28 datasets found
  1. R

    Animeheads Dataset

    • universe.roboflow.com
    zip
    Updated Jul 7, 2025
    + more versions
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    nyuuzyou (2025). Animeheads Dataset [Dataset]. https://universe.roboflow.com/nyuuzyou/animeheads
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jul 7, 2025
    Dataset authored and provided by
    nyuuzyou
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Variables measured
    Head Bounding Boxes
    Description

    AnimeHeads Object Detection Dataset

    The AnimeHeadsv3 Object Detection Dataset is a collection of anime and art images, including manga pages, that have been annotated with object bounding boxes for use in object detection tasks. This dataset was used to train the final version of the Anime Object Detection Models, based on the YOLOv8l architecture.

    Contents

    The dataset contains a total of 8037 images, split into training, validation, and testing sets. The images were collected from various sources and include a variety of anime and art styles, including manga.

    Each annotation file containing the bounding box coordinates and label for each object in the corresponding image. Dataset has only one class named "head"

    Usage

    To use this dataset for object detection tasks, you can download the dataset files and annotations and use them to train your own object detection model.

    Pre-trained models based on this dataset are available on Hugging Face at the following link: - https://huggingface.co/nyuuzyou/AnimeHeads

  2. h

    yolo-rubber-ducks

    • huggingface.co
    Updated Jan 30, 2025
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    Brain Wave Collective (2025). yolo-rubber-ducks [Dataset]. https://huggingface.co/datasets/brainwavecollective/yolo-rubber-ducks
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 30, 2025
    Dataset authored and provided by
    Brain Wave Collective
    Description

    Rubber Duck Detection Dataset

      Overview
    

    This dataset contains 192 annotated images of rubber ducks, specifically curated for object detection tasks. It was used for experimentation related to the YOLOv8n Rubber Duck Detector model. NOTE: I DO NOT RECOMMEND USING THIS DATASET AT THIS TIME. There is an open and ongoing discussion around the use of the datasets that were combined for this.See related licensing discussion on the forum

      Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/brainwavecollective/yolo-rubber-ducks.
    
  3. R

    Huggingface 42 Lerobot Dataset

    • universe.roboflow.com
    zip
    Updated Jun 19, 2025
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    huggingface42lerobot (2025). Huggingface 42 Lerobot Dataset [Dataset]. https://universe.roboflow.com/huggingface42lerobot/huggingface-42-lerobot/model/4
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 19, 2025
    Dataset authored and provided by
    huggingface42lerobot
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Tokens Bounding Boxes
    Description

    Huggingface 42 Lerobot

    ## Overview
    
    Huggingface 42 Lerobot is a dataset for object detection tasks - it contains Tokens annotations for 1,411 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).
    
  4. h

    turtlebot-detection-yolo-v1

    • huggingface.co
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    Fabian, turtlebot-detection-yolo-v1 [Dataset]. https://huggingface.co/datasets/fhahn/turtlebot-detection-yolo-v1
    Explore at:
    Authors
    Fabian
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    TurtleBot Detection Dataset v1

      Dataset Description
    

    A curated dataset of 1,006 TurtleBot images with YOLO-format bounding box annotations for object detection. Contains annotations for a single class (Turtlebot).

    Homepage: https://huggingface.co/datasets/fhahn/turtlebot-detection-dataset-v1 License: CC-BY-4.0 (Attribution required) Total Images: 1,006 Classes: Turtlebot (class ID: 0)

      Usage
    

    from datasets import load_dataset

    dataset =… See the full description on the dataset page: https://huggingface.co/datasets/fhahn/turtlebot-detection-yolo-v1.

  5. R

    Custom Yolov7 On Kaggle On Custom Dataset

    • universe.roboflow.com
    zip
    Updated Jan 29, 2023
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    Owais Ahmad (2023). Custom Yolov7 On Kaggle On Custom Dataset [Dataset]. https://universe.roboflow.com/owais-ahmad/custom-yolov7-on-kaggle-on-custom-dataset-rakiq/dataset/2
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 29, 2023
    Dataset authored and provided by
    Owais Ahmad
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Person Car Bounding Boxes
    Description

    Custom Training with YOLOv7 🔥

    Some Important links

    Contact Information

    Objective

    To Showcase custom Object Detection on the Given Dataset to train and Infer the Model using newly launched YoloV7.

    Data Acquisition

    The goal of this task is to train a model that can localize and classify each instance of Person and Car as accurately as possible.

    from IPython.display import Markdown, display
    
    display(Markdown("../input/Car-Person-v2-Roboflow/README.roboflow.txt"))
    

    Custom Training with YOLOv7 🔥

    In this Notebook, I have processed the images with RoboFlow because in COCO formatted dataset was having different dimensions of image and Also data set was not splitted into different Format. To train a custom YOLOv7 model we need to recognize the objects in the dataset. To do so I have taken the following steps:

    • Export the dataset to YOLOv7
    • Train YOLOv7 to recognize the objects in our dataset
    • Evaluate our YOLOv7 model's performance
    • Run test inference to view performance of YOLOv7 model at work

    📦 YOLOv7

    https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/car-person-2.PNG" width=800>

    Image Credit - jinfagang

    Step 1: Install Requirements

    !git clone https://github.com/WongKinYiu/yolov7 # Downloading YOLOv7 repository and installing requirements
    %cd yolov7
    !pip install -qr requirements.txt
    !pip install -q roboflow
    

    Downloading YOLOV7 starting checkpoint

    !wget "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"
    
    import os
    import glob
    import wandb
    import torch
    from roboflow import Roboflow
    from kaggle_secrets import UserSecretsClient
    from IPython.display import Image, clear_output, display # to display images
    
    
    
    print(f"Setup complete. Using torch {torch._version_} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")
    

    https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67">

    I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!

    YOLOv7-Car-Person-Custom

    try:
      user_secrets = UserSecretsClient()
      wandb_api_key = user_secrets.get_secret("wandb_api")
      wandb.login(key=wandb_api_key)
      anonymous = None
    except:
      wandb.login(anonymous='must')
      print('To use your W&B account,
    Go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. 
    Get your W&B access token from here: https://wandb.ai/authorize')
      
      
      
    wandb.init(project="YOLOvR",name=f"7. YOLOv7-Car-Person-Custom-Run-7")
    

    Step 2: Assemble Our Dataset

    https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png" alt="">

    In order to train our custom model, we need to assemble a dataset of representative images with bounding box annotations around the objects that we want to detect. And we need our dataset to be in YOLOv7 format.

    In Roboflow, We can choose between two paths:

    Version v2 Aug 12, 2022 Looks like this.

    https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/Roboflow.PNG" alt="">

    user_secrets = UserSecretsClient()
    roboflow_api_key = user_secrets.get_secret("roboflow_api")
    
    rf = Roboflow(api_key=roboflow_api_key)
    project = rf.workspace("owais-ahmad").project("custom-yolov7-on-kaggle-on-custom-dataset-rakiq")
    dataset = project.version(2).download("yolov7")
    

    Step 3: Training Custom pretrained YOLOv7 model

    Here, I am able to pass a number of arguments: - img: define input image size - batch: determine

  6. h

    fruit-ripeness-detection-dataset

    • huggingface.co
    Updated Jan 28, 2025
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    Subhajit Chatterjee (2025). fruit-ripeness-detection-dataset [Dataset]. https://huggingface.co/datasets/darthraider/fruit-ripeness-detection-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 28, 2025
    Authors
    Subhajit Chatterjee
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Dataset Card for Fruit-Ripeness-Classification dataset

    This is a collection of ripe and unripe fruits (mangoes and bananas) in outside lighting and outside conditions.

    Train - 80% (4k images) Test - 20% (1k images)

    Dimensions of image : 640 x 480 The dataset has been collected from Mendeley data: https://data.mendeley.com/datasets/y3649cmgg6/3 (Mango and Banana Dataset (Ripe Unripe) : Indian RGB image datasets for YOLO object detection) Initially the data was for training YOLO… See the full description on the dataset page: https://huggingface.co/datasets/darthraider/fruit-ripeness-detection-dataset.

  7. h

    chesspiece-detection-yolo

    • huggingface.co
    Updated May 27, 2025
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    Andrea Capitani (2025). chesspiece-detection-yolo [Dataset]. https://huggingface.co/datasets/acapitani/chesspiece-detection-yolo
    Explore at:
    Dataset updated
    May 27, 2025
    Authors
    Andrea Capitani
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Chess Pieces Detection Dataset (YOLO format)

      📦 Overview
    

    This dataset is designed for object detection of chess pieces on a chessboard using YOLOv5/YOLOv10.

    Classes: 12 (6 white + 6 black pieces) Format: YOLO (txt annotations) Split: Train/Validation Image count: ~2208 images

      🗂️ Structure
    

    images/train/ images/val/ labels/train/ labels/val/ dataset.yaml

      🏷️ Class Names
    

    0 = white pawn 1 = white rook 2 = white knight 3 = white bishop 4 = white queen… See the full description on the dataset page: https://huggingface.co/datasets/acapitani/chesspiece-detection-yolo.

  8. R

    Cattle Body Parts For Object Detection Dataset

    • universe.roboflow.com
    zip
    Updated Apr 29, 2025
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    Ali KHalili (2025). Cattle Body Parts For Object Detection Dataset [Dataset]. https://universe.roboflow.com/ali-khalili/cattle-body-parts-dataset-for-object-detection
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 29, 2025
    Dataset authored and provided by
    Ali KHalili
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Temp3 Bounding Boxes
    Description

    Cattle Body Parts Image Dataset for Object Detection

    This dataset is a curated collection of images featuring various cattle body parts aimed at facilitating object detection tasks. The dataset contains a total of 428 high-quality photos, meticulously annotated with three distinct classes: "Back," "Head," and "Leg."

    The dataset can be downloaded using this link. The dataset is also available at Roboflow Universe.

    A YOLOv7X model has been trained using the dataset and achieved a mAP of 99.6%. You can access the trained weights through this link.

    Motivation

    Accurate and reliable identification of different cattle body parts is crucial for various agricultural and veterinary applications. This dataset aims to provide a valuable resource for researchers, developers, and enthusiasts working on object detection tasks involving cattle, ultimately contributing to advancements in livestock management, health monitoring, and related fields.

    Data

    Overview

    • Total Images: 428
    • Classes: Back, Head, Leg
    • Annotations: Bounding boxes for each class

    Contents

    📦 Cattle_Body_Parts_OD.zip
     ┣ 📂 images
     ┃ ┣ 📜 image1.jpg
     ┃ ┣ 📜 image2.jpg
     ┃ ┗ ...
     ┗ 📂 annotations
      ┣ 📜 image1.json
      ┣ 📜 image2.json
      ┗ ...
    

    Annotation Format

    Each annotation file corresponds to an image in the dataset and is formatted as per the LabelMe JSON standard. These annotations define the bounding box coordinates for each labeled body part, enabling straightforward integration into object detection pipelines.

    License

    This work is licensed under a Creative Commons Attribution 4.0 International License.

    Disclaimer

    This dataset has been collected from publicly available sources. I do not claim ownership of the data and have no intention of infringing on any copyright. The material contained in this dataset is copyrighted to their respective owners. I have made every effort to ensure the data is accurate and complete, but I cannot guarantee its accuracy or completeness. If you believe any data in this dataset infringes on your copyright, please get in touch with me immediately so I can take appropriate action.

  9. h

    Paimon-Dataset-YOLO-Detection

    • huggingface.co
    Updated Mar 18, 2025
    + more versions
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    Globose Technology Solutions (2025). Paimon-Dataset-YOLO-Detection [Dataset]. https://huggingface.co/datasets/gtsaidata/Paimon-Dataset-YOLO-Detection
    Explore at:
    Dataset updated
    Mar 18, 2025
    Authors
    Globose Technology Solutions
    Description

    Description: 👉 Download the dataset here This dataset consists of a diverse collection of images featuring Paimon, a popular character from the game Genshin Impact. The images have been sourced from in-game gameplay footage and capture Paimon from various angles and in different sizes (scales), making the dataset suitable for training YOLO object detection models. The dataset provides a comprehensive view of Paimon in different lighting conditions, game environments, and positions, ensuring… See the full description on the dataset page: https://huggingface.co/datasets/gtsaidata/Paimon-Dataset-YOLO-Detection.

  10. R

    Detecciones Dataset

    • universe.roboflow.com
    • huggingface.co
    zip
    Updated Jan 12, 2024
    + more versions
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    David (2024). Detecciones Dataset [Dataset]. https://universe.roboflow.com/david-bxemt/detecciones
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 12, 2024
    Dataset authored and provided by
    David
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Figura Bounding Boxes
    Description

    Detecciones

    ## Overview
    
    Detecciones is a dataset for object detection tasks - it contains Figura annotations for 1,934 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).
    
  11. Dataset for marine vessel detection from Sentinel 2 images in the Finnish...

    • zenodo.org
    bin
    Updated Jan 27, 2025
    + more versions
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    Janne Mäyrä; Janne Mäyrä; Ari-Pekka Jokinen; Ari-Pekka Jokinen (2025). Dataset for marine vessel detection from Sentinel 2 images in the Finnish coast [Dataset]. http://doi.org/10.5281/zenodo.10046342
    Explore at:
    binAvailable download formats
    Dataset updated
    Jan 27, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Janne Mäyrä; Janne Mäyrä; Ari-Pekka Jokinen; Ari-Pekka Jokinen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains annotated marine vessels from 15 different Sentinel-2 product, used for training object detection models for marine vessel detection. The vessels are annotated as bounding boxes, covering also some amount of the wake if present.

    Source data

    Individual products used to generate annotations are shown in the following table:
    LocationProduct name
    Archipelago seaS2B_MSIL1C_20220619T100029_N0400_R122_T34VEM_20220619T104419
    S2A_MSIL1C_20220721T095041_N0400_R079_T34VEM_20220721T115325
    S2A_MSIL1C_20220813T095601_N0400_R122_T34VEM_20220813T120233
    Gulf of FinlandS2B_MSIL1C_20220606T095029_N0400_R079_T35VLG_20220606T105944
    S2B_MSIL1C_20220626T095039_N0400_R079_T35VLG_20220626T104321
    S2A_MSIL1C_20220721T095041_N0400_R079_T35VLG_20220721T115325
    Bothnian BayS2A_MSIL1C_20220627T100611_N0400_R022_T34WFT_20220627T134958
    S2B_MSIL1C_20220712T100559_N0400_R022_T34WFT_20220712T121613
    S2B_MSIL1C_20220828T095549_N0400_R122_T34WFT_20220828T104748
    Bothnian SeaS2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043
    S2A_MSIL1C_20220624T100041_N0400_R122_T34VEN_20220624T120211
    S2A_MSIL1C_20220813T095601_N0400_R122_T34VEN_20220813T120233
    KvarkenS2A_MSIL1C_20220617T100611_N0400_R022_T34VER_20220617T135008
    S2B_MSIL1C_20220712T100559_N0400_R022_T34VER_20220712T121613
    S2A_MSIL1C_20220826T100611_N0400_R022_T34VER_20220826T135136
    Even though the reference data IDs are for L1C products, L2A products from the same acquisition dates can be used along with the annotations. However, Sen2Cor has been known to produce incorrect reflectance values for water bodies.


    The raw products can be acquired from Copernicus Data Space Ecosystem.

    Annotations


    The annotations are bounding boxes drawn around marine vessels so that some amount of their wakes, if present, are also contained within the boxes. The data are distributed as geopackage files, so that one geopackage corresponds to a single Sentinel-2 tile, and each package has separate layers for individual products as shown below:

    T34VEM
    |-20220619
    |-20220721
    |-20220813

    All layers have a column id, which has the value boat for all annotations.

    CRS is EPSG:32634 for all products except for the Gulf of Finland (35VLG), which is in EPSG:32635. This is done in order to have the bounding boxes to be aligned with the pixels in the imagery.

    As tiles 34VEM and 34VEN have an overlap of 9.5x100 km, 34VEN is not annotated from the overlapping part to prevent data leakage between splits.

    Annotation process

    The minimum size for an object to be considered as a potential marine vessel was set to 2x2 pixels. Three separate acquisitions for each location were used to detect smallest objects, so that if an object was located at the same place in all images, then it was left unannotated. The data were annotated by two experts.
    Product nameNumber of annotations
    S2B_MSIL1C_20220619T100029_N0400_R122_T34VEM_20220619T104419591
    S2A_MSIL1C_20220721T095041_N0400_R079_T34VEM_20220721T1153251518
    S2A_MSIL1C_20220813T095601_N0400_R122_T34VEM_20220813T1202331368
    S2B_MSIL1C_20220606T095029_N0400_R079_T35VLG_20220606T105944248
    S2B_MSIL1C_20220626T095039_N0400_R079_T35VLG_20220626T1043211206
    S2A_MSIL1C_20220721T095041_N0400_R079_T35VLG_20220721T115325971
    S2A_MSIL1C_20220627T100611_N0400_R022_T34WFT_20220627T134958122
    S2B_MSIL1C_20220712T100559_N0400_R022_T34WFT_20220712T121613162
    S2B_MSIL1C_20220828T095549_N0400_R122_T34WFT_20220828T10474898
    S2B_MSIL1C_20210714T100029_N0301_R122_T34VEN_20210714T121056450
    S2A_MSIL1C_20220624T100041_N0400_R122_T34VEN_20220624T120211424
    S2A_MSIL1C_20220813T095601_N0400_R122_T34VEN_20220813T120233399
    S2A_MSIL1C_20220617T100611_N0400_R022_T34VER_20220617T13500883
    S2B_MSIL1C_20220712T100559_N0400_R022_T34VER_20220712T121613183
    S2A_MSIL1C_20220826T100611_N0400_R022_T34VER_20220826T13513688


    Annotation statistics


    Sentinel-2 images have spatial resolution of 10 m, so below statistics can be converted to pixel sizes by dividing them by 10 (diameter) pr 100 (area).
    meanmin25%50%75%max
    Area (m²)5305.7567.91629.92328.25176.3414795.7
    Diameter (m)92.533.957.969.4108.3913.9


    As most of the annotations cover also most of the wake of the marine vessel, the bounding boxes are significantly larger than a typical boat. There are a few annotations larger than 100 000 m², which are either cruise or cargo ships that are travelling along ordinal directions instead of cardinal directions, instead of e.g. smaller leisure boats.

    Annotations typically have diameter less than 100 meters, and the largest diameters correspond to similar instances than the largest bounding box areas.

    Train-test-split


    We used tiles 34VEN and 34VER as the test dataset. The results acquired using RGB mosaics generated from L1C images are shown in the below table
    ModelFoldPrecisionRecallmAP50mAP
    yolov8n10,8208060.8383530.8420.403
    yolov8s40.8438220.8604790.8650.422
    yolov8m40.8582630.8746160.8800.453
    yolov8l10.8403110.8635530.8620.443
    yolov8x10.8551340.8598650.8760.450


    Before evaluating, the predictions for the test set are cleaned using the following steps:

    1. All prediction whose centroid points are not located on water are discarded. The water mask used contains layers jarvi (Lakes), meri (Sea) and virtavesialue (Rivers as polygon geometry) from the Topographical database by the National Land Survey of Finland. Unfortunately this also discards all points not within the Finnish borders.
    2. All predictions whose centroid points are located on water rock areas are discarded. The mask is the layer vesikivikko (Water rock areas) from the Topographical database.
    3. All predictions that contain an above water rock within the bounding box are discarded. The mask contains classes 38511, 38512, 38513 from the layer vesikivi in the Topographical database.
    4. All predictions that contain a lighthouse or a sector light within the bounding box are discarded. Lighthouses and sector lights come from Väylävirasto data, ty_njr class ids are 1, 2, 3, 4, 5, 8
    5. All predictions that are wind turbines, found in Topographical database layer tuulivoimalat
    6. All predictions that are obviously too large are discarded. The prediction is defined to be "too large" if either of its edges is longer than 750 meters.
    Model checkpoints are available on Hugging Face platform: https://huggingface.co/mayrajeo/marine-vessel-detection-yolov8

    Usage

    The simplest way to chip the rasters into suitable format and convert the data to COCO or YOLO formats is to use geo2ml. First download the raw mosaics and convert them into GeoTiff files and then use the following to generate the datasets.
    To generate COCO format dataset run
    from geo2ml.scripts.data import create_coco_dataset
    raster_path = '
  12. R

    Ecg_labeled_marzo Dataset

    • universe.roboflow.com
    • huggingface.co
    zip
    Updated Mar 10, 2023
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    centro oncolgico integral (2023). Ecg_labeled_marzo Dataset [Dataset]. https://universe.roboflow.com/centro-oncolgico-integral/ecg_labeled_marzo/dataset/1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Mar 10, 2023
    Dataset authored and provided by
    centro oncolgico integral
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Ecg Bounding Boxes
    Description

    Ecg_labeled_Marzo

    ## Overview
    
    Ecg_labeled_Marzo is a dataset for object detection tasks - it contains Ecg annotations for 284 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).
    
  13. R

    Football Players Dataset

    • universe.roboflow.com
    • huggingface.co
    zip
    Updated Jun 12, 2023
    + more versions
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    Konstantin Sargsyan (2023). Football Players Dataset [Dataset]. https://universe.roboflow.com/konstantin-sargsyan-wucpb/football-players-2l81z/dataset/1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 12, 2023
    Dataset authored and provided by
    Konstantin Sargsyan
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Variables measured
    Players Bounding Boxes
    Description

    Football Players

    ## Overview
    
    Football Players is a dataset for object detection tasks - it contains Players annotations for 206 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 [MIT license](https://creativecommons.org/licenses/MIT).
    
  14. h

    Drone_Detection

    • huggingface.co
    Updated Dec 15, 2023
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    Wenjun Yu (2023). Drone_Detection [Dataset]. https://huggingface.co/datasets/ywanny/Drone_Detection
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 15, 2023
    Authors
    Wenjun Yu
    Description

    Dataset Card for Dataset Name

    Credit: https://www.kaggle.com/datasets/dasmehdixtr/drone-dataset-uavThis is a dataset from the above the link. It's used for object detection training on yolo model for the class of drone.

      Dataset Details
    
    
    
    
    
      Dataset Description
    

    Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More… See the full description on the dataset page: https://huggingface.co/datasets/ywanny/Drone_Detection.

  15. h

    conequest_detection

    • huggingface.co
    Updated May 11, 2025
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    Kunal Kasodekar (2025). conequest_detection [Dataset]. https://huggingface.co/datasets/gremlin97/conequest_detection
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    Dataset updated
    May 11, 2025
    Authors
    Kunal Kasodekar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    conequest_detection Dataset

    An object detection dataset in YOLO format containing 3 splits: train, val, test.

      Dataset Metadata
    

    License: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Version: 1.0 Date Published: 2025-05-11 Cite As: TBD

      Dataset Details
    

    Format: YOLO

    Splits: train, val, test

    Classes: cone

      Additional Formats
    

    Includes COCO format annotations Includes Pascal VOC format annotations

      Usage
    

    from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/gremlin97/conequest_detection.

  16. h

    dust_devil_detection

    • huggingface.co
    Updated May 11, 2025
    + more versions
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    Kunal Kasodekar (2025). dust_devil_detection [Dataset]. https://huggingface.co/datasets/gremlin97/dust_devil_detection
    Explore at:
    Dataset updated
    May 11, 2025
    Authors
    Kunal Kasodekar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    dust_devil_detection Dataset

    An object detection dataset in YOLO format containing 3 splits: train, val, test.

      Dataset Metadata
    

    License: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Version: 1.0 Date Published: 2025-05-11 Cite As: TBD

      Dataset Details
    

    Format: YOLO

    Splits: train, val, test

    Classes: dustdevil

      Additional Formats
    

    Includes COCO format annotations Includes Pascal VOC format annotations

      Usage
    

    from datasets… See the full description on the dataset page: https://huggingface.co/datasets/gremlin97/dust_devil_detection.

  17. h

    boulder_detection

    • huggingface.co
    Updated May 16, 2025
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    Kunal Kasodekar (2025). boulder_detection [Dataset]. https://huggingface.co/datasets/gremlin97/boulder_detection
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    Dataset updated
    May 16, 2025
    Authors
    Kunal Kasodekar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Boulder
    Description

    boulder_detection Dataset

    An object detection dataset in YOLO format containing 3 splits: train, val, test.

      Dataset Metadata
    

    License: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Version: 1.0 Date Published: 2025-05-16 Cite As: TBD

      Dataset Details
    

    Format: YOLO

    Splits: train, val, test

    Classes: boulder

      Additional Formats
    

    Includes COCO format annotations Includes Pascal VOC format annotations

      Data Format
    

    This dataset… See the full description on the dataset page: https://huggingface.co/datasets/gremlin97/boulder_detection.

  18. h

    VisDrone-Dataset

    • huggingface.co
    Updated Jul 1, 2025
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    Banuprasad B (2025). VisDrone-Dataset [Dataset]. https://huggingface.co/datasets/banu4prasad/VisDrone-Dataset
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    Dataset updated
    Jul 1, 2025
    Authors
    Banuprasad B
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    VisDrone Dataset (YOLO Format)

      Overview
    

    This repository contains the VisDrone dataset converted into the YOLO (You Only Look Once) format. The VisDrone dataset is a large-scale benchmark for object detection, segmentation, and tracking in drone videos. The dataset includes a variety of challenging scenarios with diverse objects and backgrounds.

      Dataset Details
    

    Classes: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8:… See the full description on the dataset page: https://huggingface.co/datasets/banu4prasad/VisDrone-Dataset.

  19. h

    mb-boulder_det

    • huggingface.co
    Updated May 23, 2025
    + more versions
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    Mirali Purohit (2025). mb-boulder_det [Dataset]. https://huggingface.co/datasets/Mirali33/mb-boulder_det
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    Dataset updated
    May 23, 2025
    Authors
    Mirali Purohit
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Boulder
    Description

    mb-boulder_det Dataset

    An object detection dataset in YOLO format containing 8 splits: train, val, test, 0.50x_partition, 0.20x_partition, 0.05x_partition, 0.10x_partition, 0.25x_partition.

      Dataset Metadata
    

    License: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Version: 1.0 Date Published: 2025-05-16 Cite As: TBD

      Dataset Details
    

    Format: YOLO Splits: train, val, test, 0.50x_partition, 0.20x_partition, 0.05x_partition, 0.10x_partition… See the full description on the dataset page: https://huggingface.co/datasets/Mirali33/mb-boulder_det.

  20. h

    dawn-dataset

    • huggingface.co
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    Max clouser, dawn-dataset [Dataset]. https://huggingface.co/datasets/Maxim37/dawn-dataset
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    Authors
    Max clouser
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Description

    Dawn Dataset

    This dataset contains images with annotations for object detection in the YOLO format, converted to absolute coordinates for easier use. The dataset is focused on detecting vehicles and people in various environments.

      Dataset Source
    

    This dataset is based on the DAWN (Detection in Adverse Weather Nature) dataset, which was originally published by Mourad KENK on Mendeley Data:

    Original Source: DAWN Dataset on Mendeley

    The original DAWN dataset consists of… See the full description on the dataset page: https://huggingface.co/datasets/Maxim37/dawn-dataset.

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nyuuzyou (2025). Animeheads Dataset [Dataset]. https://universe.roboflow.com/nyuuzyou/animeheads

Animeheads Dataset

animeheads

animeheads-dataset

Explore at:
zipAvailable download formats
Dataset updated
Jul 7, 2025
Dataset authored and provided by
nyuuzyou
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Variables measured
Head Bounding Boxes
Description

AnimeHeads Object Detection Dataset

The AnimeHeadsv3 Object Detection Dataset is a collection of anime and art images, including manga pages, that have been annotated with object bounding boxes for use in object detection tasks. This dataset was used to train the final version of the Anime Object Detection Models, based on the YOLOv8l architecture.

Contents

The dataset contains a total of 8037 images, split into training, validation, and testing sets. The images were collected from various sources and include a variety of anime and art styles, including manga.

Each annotation file containing the bounding box coordinates and label for each object in the corresponding image. Dataset has only one class named "head"

Usage

To use this dataset for object detection tasks, you can download the dataset files and annotations and use them to train your own object detection model.

Pre-trained models based on this dataset are available on Hugging Face at the following link: - https://huggingface.co/nyuuzyou/AnimeHeads

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