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

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

About This Dataset

The Roboflow Website Screenshots dataset is a synthetically generated dataset composed of screenshots from over 1000 of the world's top websites. They have been automatically annotated to label the following classes: :fa-spacer: * button - navigation links, tabs, etc. * heading - text that was enclosed in <h1> to <h6> tags. * link - inline, textual <a> tags. * label - text labeling form fields. * text - all other text. * image - <img>, <svg>, or <video> tags, and icons. * iframe - ads and 3rd party content.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/mOG3u3Z.png" alt="WIkipedia Screenshot">

Usage

Annotated screenshots are very useful in Robotic Process Automation. But they can be expensive to label. This dataset would cost over $4000 for humans to label on popular labeling services. We hope this dataset provides a good starting point for your project. Try it with a model from our model library.

Collecting Custom Data

Roboflow is happy to provide a custom screenshots dataset to meet your particular needs. We can crawl public or internal web applications. Just reach out and we'll be happy to provide a quote!

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Overview

Mountain Dew is running a $1,000,000 counting contest. Computer Vision can help you win.

:fa-spacer:

Watch our video explaining how to use this dataset.

https://i.imgur.com/ED4jpM3.png" alt="Mountain Dew">

During Super Bowl LV, Mountain Dew sponsored an ad that encourages viewers to count all unique occurrences of Mountain Dew bottles. You can watch the full ad here. The first person to tweet the exactly correct count at Mountain Dew is eligible to win $1 million (see rules here).

Counting things is a perfect place for where computer vision can help.

We uploaded the Mountain Dew video to Roboflow, created three images per each second of the commercial (91 images from ~30 seconds of commercial), and annotated all bottles we could see. This dataset is the result.

We trained a model to recognize the Mountain Dew bottles, and then ran the original commercial back through this model. This helps identify Mountain Dew bottles that the human eye may have missed when completing counts.

https://i.imgur.com/rjZCS2a.png" alt="Image example">

Getting Started

Click "Fork" in the upper right hand corner or download the raw annotations in your desired format.

Note that while the images are property of PepsiCo, we are using them here as fair-use for educational purposes and have released the annotations under a Creative Commons license.

About Roboflow

Roboflow enables teams to use computer vision. :fa-spacer: Our end-to-end platform enables developers to collect, organize, annotate, train, deploy, and improve their computer vision models -- all without needing to hire a new ML engineering team. :fa-spacer:

This is a training split of the parent BCCD dataset.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

Overview

This is a dataset of Chess board photos and various pieces. All photos were captured from a constant angle, a tripod to the left of the board. The bounding boxes of all pieces are annotated as follows: white-king, white-queen, white-bishop, white-knight, white-rook, white-pawn, black-king, black-queen, black-bishop, black-knight, black-rook, black-pawn. There are 2894 labels across 292 images.

https://i.imgur.com/nkjobw1.png" alt="Chess Example">

Follow this tutorial to see an example of training an object detection model using this dataset or jump straight to the Colab notebook.

Use Cases

At Roboflow, we built a chess piece object detection model using this dataset.

https://blog.roboflow.ai/content/images/2020/01/chess-detection-longer.gif" alt="ChessBoss">

You can see a video demo of that here. (We did struggle with pieces that were occluded, i.e. the state of the board at the very beginning of a game has many pieces obscured - let us know how your results fare!)

Using this Dataset

We're releasing the data free on a public license.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility.

This is a test split of the parent BCCD dataset.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

Overview

https://i.imgur.com/9hvxcRQ.jpg" alt="Image example">

This is an object detection dataset of ocean fish classified by their latin names.

https://i.imgur.com/ECPln18.jpg" alt="Image example">

Use Cases

This dataset can be used for the following purposes:

• Underwater object detection model
• Fish object detection model
• Train object detection model to recognize underwater species
• Prototype fish detection system
• Identifying fish with computer vision
• Free fish dataset
• Free fish identificaiton dataset
• Scuba diving object detection dataset
• Fish bounding boxes
• Fish species annotations

Enjoy! These images have been listed in the public domain.

Note: These images have been sourced from makeml.app/datasets/fish

Overview

The License Plates dataset is a object detection dataset of different vehicles (i.e. cars, vans, etc.) and their respective license plate. Annotations also include examples of "vehicle" and "license-plate". This dataset has a train/validation/test split of 245/70/35 respectively. https://i.imgur.com/JmRgjBq.png" alt="Dataset Example">

Use Cases

This dataset could be used to create a vehicle and license plate detection object detection model. Roboflow provides a great guide on creating a license plate and vehicle object detection model.

Using this Dataset

This dataset is a subset of the Open Images Dataset. The annotations are licensed by Google LLC under CC BY 4.0 license. Some annotations have been combined or removed using Roboflow's annotation management tools to better align the annotations with the purpose of the dataset. The images have a CC BY 2.0 license.

About Roboflow

Roboflow creates tools that make computer vision easy to use for any developer, even if you're not a machine learning expert. You can use it to organize, label, inspect, convert, and export your image datasets. And even to train and deploy computer vision models with no code required. https://i.imgur.com/WHFqYSJ.png" alt="https://roboflow.com">

About this Dataset

This is a collection of images and video frames of cheetahs at the Omaha Henry Doorly Zoo taken in October, 2020. The capture device was a SEEK Thermal Compact XR connected to an iPhone 11 Pro. Video frames were sampled and labeled by hand with bounding boxes for object detection using Robofow.

Using this Dataset

We have provided the dataset for download under a creative commons by-attribution license. You may use this dataset in any project (including for commercial use) but must cite Roboflow as the source.

Example Use Cases

This dataset could be used for conservation of endangered species, cataloging animals with a trail camera, gathering statistics on wildlife behavior, or experimenting with other thermal and infrared imagery.

About Roboflow

Roboflow creates tools that make computer vision easy to use for any developer, even if you're not a machine learning expert. You can use it to organize, label, inspect, convert, and export your image datasets. And even to train and deploy computer vision models with no code required.

Pothole Dataset

https://i.imgur.com/7Xz8d5M.gif" alt="Example Image">

This is a collection of 665 images of roads with the potholes labeled. The dataset was created and shared by Atikur Rahman Chitholian as part of his undergraduate thesis and was originally shared on Kaggle.

Note: The original dataset did not contain a validation set; we have re-shuffled the images into a 70/20/10 train-valid-test split.

Usage

This dataset could be used for automatically finding and categorizing potholes in city streets so the worst ones can be fixed faster.

The dataset is provided in a wide variety of formats for various common machine learning models.

Public Test A

## Overview

Public Test A is a dataset for object detection tasks - it contains A annotations for 989 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).

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

Dataset Details

This dataset consists of 638 images collected by Roboflow from two aquariums in the United States: The Henry Doorly Zoo in Omaha (October 16, 2020) and the National Aquarium in Baltimore (November 14, 2020). The images were labeled for object detection by the Roboflow team (with some help from SageMaker Ground Truth). Images and annotations are released under a Creative Commons By-Attribution license. You are free to use them for any purposes personal, commercial, or academic provided you give acknowledgement of their source.

Projects Using this Dataset:

No-Code Object Detection Tutorial

Class Breakdown

The following classes are labeled: fish, jellyfish, penguins, sharks, puffins, stingrays, and starfish. Most images contain multiple bounding boxes.

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

Usage

The dataset is provided in many popular formats for easily training machine learning models. We have trained a model with CreateML (see gif above).

This dataset could be used for coral reef conservation, environmental health monitoring, swimmer safety, pet analytics, automated feeding, and much more. We're excited to see what you build!

About this dataset

This dataset contains 6,000 example images generated with the process described in Roboflow's How to Create a Synthetic Dataset tutorial.

The images are composed of a background (randomly selected from Google's Open Images dataset) and a number of fruits (from Horea94's Fruit Classification Dataset) superimposed on top with a random orientation, scale, and color transformation. All images are 416x550 to simulate a smartphone aspect ratio.

To generate your own images, follow our tutorial or download the code.

Example: https://blog.roboflow.ai/content/images/2020/04/synthetic-fruit-examples.jpg" alt="Example Image">

https://i.imgur.com/ztezlER.png" alt="Image example">

Overview

This dataset contains 627 images of various vehicle classes for object detection. These images are derived from the Open Images open source computer vision datasets.

This dataset only scratches the surface of the Open Images dataset for vehicles!

https://i.imgur.com/4ZHN8kk.png" alt="Image example">

Use Cases

• Train object detector to differentiate between a car, bus, motorcycle, ambulance, and truck.
• Checkpoint object detector for autonomous vehicle detector
• Test object detector on high density of ambulances in vehicles
• Train ambulance detector
• Explore the quality and range of Open Image dataset

Tools Used to Derive Dataset

https://i.imgur.com/1U0M573.png" alt="Image example">

These images were gathered via the OIDv4 Toolkit This toolkit allows you to pick an object class and retrieve a set number of images from that class with bound box lables.

We provide this dataset as an example of the ability to query the OID for a given subdomain. This dataset can easily be scaled up - please reach out to us if that interests you.

Overview

This dataset contains 8,992 images of Uno cards and 26,976 labeled examples on various textured backgrounds.

This dataset was collected, processed, and released by Roboflow user Adam Crawshaw, released with a modified MIT license: https://firstdonoharm.dev/

https://i.imgur.com/P8jIKjb.jpg" alt="Image example">

Use Cases

Adam used this dataset to create an auto-scoring Uno application:

Getting Started

Fork or download this dataset and follow our How to train state of the art object detector YOLOv4 for more.

Annotation Guide

See here for how to use the CVAT annotation tool.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

About This Dataset

The Roboflow Packages dataset is a collection of packages located at the doors of various apartments and homes. Packages are flat envelopes, small boxes, and large boxes. Some images contain multiple annotated packages.

Usage

This dataset may be used as a good starter dataset to track and identify when a package has been delivered to a home. Perhaps you want to know when a package arrives to claim it quickly or prevent package theft.

If you plan to use this dataset and adapt it to your own front door, it is recommended that you capture and add images from the context of your specific camera position. You can easily add images to this dataset via the web UI or via the Roboflow Upload API.

About Roboflow

Roboflow enables teams to build better computer vision models faster. We provide tools for image collection, organization, labeling, preprocessing, augmentation, training and deployment. :fa-spacer: Developers reduce boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Overview

This dataset contains 74 images of aerial maritime photographs taken with via a Mavic Air 2 drone and 1,151 bounding boxes, consisting of docks, boats, lifts, jetskis, and cars. This is a multi class problem. This is an aerial object detection dataset. This is a maritime object detection dataset.

The drone was flown at 400 ft. No drones were harmed in the making of this dataset.

This dataset was collected and annotated by the Roboflow team, released with MIT license.

https://i.imgur.com/9ZYLQSO.jpg" alt="Image example">

Use Cases

• Identify number of boats on the water over a lake via quadcopter.
• Boat object detection dataset
• Aerial Object Detection proof of concept
• Identify if boat lifts have been taken out via a drone
• Identify cars with a UAV drone
• Find which lakes are inhabited and to which degree.
• Identify if visitors are visiting the lake house via quad copter.
• Proof of concept for UAV imagery project
• Proof of concept for maritime project
• Etc.

This dataset is a great starter dataset for building an aerial object detection model with your drone.

Getting Started

Fork or download this dataset and follow our How to train state of the art object detector YOLOv4 for more. Stay tuned for particular tutorials on how to teach your UAV drone how to see and comprable airplane imagery and airplane footage.

Annotation Guide

See here for how to use the CVAT annotation tool that was used to create this dataset.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Overview

We have captured and annotated photos of six-sided dice. There are 359 total images from a few sets:

• 154 single dice of various styles on a white table
• 388 Catan Dice (Red and Yellow, some rolled on a white table, 160 on top of or near the Catan board)
• 13 mass groupings of dice in various styles

These images are released for you to use in training your machine learning models. :fa-spacer: https://i.imgur.com/ItN4AEk.png%20=250x" alt="Example Image"> :fa-spacer: Classes are generally balanced. Here's the output of Roboflow's Dataset Health check: https://i.imgur.com/ULrV1bZ.png%20=700x" alt="Class Balance">

Use Cases

This would be a great dataset to test out different object detection models like YOLO v3, MaskRCNN, mobilenet, or others.

You could use it to create dice game helper apps (like a dice counter) or independent games.

Using this Dataset

We're releasing the data as public domain. Feel free to use it for any purpose. It's not required to provide attribution, but it'd be nice! :fal-smile-wink:

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

https://www.robots.ox.ac.uk/%7Evgg/data/pets/pet_annotations.jpg" alt="Example Annotations">

About this Dataset

The Oxford Pets dataset (also known as the "dogs vs cats" dataset) is a collection of images and annotations labeling various breeds of dogs and cats. There are approximately 100 examples of each of the 37 breeds. This dataset contains the object detection portion of the original dataset with bounding boxes around the animals' heads.

Origin

This dataset was collected by the Visual Geometry Group (VGG) at the University of Oxford.

This dataset contains images of cottontail rabbits, that you might commonly find in your back yard in North America.

https://i.imgur.com/YiQI5Xn.png%5B/img%5D" alt="Cottontail Rabbits">

As we all know, rabbits can be quite a nuisance to our gardens and vegetables. That's why this dataset was used to train an object detection model that automatically recognizes rabbits, issuing a sound to deter them away.

Hardware:

• A Raspberry Pi
• External Power Supply - https://www.amazon.com/MakerHawk-Raspberry-Uninterruptible-Management-Expansion/dp/B082CVWH3R/ref=sr_1_6?crid=3LJGHA055O4VL&dchild=1&keywords=battery+for+raspberry+pi&qid=1623698007&sprefix=battery+for+raspbe%2Caps%2C184&sr=8-6
• Camera for the Pi - https://www.amazon.com/Arducam-Megapixels-Sensor-OV5647-Raspberry/dp/B012V1HEP4/ref=sr_1_6?dchild=1&keywords=Raspberry+Pi+camera&qid=1624689746&sr=8-6
• Bluetooth Speaker - https://www.amazon.com/AUDIOVOX-SP881BL-Portable-Bluetooth-Rechargeable/dp/B07F8N6KJ9/ref=sr_1_4?crid=2363N4JZD3B18&dchild=1&keywords=canz+bluetooth+speaker&qid=1626056945&sprefix=CANZ+bluetoot%2Caps%2C173&sr=8-4

Software:

• Public Dataset - https://public.roboflow.com/object-detection/eastern-cottontail-rabbits
• Roboflow Annotate - https://docs.roboflow.com/annotate
• Roboflow Train - https://docs.roboflow.com/train
• Roboflow Inference API - https://docs.roboflow.com/inference

Code repository here - https://github.com/roboflow-ai/rabbit-deterrence