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'

Small Objects Detection

## Overview

Small Objects Detection is a dataset for object detection tasks - it contains Maritime annotations for 44 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 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.

https://i.imgur.com/s4PgS4X.gif" alt="CreateML Output">

Info

The dataset contains 7 classes of underwater creatures with provided bboxes locations for every animal. The dataset is already split into the train, validation, and test sets.

Data

It includes 638 images. - Creatures are annotated in YOLO v5 PyTorch format

Pre-Processing

The following pre-processing was applied to each image: - Auto-orientation of pixel data (with EXIF-orientation stripping) - Resize to 1024x1024 (Fit within)

Class Breakdown

The following classes are labeled: ['fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', 'stingray']. Most images contain multiple bounding boxes.

https://i.imgur.com/lFzeXsT.png" alt="Class Balance">

People Tracking & Object Detection dataset

The dataset comprises of annotated video frames from positioned in a public space camera. The tracking of each individual in the camera's view has been achieved using the rectangle tool in the Computer Vision Annotation Tool (CVAT).

The dataset is created on the basis of Real-Time Traffic Video Dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fc5a8dc4f63fe85c64a5fead10fad3031%2Fpersons_gif.gif?generation=1690705558283123&alt=media" alt="">

Dataset Structure

• The images directory houses the original video frames, serving as the primary source of raw data.
• The annotations.xml file provides the detailed annotation data for the images.
• The boxes directory contains frames that visually represent the bounding box annotations, showing the locations of the tracked individuals within each frame. These images can be used to understand how the tracking has been implemented and to visualize the marked areas for each individual.

Data Format

The annotations are represented as rectangle bounding boxes that are placed around each individual. Each bounding box annotation contains the position ( xtl-ytl-xbr-ybr coordinates ) for the respective box within the frame. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4f274551e10db2754c4d8a16dff97b33%2Fcarbon%20(10).png?generation=1687776281548084&alt=media" alt="">

👉 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

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

keywords: multiple people tracking, human detection dataset, object detection dataset, people tracking dataset, tracking human object interactions, human Identification tracking dataset, people detection annotations, detecting human in a crowd, human trafficking dataset, deep learning object tracking, multi-object tracking dataset, labeled web tracking dataset, large-scale object tracking dataset

DATASET SAMPLE

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model in multiclass object detection -- and it's 100% free! Just create an EDU account here. This HuggingFace dataset is a 20 image and label sample, but you can get the rest at no cost by creating a FalconCloud account. Once you verify your email, the link will redirect you to the dataset page. What makes this dataset unique, useful, and capable of bridging the Sim2Real gap?

The digital twins are… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Multiclass-Object-Detection-Dataset.

A comprehensive dataset of images annotated with 26 different urban and outdoor object classes, ideal for training AI models for object detection, autonomous vehicles, smart surveillance, and AR applications.

Explore our detailed Small Object Detection Dataset designed for AI and machine learning applications.

Here are a few use cases for this project:

1. Traffic Management: The model could be used in traffic management systems to detect live objects and classify them for improved traffic data analysis. It could help monitor traffic, track vehicles types, and assist in planning and controlling traffic flow.

2. Road Anomaly Detection: The model could provide valuable insights related to road conditions by detecting damages to guide repair and maintenance. This could be instrumental in maintaining road infrastructure.

3. Autonomous Driving: Utilizing this model, autonomous vehicles could determine not just other vehicles in their vicinity, but also analyze conditions of the road to adopt best route or avoid potential accidents.

4. Safety and Monitoring: This model could also be used in surveillance systems for safety purposes, assisting in identifying classified objects that could pose threats or disruptions.

5. Smart Cities Planning: Urban planners could use the model's data on traffic patterns and road conditions to inform advanced city planning, especially in terms of transport routes and road improvements.

Grain And Objects Detection

## Overview

Grain And Objects Detection is a dataset for object detection tasks - it contains Maize Seed annotations for 2,107 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).

🌟 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:

🔢 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.

Fruit Object Detection is a dataset for an object detection task. Possible applications of the dataset could be in the food industry. The dataset consists of 4474 images with 22576 labeled objects belonging to 11 different classes including pear, apple, grape, and other: pineapple, durian, korean melon, watermelon, tangerine, lemon, cantaloupe, and dragon fruit

A comprehensive dataset for detecting and classifying traffic vehicles in diverse road and environmental conditions, suitable for training advanced computer vision and autonomous driving models.

HoaAn2003/Object-Detection-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

nihany/car-object-detection dataset hosted on Hugging Face and contributed by the HF Datasets community

A diverse compilation of human facial images encompassing various races, age groups, and profiles, with the aim of creating an unbiased dataset that includes coordinates of facial regions suitable for training object detection models.

Buy me a coffee: https://bmc.link/baghbidi

Vegetables Object Detection Dataset

Dataset Description

Abstract

This dataset contains images of various vegetables, annotated with bounding boxes for object detection tasks.

Dataset Information

Format

The dataset is in the format of images (PNG) and annotations (TXT).

Collection Process

The images were taken by the dataset creator and manually annotated.

Roboflow Dataset Page

https://universe.roboflow.com/augmented-startups/football-player-detection-kucab

  Citation

@misc{ football-player-detection-kucab_dataset, title = { Football-Player-Detection Dataset }, type = { Open Source Dataset }, author = { Augmented Startups }, howpublished = { \url{ https://universe.roboflow.com/augmented-startups/football-player-detection-kucab } }, url = {… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/football-object-detection.

This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

This dataset is an extremely challenging set of over 8000+ images of table in home, which are captured and crowdsourced from over 5000+ urban and rural locations, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. It contains a wide variety table like study table, work table, dinning table etc..

Dataset Features

• Dataset size : 8000+ images
• Captured by : Over 5000+ crowdsource contributors
• Resolution : HD and above (1920x1080 and above)
• Location : Captured with 100+ locations across India
• Diversity : Various lighting conditions like day, night, varied distances, view points etc.
• Device used : Captured using mobile phones in 2020-2022
• Usage : Table Detection, Wooden Object Detection, Indoor Object Detecction, Table Plane Detection, Object Detection, Computer Vision, etc.

Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Dataset

This dataset was created by Shreyas Kulkarni

Released under Apache 2.0

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