Kaggle Person Detection 2

## Overview

Kaggle Person Detection 2 is a dataset for object detection tasks - it contains Person IrZn annotations for 1,111 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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https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18802242%2Fa45e9981a7e6e88d107ab928bec3d2e8%2Ffoto_no_exif-2.jpg?generation=1722318405836434&alt=media">

This dataset contains over 24,000 labels in almost 8,000 images and corresponding labels to train a YOLOv8 model to detect people and hi vis jackets: The dataset contains 2 directories, images and labels. Each image has a txt label file where each line has the class ID of the object detected followed by the normalised coordinates of the bounding box.

This dataset was used to train a YOLO model for tracking people exclusively wearing a high-vis jacket. You may find the project here - https://github.com/tudorhirtopanu/YOLO-HiVis

Person Detection With Head

## Overview

Person Detection With Head is a dataset for object detection tasks - it contains Person 7QuV Person annotations for 5,822 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).

This dataset contain 20 classes which include 'person', 'car', 'chair', 'bottle', 'pottedplant', 'bird', 'dog', 'sofa', 'bicycle', 'horse', 'boat', 'motorbike', 'cat', 'tvmonitor', 'cow', 'sheep', 'aeroplane', 'train', 'diningtable', 'bus' and also have file Image Data which contain 'Filename' 'Width' 'Height' 'Name' 'xmin' 'xmax' 'ymin' 'ymax'

A large collection of about 32k images (26k Train + 6k Validation) and clean labels in xywh (top-left x-coord, top-left y-coord, face-width, face-height) format for Human Face Detection tasks.

Under Water Person Detection

## Overview

Under Water Person Detection is a dataset for object detection tasks - it contains Human annotations for 290 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).

The authors collected and labeled a detection dataset named Human Parts Dataset which contains annotations of three categories, including person, hand and

Only Person Detection

## Overview

Only Person Detection is a dataset for object detection tasks - it contains Human Pose PeZ7 annotations for 3,713 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).

The OpenDR Humans in Fields dataset is a 2D Object Detection dataset, specifically designed for person detection in agricultural fields. A Robotti robot was deployed by AGI to collect images with a front and back camera, in a realistic scenario to mimic the images that the robot might encounter in the agricultural use case. The cameras are equipped with wide-angle lenses, contributing to the domain shift problem when applying pretrained person detectors to the task. The collected images are saved in JPG format at a resolution of 2048x1536. The dataset is split in train and test sets, and each of these is split in two subsets: a) images depicting humans, and b) images with no humans. The dataset was annotated with bounding boxes by AUTH using the labelImg tool, and the annotations are provided in PASCAL VOC .xml format.

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Medical Masks Dataset dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

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This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

🚀 You can learn more about our high-quality unique datasets here

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

🌟 Unlock the potential of advanced computer vision tasks with our comprehensive dataset comprising 15,000 high-quality images. Whether you're delving into segmentation, object detection, or image captioning, our dataset offers a diverse array of visual data to fuel your machine learning models.

🔍 Our dataset is meticulously curated to encompass a wide range of streams, ensuring versatility and applicability across various domains. From natural landscapes to urban environments, from wildlife to everyday objects, our collection captures the richness and diversity of visual content.

📊 Dataset Overview:

Total Images	Training Set (70%)	Testing Set (30%)
15,000	10,500	4,500

🔢 Image Details:

• Format: JPG
• Size Range: Approximately 150 to 300 KB per image

Embark on your computer vision journey and leverage our dataset to develop cutting-edge algorithms, advance research, and push the boundaries of what's possible in visual recognition tasks. Join us in shaping the future of AI-powered image analysis.

This Dataset is created by organizing the WIDER FACE dataset. WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We chose 32,203 images and labeled 393,703 faces with a high degree of variability in scale, pose, and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% of data as training, validation, and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset.

Original Dataset http://shuoyang1213.me/WIDERFACE/

Person Detection Fisheye

## Overview

Person Detection Fisheye is a dataset for object detection tasks - it contains Person annotations for 50 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).

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

Instructions:

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

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

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

Mini Traffic Detection dataset comprises 8 classes with 30 instances each, divided into 70% for training and 30% for validation. Primarily designed for computer vision tasks, it focuses on traffic object detection. It's an excellent choice for transfer learning with Detectron2 for custom object detection and segmentation projects. The dataset includes classes such as bicycle, bus, car, motorcycle, person, traffic_light, truck, and stop_sign.

This dataset contains annotated images for object detection for containers and hands in a first-person view (egocentric view) during drinking activities. Both YOLOV8 format and COCO format are provided.Please refer to our paper for more details.Purpose: Training and testing the object detection model.Content: Videos from Session 1 of Subjects 1-20.Images: Extracted from the videos of Subjects 1-20 Session 1.Additional Images:~500 hand/container images from Roboflow Open Source data.~1500 null (background) images from VOC Dataset and MIT Indoor Scene Recognition Dataset:1000 indoor scenes from 'MIT Indoor Scene Recognition'400 other unrelated objects from VOC DatasetData Augmentation:Horizontal flipping±15% brightness change±10° rotationFormats Provided:COCO formatPyTorch YOLOV8 formatImage Size: 416x416 pixelsTotal Images: 16,834Training: 13,862Validation: 1,975Testing: 997Instance Numbers:Containers: Over 10,000Hands: Over 8,000

Roboflow Dataset Page

https://universe.roboflow.com/ashish-cuamw/test-y7rj3

  Citation

@misc{ test-y7rj3_dataset, title = { test Dataset }, type = { Open Source Dataset }, author = { ashish }, howpublished = { \url{ https://universe.roboflow.com/ashish-cuamw/test-y7rj3 } }, url = { https://universe.roboflow.com/ashish-cuamw/test-y7rj3 }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { oct }, note… See the full description on the dataset page: https://huggingface.co/datasets/fcakyon/gun-object-detection.

Person Detection

## Overview

Person Detection is a dataset for object detection tasks - it contains Female Male annotations for 9,998 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 [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

The Bee Image Object Detection dataset was generated for the purpose of detecting bee objects within images. The dataset comprises videos captured at the entrances of 25 beehives situated in three separate apiaries in San Jose, Cupertino, and Gilroy, CA, USA. These videos were recorded directly above the landing pads of various beehives. The camera was positioned at a unique angle to capture distinct and clear images of bees engaged in activities such as taking off, landing, or moving around on the landing pad.

This dataset was record using the ROS2-foxy framework and can be utilized with:

ros2 bag play square_test_with_gt_2

Name of ROS2 topic	Type of ROS2 topic	Information
/Detections	vision_msgs/msg/Detection2DArray	This topic includes person detections from the monocular camera that is performing the Deep Learning object detection
/GT_POINT	geometry_msgs/msg/PointStamped	Contains the PointStamped message obtained from the LiDAR person detection for ground truth purposes
/distance_data_array	itrci_hardware/msg/RadioRangeDataArray	This topic has person detections from the 3 UWB Anchors relative to the person TAG (Note that is in custom ros2 message itrci_hardware)
/tf	tf2_msgs/msg/TFMessage	base_link and odom tf (robot is static)
/tf_static	tf2_msgs/msg/TFMessage	Contains tf information of LiDAR cameras and anchors relative to the robot base_link