Kaggle Road Sign Detection

## Overview

Kaggle Road Sign Detection is a dataset for object detection tasks - it contains Road Signs annotations for 875 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Road Sign Detection Kaggle

## Overview

Road Sign Detection Kaggle is a dataset for object detection tasks - it contains Trafic Light Stop Speedlimit Crosswalk annotations for 875 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 hosts the point-clouds and the respective labels from the Google - Isolated Sign Language Recognition competition in TFRecord format.

Dataset

This dataset was created by Ardy Ansyah

Contents

Kaggle Road Sign Dataset

## Overview

Kaggle Road Sign Dataset is a dataset for object detection tasks - it contains Traffic Sign annotations for 823 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 consists of the Indian sign language of all the alphabets and numbers in Indian hand recognition given by ISRTC(Indian Sign Research and Training Center). This dataset is in black white background for faster computing and for getting better accuracy while training the dataset.

Please give credit to this dataset if you download it.

Context

This dataset was created to train a neural network for real time sign detection, which would be used as automated feedback for a learning application. The dataset ist based on the normalized hand landmark vectors provided by mediapipe's handpose in order to make the trained NN invariant to lighting situations or skin colors, which could not be represented in a diverse enough fashion in the dataset.

The dataset is therefore designed to train a NN which categorizes the MULTI_HAND_LANDMARK output of the handpose solution.

Content

The dataset contains 64 columns with the first column being the sample's label. All static signs (meaning signs not involving movement) of the German Sign language alphabet are represented as 24 classes ('a'-'y', excluding 'j').

All other columns represent the 21 linearized, three-dimensional hand landmarks provided by handpose in their normalized ([0.0, 1.0]) state.

In total the dataset contains ca. 7300 samples with at least 250 samples per class, recorded by 7 different non-native signers.

The dataset is purely made up of recorded samples and does not make use of data augmentation.

Acknowledgements

This dataset was inspired by the desire to create a German version of the Sign Language MNIST dataset with a stronger focus on practical applicability.

Inspiration

Our team is interested in providing a foundation for all kinds of practical applications involving sign language recognition. As with our own work, we appreciate a focus on applications challenging non-signers to engage with sign language in a way that promotes inclusion.

Ethical considerations

We are aware of the ethical implications of such a dataset and encourage developers to seriously consider research on the ethics of machine learning and sign language to avoid harmful outcomes of well intended projects. For more information on this topic we recommend Bragg, D., Caselli, N., Hochgesang, J. A., Huenerfauth, M., Katz-Hernandez, L., Koller, O., Kushalnagar, R., Vogler, C., & Ladner, R. E. (2021). The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective. In ACM Transactions on Accessible Computing (14th ed., Vol. 2, pp. 1-45). Association for Computing Machinery. 10.1145/3436996 as a starting point.

Dataset

This dataset was created by Mohammad_Abdullah_407

Contents

Australia Traffic Sign

## Overview

Australia Traffic Sign is a dataset for object detection tasks - it contains Traffic Sign annotations for 4,201 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 [MIT license](https://creativecommons.org/licenses/MIT).

Dataset

This dataset was created by Narasimha Pujith

Contents

180,717 Images - Sign Language Gestures Recognition Data. The data diversity includes multiple scenes, 41 static gestures, 95 dynamic gestures, multiple photographic angles, and multiple light conditions. In terms of data annotation, 21 landmarks, gesture types, and gesture attributes were annotated. This dataset can be used for tasks such as gesture recognition and sign language translation.

This dataset contains images representing the American Sign Language (ASL) alphabet from A to Z. Each alphabet class includes 200 grayscale hand gesture images, totaling 5,200 images across the entire dataset.

Each image is annotated with 21 hand landmark keypoints, enabling efficient use in computer vision, hand pose estimation, sign language recognition, and gesture classification tasks.

This dataset is suitable for:

Deep learning models for ASL recognition

Real-time gesture recognition projects

Educational tools and accessibility technologies

✅ Classes: 26 (A-Z) ✅ Images per class: 200 ✅ Total images: 5,200

Example use cases include training a CNN or integrating with hand-tracking systems like MediaPipe or OpenCV.

Context

Existing datasets mostly include European and American signs while in many projects like mine, we need Persian signs therefore we gathered and prepared “Persian Traffic Sign Dataset” which is called as PTSD. This process took near a year to take images from different places and cities in Iran and finally cropping and classifying all of them. This dataset is appropriate for recognition task.

For my future project I am preparing Traffic Signs images for detection task with annotation files. This huge data set will be published as soon.

Content

This dataset consists of more than 14 thousand cropped traffic signs images in 43 classes for recognition task. In addition to training data, we prepared 2421 images for testing phase .

Acknowledgements

I wouldn't be here without the help of others specially my classmates in Master Course in Tabriz University and my Supervisor Dr.Ghader Karimian Khosroshahi. Also I appreciate my lovely family.

Zdroje dat: https://www.kaggle.com/datasets/andrewmvd/road-sign-detection?resource=download https://pixabay.com https://unsplash.com https://www.pexels.com

Analysis of ‘Gender Recognition by Voice’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/primaryobjects/voicegender on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Voice Gender

Gender Recognition by Voice and Speech Analysis

This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).

The Dataset

The following acoustic properties of each voice are measured and included within the CSV:

• meanfreq: mean frequency (in kHz)
• sd: standard deviation of frequency
• median: median frequency (in kHz)
• Q25: first quantile (in kHz)
• Q75: third quantile (in kHz)
• IQR: interquantile range (in kHz)
• skew: skewness (see note in specprop description)
• kurt: kurtosis (see note in specprop description)
• sp.ent: spectral entropy
• sfm: spectral flatness
• mode: mode frequency
• centroid: frequency centroid (see specprop)
• peakf: peak frequency (frequency with highest energy)
• meanfun: average of fundamental frequency measured across acoustic signal
• minfun: minimum fundamental frequency measured across acoustic signal
• maxfun: maximum fundamental frequency measured across acoustic signal
• meandom: average of dominant frequency measured across acoustic signal
• mindom: minimum of dominant frequency measured across acoustic signal
• maxdom: maximum of dominant frequency measured across acoustic signal
• dfrange: range of dominant frequency measured across acoustic signal
• modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range
• label: male or female

Accuracy

Baseline (always predict male)

50% / 50%

Logistic Regression

97% / 98%

CART

96% / 97%

Random Forest

100% / 98%

SVM

100% / 99%

XGBoost

100% / 99%

Research Questions

An original analysis of the data-set can be found in the following article:

Identifying the Gender of a Voice using Machine Learning

The best model achieves 99% accuracy on the test set. According to a CART model, it appears that looking at the mean fundamental frequency might be enough to accurately classify a voice. However, some male voices use a higher frequency, even though their resonance differs from female voices, and may be incorrectly classified as female. To the human ear, there is apparently more than simple frequency, that determines a voice's gender.

Questions

• What other features differ between male and female voices?
• Can we find a difference in resonance between male and female voices?
• Can we identify falsetto from regular voices? (separate data-set likely needed for this)
• Are there other interesting features in the data?

CART Diagram

http://i.imgur.com/Npr2U7O.png" alt="CART model">

Mean fundamental frequency appears to be an indicator of voice gender, with a threshold of 140hz separating male from female classifications.

References

The Harvard-Haskins Database of Regularly-Timed Speech

Telecommunications & Signal Processing Laboratory (TSP) Speech Database at McGill University, Home

VoxForge Speech Corpus, Home

Festvox CMU_ARCTIC Speech Database at Carnegie Mellon University

--- Original source retains full ownership of the source dataset ---

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 2000+ original Indian Traffic Sign images captured and crowdsourced from over 400+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at DC Labs.

Optimized for Generative AI, Visual Question Answering, Image Classification, and LMM development, this dataset provides a strong basis for achieving robust model performance.

Dataset Features

• Dataset size : 2000+
• Captured by : Over 400+ crowdsource contributors
• Resolution : 100% of images HD and above (1920x1080 and above)
• Location : Captured with 400+ cities accross India
• Diversity : Various lighting conditions like day, night, varied distances, view points etc.
• Device used : Captured using mobile phones in 2020-2021
• Usage : Traffic sign detection, Self-driving systems, traffic detection, sign detection, 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 sahilsgi

Released under MIT

Contents

:triangular_flag_on_post: Details

The dataset includes image files and appropriate annotations to train YOLO v5 detector. It is separated into two versions: 1. with 4 classes only 1. and with all 43 classes

Before training, edit dataset.yaml file and specify there appropriate path 👇

# The root directory of the dataset
# (!) Update the root path according to your location
path: ..\..\Downloads\ts_yolo_v5_format\ts4classes

train: images\train\   # train images (relative to 'path')
val: images\validation\  # val images (relative to 'path')
test: images\test\    # test images (relative to 'path')

# Number of classes and their names
nc: 4
names: [ 'prohibitory', 'danger', 'mandatory', 'other']

🎥 Watch video about YOLO format 👇

https://www.youtube.com/watch?v=-bU0ZBbG8l4" alt="">

🎓 YOLO v5: Label, Train and Test. Join the course! 👇

https://www.udemy.com/course/yolo-v5-label-train-and-test

Have a look at the abilities that you will obtain:
📢 Run YOLO v5 to detect objects on image, video and in real time by camera in the first lectures.
📢 Label-Create-Convert own dataset in YOLO format.
📢 Train & Test both: in your local machine and in the cloud machine (with custom data and by few lines of the code).

Concept map of the YOLO v5 course 👇

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3400968%2Fac1893f68be61efb21e376b3c405147c%2Fconcept_map_YOLO_v5.png?generation=1701165575909796&alt=media" alt="Concept map of the YOLO v5 course">

Join the course! 👇

https://www.udemy.com/course/yolo-v5-label-train-and-test

Acknowledgements

Initial data is The German Traffic Sign Recognition Benchmarks (GTSRB).

Dataset

This dataset was created by 23-hooon

Released under Apache 2.0

Contents

• Sign language datasets for hand gesture recognition for numbers.
• Following gestures can be detected using this datasets https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3501914%2F7b0dad7ab22042627bb4a82b1c217d5d%2FNumbers.JPG?generation=1573758431577149&alt=media" alt="">