Dataset

This dataset was created by Alin Cijov

Released under CC0: Public Domain

Contents

Give Machines the Power to See People.

This isn’t just a dataset — it’s a foundation for building the future of human-aware technology. Carefully crafted and annotated with precision, the People Detection dataset enables AI systems to recognize and understand human presence in dynamic, real-world environments.

Whether you’re building smart surveillance, autonomous vehicles, crowd analytics, or next-gen robotics, this dataset gives your model the eyes it needs.

What Makes This Dataset Different?

• Real-World Images – Diverse environments, realistic lighting, and real human motion
• High-Quality Annotations – Every person labeled with clean YOLO-format bounding boxes
• Plug-and-Play – Comes with pre-split training, validation, and test sets — no extra prep needed
• Speed-Optimized – Perfect for real-time object detection applications

Built for Visionaries

• Detect people instantly — in cities, offices, or crowds
• Build systems that respond to human presence
• Train intelligent agents to navigate human spaces safely and smartly

Created using Roboflow. Optimized for clarity, performance, and scale. Source Dataset on Roboflow →

This is more than a dataset. It’s a step toward a smarter world — One where machines can understand people.

Description
This synthetic dataset has been generated to facilitate object detection (in YOLO format) for research on dyslexia-related handwriting patterns. It builds upon an original corpus of uppercase and lowercase letters obtained from multiple sources: the NIST Special Database 19 111, the Kaggle dataset “A-Z Handwritten Alphabets in .csv format” 222, as well as handwriting samples from dyslexic primary school children of Seberang Jaya, Penang (Malaysia).

In the original dataset, uppercase letters originated from NIST Special Database 19, while lowercase letters came from the Kaggle dataset curated by S. Patel. Additional images (categorized as Normal, Reversal, and Corrected) were collected and labeled based on handwriting samples of dyslexic and non-dyslexic students, resulting in:

• 78,275 images labeled as Normal
• 52,196 images labeled as Reversal
• 8,029 images labeled as Corrected

Building upon this foundation, the Synthetic Dyslexia Handwriting Dataset presented here was programmatically generated to produce labeled examples suitable for training and validating object detection models. Each synthetic image arranges multiple letters of various classes (Normal, Reversal, Corrected) in a “text line” style on a black background, providing YOLO-compatible .txt annotations that specify bounding boxes for each letter.

Key Points of the Synthetic Generation Process

1. Letter-Level Source Data
   Individual characters were sampled from the original image sets.
2. Randomized Layout
   Letters are randomly assembled into words and lines, ensuring a wide variety of visual arrangements.
3. Bounding Box Labels
   Each character is assigned a bounding box with (x, y, width, height) in YOLO format.
4. Class Annotations
   Classes include 0 = Normal, 1 = Reversal, and 2 = Corrected.
5. Preservation of Visual Characteristics
   Letters retain their key dyslexia-relevant features (e.g., reversals).

Historical References & Credits

If you are using this synthetic dataset or the original Dyslexia Handwriting Dataset, please cite the following papers:

• M. S. A. B. Rosli, I. S. Isa, S. A. Ramlan, S. N. Sulaiman and M. I. F. Maruzuki, "Development of CNN Transfer Learning for Dyslexia Handwriting Recognition," 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2021, pp. 194–199, doi: 10.1109/ICCSCE52189.2021.9530971.
• N. S. L. Seman, I. S. Isa, S. A. Ramlan, W. Li-Chih and M. I. F. Maruzuki, "Notice of Removal: Classification of Handwriting Impairment Using CNN for Potential Dyslexia Symptom," 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2021, pp. 188–193, doi: 10.1109/ICCSCE52189.2021.9530989.
• Isa, Iza Sazanita. CNN Comparisons Models On Dyslexia Handwriting Classification / Iza Sazanita Isa … [et Al.]. Universiti Teknologi MARA Cawangan Pulau Pinang, 2021.
• Isa, I. S., Rahimi, W. N. S., Ramlan, S. A., & Sulaiman, S. N. (2019). Automated detection of dyslexia symptom based on handwriting image for primary school children. Procedia Computer Science, 163, 440–449.

References to Original Data Sources

111 P. J. Grother, “NIST Special Database 19,” NIST, 2016. [Online]. Available:
https://www.nist.gov/srd/nist-special-database-19

222 S. Patel, “A-Z Handwritten Alphabets in .csv format,” Kaggle, 2017. [Online]. Available:
https://www.kaggle.com/sachinpatel21/az-handwritten-alphabets-in-csv-format

Usage & Citation

Researchers and practitioners are encouraged to integrate this synthetic dataset into their computer vision pipelines for tasks such as dyslexia pattern analysis, character recognition, and educational technology development. Please cite the original authors and publications if you utilize this synthetic dataset in your work.

Password Note (Original Data)

The original RAR file was password-protected with the password: WanAsy321. This synthetic dataset, however, is provided openly for streamlined usage.

White Blood Cell (WBC) Detection in Microscopic Blood Cell Images

Overview

This dataset consists of microscopic images of blood cells specifically designed for the detection of White Blood Cells (WBC). It is intended for object detection tasks where the goal is to accurately locate and identify WBCs within blood smear images. Researchers and developers can utilize this data to train machine learning models for medical applications such as automated blood cell analysis.

Dataset Content

Images: The dataset contains high-resolution microscopic images of blood smears, where WBCs are scattered among Red Blood Cells (RBCs) and platelets. Each image is annotated with bounding boxes around the WBCs.

Annotations: The annotations are provided in YOLO format, where each bounding box is associated with a label for WBC.

File Structure:

images/: Contains the blood cell images in .jpg or .png format. labels/: Contains the annotation files in .txt format (YOLO format), with each file corresponding to an image. Image Size: Varies, but all images are in high resolution suitable for detection tasks.

Applications

Medical Image Analysis: This dataset can be used to build models for the automated detection of WBCs, which is a crucial step in diagnosing various blood-related disorders. Object Detection: Ideal for testing object detection algorithms like YOLO, Faster R-CNN, or SSD. Acknowledgments This dataset is created using publicly available microscopic blood cell images, annotated for educational and research purposes. It can be used for developing machine learning models for academic research, prototyping medical applications, or object detection benchmarking.

Most street object datasets require your model to do object detection and extract multiple objects from a single image. Which is fine if you're working with complex models like YOLO or R-CNN. This dataset however is for image classification training that could be done with any simple CNN model or even traditional ML models with enough processing and feature extraction.

A convenient csv file with image paths and encoded labels is provided for use in image data generators.

Overview

Dataset contains 9879 images with varying sizes categorized into 7 classes.

• 0: bicycle
• 1: car
• 2: limit30
• 3: person
• 4: stop
• 5: trafficlight
• 6: truck

Sources

This dataset was obtained by performing some processing on the following dataset:

https://www.kaggle.com/datasets/ahmedyoussefff/street-objects-dataset/

https://universe.roboflow.com/project-mzmwg/street-objects-ag7dt

The preprocessing consisted of cropping each object specified by the YOLO format into its own separate image. Preprocessing code is available here: https://www.kaggle.com/code/owm4096/street-objects-classification-dataset-extraction

Mendeley Link : https://data.mendeley.com/datasets/pwyyg8zmk5/2

Poribohon-BD is a vehicle dataset of 15 native vehicles of Bangladesh. The vehicles are: i) Bicycle, ii) Boat, iii) Bus, iv) Car, v) CNG, vi) Easy-bike, vii) Horse-cart, viii) Launch, ix) Leguna, x) Motorbike, xi) Rickshaw, xii) Tractor, xiii) Truck, xiv) Van, xv) Wheelbarrow. The dataset contains a total of 9058 images with a high diversity of poses, angles, lighting conditions, weather conditions, backgrounds. All of the images are in JPG format. The dataset also contains 9058 image annotation files. These files state the exact positions of the objects with labels in the corresponding image. The annotation has been performed manually and the annotated values are stored in XML files. LabelImg tool by Tzuta Lin has been used to label the images. Moreover, data augmentation techniques have been applied to keep the number of images comparable to each type of vehicle. Human faces have also been blurred to maintain privacy and confidentiality. The data files are divided into 15 individual folders. Each folder contains images and annotation files of one vehicle type. The 16th folder titled ‘Multi-class Vehicles’ contains images and annotation files of different types of vehicles. Poribohon-BD is compatible with various CNN architectures such as YOLO, VGG-16, R-CNN, DPM.

📰 Related Papers

1. Sichkar V. N. Real time detection and classification of traffic signs based on YOLO version 3 algorithm. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 3, pp. 418–424. DOI: 10.17586/2226-1494-2020-20-3-418-424 (Full-text available on ResearchGate here: Real time detection and classification of traffic signs based on YOLO version 3 algorithm

2. Sichkar V. N. Effect of various dimension convolutional layer filters on traffic sign classification accuracy. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 3, pp. 546–552. DOI: 10.17586/2226-1494-2019-19-3-546-552 (Full-text available on ResearchGate here: Effect of various dimension convolutional layer filters on traffic sign classification accuracy

:mortar_board: Related course for classification tasks

Design, Train & Test deep CNN for Image Classification. Join the course & enjoy new opportunities to get deep learning skills: https://www.udemy.com/course/convolutional-neural-networks-for-image-classification/

https://github.com/sichkar-valentyn/1-million-images-for-Traffic-Signs-Classification-tasks/blob/main/images/slideshow_classification.gif?raw=true%20=470x516" alt="CNN Course" title="CNN Course">

🗺️ Concept Map of the Course

https://github.com/sichkar-valentyn/1-million-images-for-Traffic-Signs-Classification-tasks/blob/main/images/concept_map.png?raw=true%20=570x410" alt="Concept map" title="Concept map">

👉 Join the Course

https://www.udemy.com/course/convolutional-neural-networks-for-image-classification/

Related Dataset for Detection Tasks

Explore one more dataset used for detection tasks here: https://www.kaggle.com/valentynsichkar/traffic-signs-dataset-in-yolo-format

About this Dataset for Classification Tasks

This is ready to use preprocessed data for Traffic Signs saved into the nine pickle files.
Original datasets are in the following files:
- train.pickle
- valid.pickle
- test.pickle

Code with detailed description on how datasets were preprocessed is in datasets_preparing.py

Before preprocessing training dataset was equalized making examples in the classes equal as it is shown on the figure below. Histogram of 43 classes for training dataset with their number of examples for Traffic Signs Classification before and after equalization by adding transformated images (brightness and rotation) from original dataset. After equalization, training dataset has increased up to 86989 examples.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3400968%2Fb5d9f0189353832e769c2bdd8e25243d%2Fhistogram.png?generation=1567275066871451&alt=media" alt="">

Resulted preprocessed nine files are as follows:
- data0.pickle - Shuffling
- data1.pickle - Shuffling, /255.0 Normalization
- data2.pickle - Shuffling, /255.0 + Mean Normalization
- data3.pickle - Shuffling, /255.0 + Mean + STD Normalization
- data4.pickle - Grayscale, Shuffling
- data5.pickle - Grayscale, Shuffling, Local Histogram Equalization
- data6.pickle - Grayscale, Shuffling, Local Histogram Equalization, /255.0 Normalization
- data7.pickle - Grayscale, Shuffling, Local Histogram Equalization, /255.0 + Mean Normalization
- data8.pickle - Grayscale, Shuffling, Local Histogram Equalization, /255.0 + Mean + STD Normalization

Datasets data0 - data3 have RGB images and datasets data4 - data8 have Gray images.

Shapes of data0 - data3 are as following (RGB):
- x_train: (86989, 3, 32, 32)
- y_train: (86989,)
- x_validation: (4410, 3, 32, 32)
- y_validation: (4410,)
- x_test: (12630, 3, 32, 32)
- y_test: (12630,)

Shapes of data4 - data8 are as following (Gray):
- x_train: (86989, 1, 32, 32)
- y_train: (86989,)
- x_validation: (4410, 1, 32, 32)
- y_validation: (4410,)
- x_test: (12630, 1, 32, 32)
- y_test: (12630,)

mean image and standard deviation were calculated from train dataset and applied to validation and testing datasets for appropriate datasets. When using user's image for classification, it has to be preprocessed fi...

https://i.imgur.com/eVRmfw9.gif" alt="Rock Paper Scissors">

About the Dataset

Dive into the Rock-Paper-Scissors SXSW collection—a vibrant, crowd-sourced set of 7,521 hand-pose images, fine-tuned and battle-ready for your next computer-vision masterpiece! Exported from Roboflow on March 15, 2024, this dataset is packaged in TensorFlow Object Detection format so you can hit the ground running with training and experimentation.

~ Why You’ll Love It

Massive scale: 7,521 uniquely captured poses, each stretched to 640 × 640 px for consistency. Built-in robustness: Every image is tripled with on-the-fly augmentations—flips, crops, brightness tweaks, and exposure shifts—so your model sees every angle, every time. Three clear classes: rock, paper, and scissors—perfect for straightforward multiclass detection.

~ How It Was Made Born from the World’s Largest Game of Rock, Paper, Scissors at SXSW 2023, this collection blends:

1. Open-source captures from Roboflow’s public repos
2. Fresh, hand-labeled shots by the Roboflow team and friends
3. Automated pre-processing (EXIF-safe auto-orientation + resizing)

~ Plug-and-Play Uses

Rapid prototyping: Train YOLO, SSD, Faster R-CNN (or your favorite detector) in minutes. Edge deployment: Build real-time hand-gesture games for mobile or IoT devices. Research & transfer learning: Use as a springboard for sign-language or fine-grained gesture tasks. Active learning loops: Easily append new samples or tweak augmentations to push performance further.

~ License & Citation

License: CC0 1.0 Public Domain—use, remix, and share without barriers.

Get ready to rock your next computer-vision project—no paper cuts included!