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.

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.

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:

Overview

The American Sign Language Letters dataset is an object detection dataset of each ASL letter with a bounding box. David Lee, a data scientist focused on accessibility, curated and released the dataset for public use.

Use Cases

One could build a model that reads letters in sign language. For example, Roboflow user David Lee wrote about how he made the model demonstrated above in this blog post

Using this Dataset

Use the fork button to copy this dataset to your own Roboflow account and export it with new preprocessing settings, or additional augmentations to make your model generalize better.

About Roboflow

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

Developers build computer vision models faster and more accurately with Roboflow.

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:

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:

About this dataset

The EgoHands dataset is a collection of 4800 annotated images of human hands from a first-person view originally collected and labeled by Sven Bambach, Stefan Lee, David Crandall, and Chen Yu of Indiana University.

The dataset was captured via frames extracted from video recorded through head-mounted cameras on a Google Glass headset while peforming four activities: building a puzzle, playing chess, playing Jenga, and playing cards. There are 100 labeled frames for each of 48 video clips.

Our modifications

The original EgoHands dataset was labeled with polygons for segmentation and released in a Matlab binary format. We converted it to an object detection dataset using a modified version of this script from @molyswu and have archived it in many popular formats for use with your computer vision models.

After converting to bounding boxes for object detection, we noticed that there were several dozen unlabeled hands. We added these by hand and improved several hundred of the other labels that did not fully encompass the hands (usually to include omitted fingertips, knuckles, or thumbs). In total, 344 images' annotations were edited manually.

We chose a new random train/test split of 80% training, 10% validation, and 10% testing. Notably, this is not the same split as in the original EgoHands paper.

There are two versions of the converted dataset available:

• specific is labeled with four classes: myleft, myright, yourleft, yourright representing which hand of which person (the viewer or the opponent across the table) is contained in the bounding box.
• generic contains the same boxes but with a single hand class.

Using this dataset

The authors have graciously allowed Roboflow to re-host this derivative dataset. It is released under a Creative Commons by Attribution 4.0 license. You may use it for academic or commercial purposes but must cite the original paper.

Please use the following Bibtext:

@inproceedings{egohands2015iccv,
                                title = {Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions},
                                author = {Sven Bambach and Stefan Lee and David Crandall and Chen Yu},
                                booktitle = {IEEE International Conference on Computer Vision (ICCV)},
                                year = {2015}
                            }
                            

This dataset is released by AI for Mankind in collaboration with HPWREN under a Creative Commons by Attribution Non-Commercial Share Alike license. The original dataset (and additional images without bounding boxes) can be found in their GitHub repo.

We have mirrored the dataset here for ease of download in a variety of common computer vision formats.

To learn more about this dataset and its possible applications in fighting wildfires, see this case study of Abhishek Ghosh's wildfire detection model.

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:

Dataset Information

This dataset contains 14,674 images (12,444 of which contain objects of interest with bounding box annotations) of fish, crabs, and other marine animals. It was collected with a camera mounted 9 meters below the surface on the Limfjords bridge in northern Denmark by Aalborg University.

Composition

Roboflow has extracted and processed the frames from the source videos and converted the annotations for use with many popular computer vision models. We have maintained the same 80/10/10 train/valid/test split as the original dataset.

The class balance in the annotations is as follows:

Most of the identified objects are congregated towards the bottom of the frames.

More Information

For more information, see the Detection of Marine Animals in a New Underwater Dataset with Varying Visibility paper.

If you find the dataset useful, the authors request that you please cite their paper:

@InProceedings{pedersen2019brackish,
                                title={Detection of Marine Animals in a New Underwater Dataset with Varying Visibility},
                                author={Pedersen, Malte and Haurum, Joakim Bruslund and Gade, Rikke and Moeslund, Thomas B. and Madsen, Niels},
                                booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
                                month = {June},
                                year = {2019}
                            }
                            

Overview

This dataset contains 196 images of raccoons and 213 bounding boxes (some images have two raccoons). This is a single class problem, and images vary in dimensions. It's a great first dataset for getting started with object detection.

This dataset was originally collected by Dat Tran, released with MIT license, and posted here with his permission.

Per Roboflow's Dataset Health Check, here's how images vary in size:

Use Cases

Find raccoons!

This dataset is a great starter dataset for building an object detection model. Dat has written a comprehensive tutorial here.

Getting Started

Fork or download this dataset and follow Dat's tutorial for more.

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.

About This Dataset

The Roboflow Thermal Dogs and People dataset is a collection of 203 thermal infrared images captured at various distances from people and dogs in a park and near a home. Some images are deliberately unannotated as they do not contain a person or dog (see the Dataset Health Check for more). Images were captured both portrait and landscape. (Roboflow auto-orient assures the annotations align regardless of the image orientation.)

Thermal images were captured using the Seek Compact XR Extra Range Thermal Imaging Camera for iPhone. The selected color palette is Spectra.

Example

This is an example image and annotation from the dataset:

Usage

Thermal images have a wide array of applications: monitoring machine performance, seeing in low light conditions, and adding another dimension to standard RGB scenarios. Infrared imaging is useful in security, wildlife detection,and hunting / outdoors recreation.

This dataset serves as a way to experiment with infrared images in Roboflow. (Or, you could build your own night time pet finder!)

Collecting Custom Data

Roboflow is happy to improve your operations with infrared imaging and computer vision. Services range from data collection to building automated monitoring systems leveraging computer vision. Reach out for more.

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

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.

Overview

We have captured and annotated photos of the popular board game, Boggle. Images are predominantly from 4x4 Boggle with about 30 images from Big Boggle (5x5).

• 357 images
• 7110 annotated letter cubes

These images are released for you to use in training your machine learning models.

Use Cases

We used this dataset to create BoardBoss, an augmented reality board game helper app. You can download BoardBoss in the App Store for free to see the end result! :fa-spacer: :fa-spacer: The model trained from this dataset was paired with some heuristics to recreate the board state and overlay it with an AR representation. We then used a traditional recursive backtracking algorithm to find and show the best words on the board.

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:

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.

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

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:

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.

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.

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.

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.

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.

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.

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.

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.

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!

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

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

Overview

The Mask Wearing dataset is an object detection dataset of individuals wearing various types of masks and those without masks. The images were originally collected by Cheng Hsun Teng from Eden Social Welfare Foundation, Taiwan and relabled by the Roboflow team.

Example image (some with masks, some without):

Use Cases

One could use this dataset to build a system for detecting if an individual is wearing a mask in a given photo.

Using this Dataset

Use the fork 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.

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.

Background

The Anki Vector robot (assets currently owned by Digital Dream Labs LLC which bought Anki assets in 2019) was first introduced in 2018. In my opinion, the Vector robot has been the cheapest fully functional autonomous robot that has ever been built. The Vector robot can be trained to recognize people; however Vector does not have the ability to recognize another Vector. This dataset has been designed to allow one to train a model which can detect a Vector robot in the camera feed of another Vector robot.

Details Pictures were taken with Vector’s camera with another Vector facing it and had this other Vector could move freely. This allowed pictures to be captured from different angles. These pictures were then labeled by marking the rectangular regions around Vector in all the images with the help of a free Linux utility called labelImg. Different backgrounds and lighting conditions were used to take the pictures. There is also a collection of pictures without Vector.

Example An example use case is available in my Google Colab notebook, a version of which can be found in my Git.

More More details are available in this article on my blog. If you are new to Computer Vision/ Deep Learning/ AI, you can consider my course on 'Learn AI with a Robot' which attempts to teach AI based on the AI4K12.org curriculum. There are more details available in this post.

This is the full 2017 COCO object detection dataset (train and valid), which is a subset of the most recent 2020 COCO object detection dataset.

COCO is a large-scale object detection, segmentation, and captioning dataset of many object types easily recognizable by a 4-year-old. The data is initially collected and published by Microsoft. The original source of the data is here and the paper introducing the COCO dataset is here.

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:

This Dataset contains images of popular North American mushrooms, Chicken of the Woods and Chanterelle, differentiating between the two species.

This dataset is an example of an object detection task that is possible via custom training with Roboflow.

Two versions are listed. "416x416" is a 416 resolution version that contains the base images in the dataset. "416x416augmented" contains the same images with various image augmentations applied to build a more robust model.

Overview

This dataset contains 581 images of various shellfish 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 shellfish!

Use Cases

• Train object detector to differentiate between a lobster, shrimp, and crab.
• Train object dector to differentiate between shellfish
• Object detection dataset across different sub-species
• Object detection among related species
• Test object detector on highly related objects
• Train shellfish detector
• Explore the quality and range of Open Image dataset

Tools Used to Derive Dataset

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.

Pothole Dataset

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.

Overview

The Flowers dataset is a classification detection dataset various flower species like dandelions and daisies.

Example Image:

Use Cases

Build a flower classifier model! Consider deploying that to a mobile app for outdoor enthusiasts or florist hobbyists.

Using this Dataset

Use the fork 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.

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.

Overview

The Open Poetry Vision dataset is a synthetic dataset created by Roboflow for OCR tasks.

It combines a random image from the Open Images Dataset with text primarily sampled from Gwern's GPT-2 Poetry project. Each image in the dataset contains between 1 and 5 strings in a variety of fonts and colors randomly positioned in the 512x512 canvas. The classes correspond to the font of the text.

Example Image:

Use Cases

A common OCR workflow is to use a neural network to isolate text for input into traditional optical character recognition software. This dataset could make a good starting point for an OCR project like business card parsing or automated paper form-processing.

Alternatively, you could try your hand using this as a neural font identification dataset. Nvidia, amongst others, have had success with this task.

Using this Dataset

Use the fork 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.

Version 5 of this dataset (classes_all_text-raw-images) has all classes remapped to be labeled as "text." This was accomplished by using Modify Classes as a preprocessing step.

Version 6 of this dataset (classes_all_text-augmented-FAST) has all classes remapped to be labeled as "text." and was trained with Roboflow's Fast Model.

Version 7 of this dataset (classes_all_text-augmented-ACCURATE) has all classes remapped to be labeled as "text." and was trained with Roboflow's Accurate Model.

• Introducing the New Roboflow Train
• What to Think About When Choosing Model Sizes

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

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:

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/

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:

The PKLot dataset contains 12,416 images of parking lots extracted from surveilance camera frames. There are images on sunny, cloudy, and rainy days and the parking spaces are labeled as occupied or empty. We have converted the original annotations to a variety of standard object detection formats by enclosing a bounding box around the original dataset's rotated rectangle annotations.

Using this Dataset

The PKLot database is licensed under a Creative Commons Attribution 4.0 License and may be used provided you acknowledge the source by citing the PKLot paper in publications about your research:

Almeida, P., Oliveira, L. S., Silva Jr, E., Britto Jr, A., Koerich, A., PKLot – A robust dataset for parking lot classification, Expert Systems with Applications, 42(11):4937-4949, 2015.
                            

Overview

This is a dataset of blood cells photos, originally open sourced by cosmicad and akshaylambda.

There are 364 images across three classes: WBC (white blood cells), RBC (red blood cells), and Platelets. There are 4888 labels across 3 classes (and 0 null examples).

Here's a class count from Roboflow's Dataset Health Check:

And here's an example image:

Fork this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 500x500 export.

Use Cases

This is a small scale object detection dataset, commonly used to assess model performance. It's a first example of medical imaging capabilities.

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! :)

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 dataset contains 2986 images and 3448 labels across a single annotation class: pistols. Images are wide-ranging: pistols in-hand, cartoons, and staged studio quality images of guns.

The dataset was originally released by the University of Grenada , duplicates removed, and rehosted by a Roboflow user.

Use Cases

One can create a gun object detection model to monitor security camera footage for the presence of guns, perhaps in places where they should not be. Alaa Senjab built on Roboflow to achieve this goal. He's also open sourced much of his work in this tutorial .

Overview

The PlantDoc dataset was originally published by researchers at the Indian Institute of Technology, and described in depth in their paper. One of the paper’s authors, Pratik Kayal, shared the object detection dataset available on GitHub.

PlantDoc is a dataset of 2,569 images across 13 plant species and 30 classes (diseased and healthy) for image classification and object detection. There are 8,851 labels. Read more about how the version available on Roboflow improves on the original version here.

And here's an example image:

Fork this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 416x416 export.

Use Cases

As the researchers from IIT stated in their paper, “plant diseases alone cost the global economy around US$220 billion annually.” Training models to recognize plant diseases earlier dramatically increases yield potential.

The dataset also serves as a useful open dataset for benchmarks. The researchers trained both object detection models like MobileNet and Faster-RCNN and image classification models like VGG16, InceptionV3, and InceptionResnet V2.

The dataset is useful for advancing general agriculture computer vision tasks, whether that be health crop classification, plant disease classification, or plant disease objection.

Using this Dataset

This dataset follows Creative Commons 4.0 protocol. You may use it commercially without Liability, Trademark use, Patent use, or Warranty.

Provide the following citation for the original authors:

@misc{singh2019plantdoc,
                                title={PlantDoc: A Dataset for Visual Plant Disease Detection},
                                author={Davinder Singh and Naman Jain and Pranjali Jain and Pratik Kayal and Sudhakar Kumawat and Nipun Batra},
                                year={2019},
                                eprint={1911.10317},
                                archivePrefix={arXiv},
                                primaryClass={cs.CV}
                            }
                            

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.

Overview

Via Laurence Maroney:

Rock Paper Scissors contains images from a variety of different hands, from different races, ages and genders, posed into Rock / Paper or Scissors and labelled as such. You can download the training set here, and the test set here. These images have all been generated using CGI techniques as an experiment in determining if a CGI-based dataset can be used for classification against real images. I also generated a few images that you can use for predictions. You can find them here.

Note that all of this data is posed against a white background.

Each image is 300×300 pixels in 24-bit color.

There are 2520 examples examples in the trianing set, 840 per class. The validation set contains 372 examples (124 per class). The test set contains 9 unlabeled images per class. (Note: in the source, Laurence calls "validation" as the "test," and "test" the "validation.")

Examples

Source

Overview

The original Udacity Self Driving Car Dataset is missing labels for thousands of pedestrians, bikers, cars, and traffic lights. This will result in poor model performance. When used in the context of self driving cars, this could even lead to human fatalities.

We re-labeled the dataset to correct errors and omissions. We have provided convenient downloads in many formats including VOC XML, COCO JSON, Tensorflow Object Detection TFRecords, and more.

Some examples of labels missing from the original dataset:

Stats

The dataset contains 97,942 labels across 11 classes and 15,000 images. There are 1,720 null examples (images with no labels).

All images are 1920x1200 (download size ~3.1 GB). We have also provided a version downsampled to 512x512 (download size ~580 MB) that is suitable for most common machine learning models (including YOLO v3, Mask R-CNN, SSD, and mobilenet).

Annotations have been hand-checked for accuracy by Roboflow.

Annotation Distribution:

Use Cases

Udacity is building an open source self driving car! You might also try using this dataset to do person-detection and tracking.

Using this Dataset

Our updates to the dataset are released under the MIT License (the same license as the original annotations and images).

Note: the dataset contains many duplicated bounding boxes for the same subject which we have not corrected. You will probably want to filter them by taking the IOU for classes that are 100% overlapping or it could affect your model performance (expecially in stoplight detection which seems to suffer from an especially severe case of duplicated bounding boxes).

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:

Pascal VOC 2012 is common benchmark for object detection. It contains common objects that one might find in images on the web.

Note: the test set is witheld, as is common with benchmark datasets.

You can think of it sort of like a baby COCO.

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: :fa-spacer: Classes are generally balanced. Here's the output of Roboflow's Dataset Health check:

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:

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!

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

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

The Flowers dataset is a classification detection dataset various flower species like dandelions and daisies.

Example Image:

Use Cases

Build a flower classifier model! Consider deploying that to a mobile app for outdoor enthusiasts or florist hobbyists.

Using this Dataset

Use the fork 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.

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.

Overview

The Fruits dataset is an image classification dataset of various fruits against white backgrounds from various angles, originally open sourced by GitHub user horea. This is a subset of that full dataset.

Example Image:

Use Cases

Build a fruit classifier! This could be a just-for-fun project just as much as you could be building a color sorter for agricultural use cases before fruits make their way to market.

Using this Dataset

Use the fork 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.

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.

Overview

The Drone Gesture Control Dataset is an object detection dataset that mimicks DJI's air gesture capability. This dataset consists of hand and body gesture commands that you can command your drone to either ,'take-off', 'land' and'follow'.

Example Image

Use Cases

One could build a model using MobileNet SSD using the Roboflow Platform deploy it to the OpenCV AI Kit. Watch the full tutorial here: https://augmentedstartups.info/AI-Drone-Tutorial

Using this Dataset

Use the fork button to copy this dataset to your own Roboflow account and export it with new preprocessing settings, or additional augmentations to make your model generalize better.

About Augmented Startups

We are at the forefront of Artificial Intelligence in computer vision. We embark on fun and innovative projects in this field and create videos and courses so that everyone can be an expert in this field. Our vision is to create a world full of inventors that can turn their dreams into reality.