Object Detection On Train Track

## Overview

Object Detection On Train Track 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).

Object detection is a vital part of any autonomous vision system and to obtain a high performing object detector data is needed. The object detection task aims to detect and classify different objects using camera input and getting bounding boxes containing the objects as output. This is usually done by utilizing deep neural networks.

When training an object detector a large amount of data is used, however it is not always practical to collect large amounts of data. This has led to multiple different techniques which decreases the amount of data needed. Examples of such techniques are transfer learning and domain adaptation. Working with construction equipment is a time consuming process and we wanted to examine if it was possible to use scale-model data to train a network and then used that network to detect real objects with no additional training.

This small dataset contains training and validation data of a scale dump truck in different environments while the test set contains images of a full size dump truck of similar model. The aim of the dataset is to train a network to classify wheels, cabs and tipping bodies of a scale-model dump truck and use that to classify the same classes on a full-scale dump truck.

The label structure of the dataset is the YOLO v3 structure, where the classes corresponds to a integer value, such that: Wheel: 0 Cab: 1 Tipping body: 2

The "26 Class Object Detection Dataset" comprises a comprehensive collection of images annotated with objects belonging to 26 distinct classes. Each class represents a common urban or outdoor element encountered in various scenarios. The dataset includes the following classes:

Bench Bicycle Branch Bus Bushes Car Crosswalk Door Elevator Fire Hydrant Green Light Gun Motorcycle Person Pothole Rat Red Light Scooter Stairs Stop Sign Traffic Cone Train Tree Truck Umbrella Yellow Light These classes encompass a wide range of objects commonly encountered in urban and outdoor environments, including transportation vehicles, traffic signs, pedestrian-related elements, and natural features. The dataset serves as a valuable resource for training and evaluating object detection models, particularly those focused on urban scene understanding and safety applications.

Round 13 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained both on synthetic image data build from Cityscapes and the DOTA_v2 dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 128 AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Object Detection In Images TRAIN

## Overview

Object Detection In Images TRAIN is a dataset for object detection tasks - it contains Visdrone_objects annotations for 1,304 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).

Dataset Description: Car Object Detection in Road Traffic

Overview:

This dataset is designed for car object detection in road traffic scenes (Images with shape 1080x1920x3). The dataset is derived from publicly available video content on YouTube, specifically from the video with the Creative Commons Attribution license, available here. https://youtu.be/MNn9qKG2UFI?si=uJz_WicTCl8zfrVl" alt="youtube video">

Source:

• Video Source: YouTube Video.
• License: Creative Commons Attribution (reuse allowed) more details here.
• Dataset Contents: The dataset consists of a collection of image frames extracted from the video. Each image frame captures various scenes from road traffic. Car objects within these frames are annotated with bounding boxes.

Annotation Details:

• Bounding Boxes: Each image frame contains annotated bounding boxes around car objects, marking their locations in the scene.
• Classes: The dataset is focused on car object detection, and car objects are labeled as the target class (aka one class only).
• Data Format: Images are provided in JPEG format.
• Annotation files are provided in YOLO text format.
• We used labelImg GUI to label this dataset in YOLO format, more details are in this GitHub repo.

Use Cases:

• Object Detection: This dataset can be used to train and evaluate object detection models, with an emphasis on detecting cars in road traffic scenarios.

Acknowledgments: We acknowledge and thank the creator of the original video for making it available under a Creative Commons Attribution license. Their contribution enables the development of datasets and research in the field of computer vision and object detection.

Disclaimer: This dataset is provided for educational and research purposes and should be used in compliance with YouTube's terms of service and the Creative Commons Attribution license.

Overview

The Hard Hat dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.

The original dataset has a 75/25 train-test split.

Example Image: https://i.imgur.com/7spoIJT.png" alt="Example Image">

Use Cases

One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.

Using this Dataset

Use the fork or Download this Dataset button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

Dataset Versions:

Image Preprocessing | Image Augmentation | Modify Classes * v1 (resize-416x416-reflect): generated with the original 75/25 train-test split | No augmentations * v2 (raw_75-25_trainTestSplit): generated with the original 75/25 train-test split | These are the raw, original images * v3 (v3): generated with the original 75/25 train-test split | Modify Classes used to drop person class | Preprocessing and Augmentation applied * v5 (raw_HeadHelmetClasses): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class * v8 (raw_HelmetClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and person classes * v9 (raw_PersonClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop head and helmet classes * v10 (raw_AllClasses): generated with a 70/20/10 train/valid/test split | These are the raw, original images * v11 (augmented3x-AllClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied | 3x image generation | Trained with Roboflow's Fast Model * v12 (augmented3x-HeadHelmetClasses-FastModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Fast Model * v13 (augmented3x-HeadHelmetClasses-AccurateModel): generated with a 70/20/10 train/valid/test split | Preprocessing and Augmentation applied, Modify Classes used to drop person class | 3x image generation | Trained with Roboflow's Accurate Model * v14 (raw_HeadClassOnly): generated with a 70/20/10 train/valid/test split | Modify Classes used to drop person class, and remap/relabel helmet class to head

Choosing Between Computer Vision Model Sizes | Roboflow Train

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Food Object Detection Train

## Overview

Food Object Detection Train is a dataset for object detection tasks - it contains Food annotations for 797 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 features over 5,500,000 high-quality images of animals sourced from photographers around the globe. Created to support AI and machine learning applications, it offers a richly diverse and precisely annotated collection of wildlife, domestic, and exotic animal imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with species information, behavior tags, and scene metadata, making it ideal for image classification, detection, and animal behavior modeling. Popularity metrics based on platform engagement are also included.

1. Unique Sourcing Capabilities: the images are gathered through a proprietary gamified platform that hosts competitions on animal photography. This approach ensures a stream of fresh, high-quality content. On-demand custom datasets can be delivered within 72 hours for specific species, habitats, or behavioral contexts.

2. Global Diversity: photographers from over 100 countries contribute to the dataset, capturing animals in a variety of ecosystems—forests, savannas, oceans, mountains, farms, and homes. It includes pets, wildlife, livestock, birds, marine life, and insects across a wide spectrum of climates and regions.

3. High-Quality Imagery: the dataset spans from standard to ultra-high-resolution images, suitable for close-up analysis of physical features or environmental interactions. A balance of candid, professional, and artistic photography styles ensures training value for real-world and creative AI tasks.

4. Popularity Scores: each image carries a popularity score from its performance in GuruShots competitions. This can be used to train AI models on visual appeal, species preference, or public interest trends.

5. AI-Ready Design: optimized for use in training models in species classification, object detection, wildlife monitoring, animal facial recognition, and habitat analysis. It integrates seamlessly with major ML frameworks and annotation tools.

6. Licensing & Compliance: all data complies with global data and wildlife imagery licensing regulations. Licenses are clear and flexible for commercial, nonprofit, and academic use.

Use Cases: 1. Training AI for wildlife identification and biodiversity monitoring. 2. Powering pet recognition, breed classification, and animal health AI tools. 3. Supporting AR/VR education tools and natural history simulations. 4. Enhancing environmental conservation and ecological research models.

This dataset offers a rich, high-quality resource for training AI and ML systems in zoology, conservation, agriculture, and consumer tech. Custom dataset requests are welcomed. Contact us to learn more!

We purchased two packs of candy from a local store—a Nestlé “party pack” containing 60 “fun size” pieces of four candy types (Butterfinger, Crunch, Baby Ruth, and 100 Grand) and a Mars pack containing 125 “minis party size” pieces of five candy types (Snickers, Twix, Musketeers, Milky Way, and Milky Way Midnight). Given the nine candy types, there are a total of 126 unique combinations of four candy types. For each combination, we took eight photos, each containing four candy pieces, one for each type. The candy pieces were placed on a grey rug to improve the identification of candy pieces. Before taking each photo, we swapped candy pieces and rearranged their positions and sides. A Canon PowerShot SX540 digital camera was used to take photos. All images in the dataset were annotated using VGG Image Annotator (VIA). In an image, a rectangular bounding box was manually drawn around each candy piece with its type labeled.

A comprehensive dataset of 340K+ jewelry images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

Round 10 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained on the COCO dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Multi Instance Object Detection Dataset Sample

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model for 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.

  Dataset Overview

This dataset consists of high-quality images of soup… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Multi-Instance-Object-Detection-Dataset.

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

A comprehensive dataset of 1M+ Footwear images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

A comprehensive dataset of 1M+ car images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

Trains Detection 3

## Overview

Trains Detection 3 is a dataset for object detection tasks - it contains Trains annotations for 551 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).

Context

This data set has been used in object detection experiments with Google's Tensorflow object detection models transfer learning.

Content

Photo images containing Obama. The goal is to train object detector to find Obama in a photo.

A comprehensive dataset of 600K+ household object images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

Soup Can Object Detection Dataset Sample

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model for 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.

  Dataset Overview

This dataset consists of high-quality images of soup cans… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Object-Detection-02-Dataset.