overhead RGB-D images (when available) Fine-grained list of ingredients Per-ingredient mass Total dish mass and calories Fat, protein, and carbohydrate macronutrient masses Official train/test split Nutrition regression eval scripts→ [Also, see our related Mobile Food Segmentation model on TensorFlow Hub]Download DataAll Nutrition5k data can be downloaded directly from our Google Cloud Storage bucket, or from the .tar.gz download link below. nutrition5k_dataset.tar.gz (181.4 GB)From the Cloud Storage bucket directory, you can also browse through the dataset folders and download specific files using the gsutil cp command:gsutil -m cp -r "gs://nutrition5k_dataset/nutrition5k_dataset/{FILE_OR_DIR_PATH}" .See here for instructions on installing the gsutil tool.Examples Example side-angle and overhead frames, with nutrition labels. Example of the incremental scanning procedure.Dataset contentsSide-Angle VideosVideo recordings were captured using 4 separate Raspberry Pi cameras (labeled A-D) at alternating 30 degree and 60 degree viewing angles. The cameras are positioned 90 degrees apart and sweep 90 degrees during video capture so that the dish is captured from all sides.Video files are found in imagery/side_angles/ and are organized. Convert Dataset Formats Download datasets; The fiftyone convert command; Convert CIFAR-10 dataset; Convert KITTI dataset; Cleanup; 330 E Liberty St Ann Arbor, MI

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Get dataSource: Open ImagesThis dataset contains a collection of ~9 million images that have been annotated with image-level labels and object bounding boxes.The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images, making it the largest dataset to exist with object location annotations.Luckily, you won’t need to register on the site or leave any personal details to access the dataset, enabling you directly download the dataset from the website with no barriers.Dataset: Get dataIn case you haven’t heard already, Google have recently released a new dataset search tool that could come in handy when you have specific requirements.Source: Faces in the WildThis portal contains 13,000 labeled images of human faces you’re able to use in any of your Machine Learning projects involving facial recognition.To download the dataset, simply click on the below link and you’ll find a sub-header named ‘Download the Database’ where you can select which file is most appropriate to download to use in your projects.You won’t need to worry about registering or leaving your details to access the dataset either, making it super easy to download the files you need and get started on your projects!Dataset: Get DataSource: Dogs DatasetContains 20,580 images and 120 different dog breed categories.Created using images from ImageNet, this dataset from Stanford contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorisation.To download the dataset, you can visit

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Site, hover over the ‘download’ menu dropdown, then select ‘original images’. Provided you’re using the datasets for educational/personal use, you can submit for access to download the original images.ImageNet also are also currently running a competition on Kaggle — check it out here.Dataset: Get DataSource: dataset is good for scene understanding with ancillary task projects (room layout estimation, saliency prediction, etc.).The vast dataset, containing images from various rooms (as depicted above), can be downloaded by visiting the website and running the script provided, found here.You can find more information about the dataset by scrolling down to the ‘scene classification’ header and clicking ‘README’ to access the documentation and demo code.Dataset: Get dataCOIL100The Columbia University Image Library dataset features 100 different objects — ranging from toys, personal care items, tablets and so on — imaged at every angle in a 360° rotation.The website doesn’t require you to register or leave any details to download the dataset, making it an easy process. To directly download the dataset, simply click the link below.Dataset: Get dataSource: Genome:This dataset portal is a detailed visual knowledge base with captioning of 108,077 images which range from people, to buildings, to signs and lots more.The website outlines the following features:108,077 Images5.4 Million Region Descriptions1.7 Million Visual Question Answers3.8 Million Object Instances2.8 Million Attributes2.3 Million RelationshipsThe website doesn’t require you to leave any details or register to download the datasets available, simply click the link below to visit the website and download the objects, relationships and aliases you need.Dataset:

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NYUDepthV2_PointCloud_ConverterWhat is this repository for?The purpose of this repo is to help you convert NYU Depth V2 Dataset into a Point Cloud Dataset. I wanted to test how well a 3D Semantic Segmentation model (like PointTransformerV3) can be trained on NYU Depth V2. In order to do so, I first had to convert NYU Depth V2 dataset into a Point Cloud dataset. The NYU Depth V2 Dataset I am converting has 1449 images in total. There are 41 classes including diverse objects. To view the names of the classes click here. If you want to check out more about the NYU Depth V2 dataset, then click here.Visualization Reuslt(Left: RGB, Middle: Depth, Right: Converted Point Cloud)PrerequisitesBelow are some knowledge that you need to have in order to understand how my repo works. However, if you just want to use the conversion code, then you don't need to know about these knowledge.Depth ImagePoint CloudCoding & Tech skills (Linux, Python, Numpy, Conda, Shell)3D Reconstruction Process (2D Image Coordinate System → 3D Camera Coodinate System)If you want to learn about 3D reconstruction, then read this post. It is written by me :D 3D ReconNYU Dataset DescriptionThis is how the converted dataset is going to be structed after the conversion process is all over.DatasetNumber of DataTotal Size (GB)Average Data Size (MB)Train7951620.13Test6541319.88Prepare NYU Depth V2 DataThe simplest way is to download NYU dataset is to just download the whole NYU dataset. However the size is too big. So I will tell you a faster way to

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3DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree ViewsProject page | PaperFree Commercial Usage3DRealCar dataset is allowed commercial usage for free. The dataset license is Apache-2.0.😮 HighlightsOur 3DRealCar is the first large-scale 3D real car dataset containing both densely captured RGB-D images and point clouds.This dataset is the first to provide 3D car parsing map annotations.🚩 UpdatesWelcome to watch 👀 this repository for the latest updates.✅ [2024.6.10] : We have released our paper, 3DRealCar on arXiv.✅ [2024.6.10] : Release project page.✅ [2024.8.13] : Code is realeased.✅ [2024.8.15] : We are releasing our dataset. You can download our dataset in the project page.✅ [2025.1.21] : Release 3DRealCar dataset download page.How to usePlease refer to our code data_preprocess✏️ CitationIf you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝.@article{du20243drealcar, title={3DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree Views}, author={Du, Xiaobiao and Sun, Haiyang and Wang, Shuyun and Wu, Zhuojie and Sheng, Hongwei and Ying, Jiaying and Lu, Ming and Zhu, Tianqing and Zhan, Kun and Yu, Xin}, journal={arXiv preprint arXiv:2406.04875}, year={2024}}. Convert Dataset Formats Download datasets; The fiftyone convert command; Convert CIFAR-10 dataset; Convert KITTI dataset; Cleanup; 330 E Liberty St Ann Arbor, MI

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50 free Machine Learning Datasets: Image DatasetsContinuing on from the last two instalments of the series, part three of the Machine Learning dataset series focuses on where can you find the right image dataset to train your Machine Learning models.For those that are new to the series, the first post focused on outlining some unique dataset portals, and governmental dataset portals, whilst the second instalment outlined various portals you’re able to locate financial and economic datasets for your projects.In this post, you’ll find various datasets and links to portals you’re able to visit to find the perfect image dataset that’s relevant to your projects. Enjoy!Image dataset portalsSource: website contains a large dataset of annotated images.Downloading them isn’t easy though. There are two ways you can download the dataset:1. Downloading all the images via the LabelMe Matlab toolbox. The toolbox will allow you to customise the portion of the database that you want to download.2. Using the images online via the LabelMe Matlab toolbox. This option is less preferred as it will be slower, but it will allow you to explore the dataset before downloading it. Once you have installed the database, you can use the LabelMe Matlab toolbox to read the annotation files and query the images to extract specific objects.Dataset: Get dataImageNetThe image dataset for new algorithms is organised according to the WordNet hierarchy, in which each node of the hierarchy is depicted by hundreds and thousands of images.Downloading datasets isn’t instant though, you’ll have to register on the

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Data option lets you narrow down your search by state or district. This is particularly useful for finding specific National Parks within a region, providing more precise datasets for analysis.Step 4: Visualize Data on the MapClick “Add on Map” to overlay the data on the Map analysis interface. This helps visualize spatial relationships and patterns, supporting better insights. Users can add data to a new map or integrate it into an existing one.Step 5: Download the DataOnce satisfied with your selection, click “Download Data.” Choose between sample data or the full dataset. Select your preferred format—Shapefile, KML, or any of the 15+ supported options. Agree to the terms and initiate the download.ConclusionGIS Data by MAPOG allows users to freely download National Park data in multiple formats. It provides accurate and current geographic information for applications like conservation, environmental studies, and policy planning. This platform serves as an important tool both for professionals and enthusiasts in GIS.With MAPOG’s versatile toolkit, you can effortlessly upload vector and raster files, add WMS (Web Map Service) layers, upload Excel or CSV data, incorporate existing files, perform polygon splitting and merging, generate new polygon and polyline data, use the converter for various formats, conduct buffer analysis, create grids, transform points into polygons, calculate isochrones, and utilize the geocoder for precise location information.For any questions or further assistance, feel free to reach out to us at support@mapog.com. We’re here to help you make the most of your GIS data.Download Shapefile for the following:World Countries ShapefileAustraliaArgentinaAustriaBelgiumBrazilCanadaDenmarkFijiFinlandGermanyGreeceIndiaIndonesiaIrelandItalyJapanKenyaLebanonMadagascarMalaysiaMexicoMongoliaNetherlandsNew ZealandNigeriaPapua. Convert Dataset Formats Download datasets; The fiftyone convert command; Convert CIFAR-10 dataset; Convert KITTI dataset; Cleanup; 330 E Liberty St Ann Arbor, MI

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Zhang, Kang; Goldbaum, Michael (2018), “Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images”, Mendeley Data, V3, doi: 10.17632/rscbjbr9sj.3SourceIf you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:@misc{ visualization-tools-for-zhang-lab-data-oct-dataset, title = { Visualization Tools for ZhangLabData: OCT Dataset }, type = { Computer Vision Tools }, author = { Dataset Ninja }, howpublished = { \url{ } }, url = { }, journal = { Dataset Ninja }, publisher = { Dataset Ninja }, year = { 2025 }, month = { mar }, note = { visited on 2025-03-22 },}Download #Dataset ZhangLabData: OCT can be downloaded in Supervisely format:As an alternative, it can be downloaded with dataset-tools package:pip install --upgrade dataset-tools… using following python code:import dataset_tools as dtoolsdtools.download(dataset='ZhangLabData: OCT', dst_dir='~/dataset-ninja/')Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.The data in original format can be downloaded here.. . .Disclaimer #Our gal from the legal dep told us we need to post this:Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.