CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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.
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"
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
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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"))
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:
Image Credit - jinfagang
!git clone https://github.com/WongKinYiu/yolov7 # Downloading YOLOv7 repository and installing requirements
%cd yolov7
!pip install -qr requirements.txt
!pip install -q roboflow
!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!
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")
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:
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")
Here, I am able to pass a number of arguments: - img: define input image size - batch: determine
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
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.
📦 Cattle_Body_Parts_OD.zip
┣ 📂 images
┃ ┣ 📜 image1.jpg
┃ ┣ 📜 image2.jpg
┃ ┗ ...
┗ 📂 annotations
┣ 📜 image1.json
┣ 📜 image2.json
┗ ...
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.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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.
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Location | Product name |
Archipelago sea | S2B_MSIL1C_20220619T100029_N0400_R122_T34VEM_20220619T104419 |
S2A_MSIL1C_20220721T095041_N0400_R079_T34VEM_20220721T115325 | |
S2A_MSIL1C_20220813T095601_N0400_R122_T34VEM_20220813T120233 | |
Gulf of Finland | S2B_MSIL1C_20220606T095029_N0400_R079_T35VLG_20220606T105944 |
S2B_MSIL1C_20220626T095039_N0400_R079_T35VLG_20220626T104321 | |
S2A_MSIL1C_20220721T095041_N0400_R079_T35VLG_20220721T115325 | |
Bothnian Bay | S2A_MSIL1C_20220627T100611_N0400_R022_T34WFT_20220627T134958 |
S2B_MSIL1C_20220712T100559_N0400_R022_T34WFT_20220712T121613 | |
S2B_MSIL1C_20220828T095549_N0400_R122_T34WFT_20220828T104748 | |
Bothnian Sea | S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043 |
S2A_MSIL1C_20220624T100041_N0400_R122_T34VEN_20220624T120211 | |
S2A_MSIL1C_20220813T095601_N0400_R122_T34VEN_20220813T120233 | |
Kvarken | S2A_MSIL1C_20220617T100611_N0400_R022_T34VER_20220617T135008 |
S2B_MSIL1C_20220712T100559_N0400_R022_T34VER_20220712T121613 | |
S2A_MSIL1C_20220826T100611_N0400_R022_T34VER_20220826T135136 |
T34VEM|-20220619|-20220721|-20220813
Product name | Number of annotations |
S2B_MSIL1C_20220619T100029_N0400_R122_T34VEM_20220619T104419 | 591 |
S2A_MSIL1C_20220721T095041_N0400_R079_T34VEM_20220721T115325 | 1518 |
S2A_MSIL1C_20220813T095601_N0400_R122_T34VEM_20220813T120233 | 1368 |
S2B_MSIL1C_20220606T095029_N0400_R079_T35VLG_20220606T105944 | 248 |
S2B_MSIL1C_20220626T095039_N0400_R079_T35VLG_20220626T104321 | 1206 |
S2A_MSIL1C_20220721T095041_N0400_R079_T35VLG_20220721T115325 | 971 |
S2A_MSIL1C_20220627T100611_N0400_R022_T34WFT_20220627T134958 | 122 |
S2B_MSIL1C_20220712T100559_N0400_R022_T34WFT_20220712T121613 | 162 |
S2B_MSIL1C_20220828T095549_N0400_R122_T34WFT_20220828T104748 | 98 |
S2B_MSIL1C_20210714T100029_N0301_R122_T34VEN_20210714T121056 | 450 |
S2A_MSIL1C_20220624T100041_N0400_R122_T34VEN_20220624T120211 | 424 |
S2A_MSIL1C_20220813T095601_N0400_R122_T34VEN_20220813T120233 | 399 |
S2A_MSIL1C_20220617T100611_N0400_R022_T34VER_20220617T135008 | 83 |
S2B_MSIL1C_20220712T100559_N0400_R022_T34VER_20220712T121613 | 183 |
S2A_MSIL1C_20220826T100611_N0400_R022_T34VER_20220826T135136 | 88 |
mean | min | 25% | 50% | 75% | max | |
Area (m²) | 5305.7 | 567.9 | 1629.9 | 2328.2 | 5176.3 | 414795.7 |
Diameter (m) | 92.5 | 33.9 | 57.9 | 69.4 | 108.3 | 913.9 |
Model | Fold | Precision | Recall | mAP50 | mAP |
yolov8n | 1 | 0,820806 | 0.838353 | 0.842 | 0.403 |
yolov8s | 4 | 0.843822 | 0.860479 | 0.865 | 0.422 |
yolov8m | 4 | 0.858263 | 0.874616 | 0.880 | 0.453 |
yolov8l | 1 | 0.840311 | 0.863553 | 0.862 | 0.443 |
yolov8x | 1 | 0.855134 | 0.859865 | 0.876 | 0.450 |
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.vesikivikko
(Water rock areas) from the Topographical database.38511
, 38512
, 38513
from the layer vesikivi
in the Topographical database.ty_njr
class ids are 1, 2, 3, 4, 5, 8tuulivoimalat
from geo2ml.scripts.data import create_coco_dataset
raster_path = '
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
## 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).
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
License information was derived automatically
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.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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.
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"
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