ASL Alphabet

Image data set for alphabets in the American Sign Language

Content
The training data set contains 87,000 images which are 200x200 pixels. There are 29 classes, of which 26 are for the letters A-Z and 3 classes for SPACE, DELETE and NOTHING.
These 3 classes are very helpful in real-time applications, and classification.
The test data set contains a mere 29 images, to encourage the use of real-world test images.

enter image description here
https://www.nidcd.nih.gov/sites/default/files/Content%20Images/NIDCD-ASL-hands-2014.jpg

ASL Alphabet

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pothole, cracks and openmanholes (Road Hazards)

The dataset includes train and valid sets with annotations

This dataset contains 2,700 images focused on detecting potholes, cracks, and open manholes on roads. It has been augmented to enhance the variety and robustness of the data. The images are organized into training and validation sets, with three distinct categories:

Potholes: class 0
Cracks: class 1
Open Manholes: class 2

The dataset includes bounding box annotations in .txt files formatted for YOLOv8s, ensuring compatibility for model training. It is structured into separate folders for each class and contains train, valid, and all classes folders, allowing for easy access and custom augmentation. The dataset is designed for further model training, testing, and custom augmentation tasks related to road safety and infrastructure detection.
Usability

pothole, cracks and openmanholes (Road Hazards)

#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset

https://t.me/datasets1

Signature

Detects human signatures in legal and general documents

Dataset Structure
The signature detection dataset is split into three subsets:
Training set: Contains 143 images, each with corresponding annotations.
Validation set: Includes 35 images, each with paired annotations.

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Alzheimer MRI Disease Classification Dataset

Dataset focuses on the classification of Alzheimer's disease based on MRI scans.

Introduction

Alzheimer MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories:

'0': Mild_Demented

'1': Moderate_Demented

'2': Non_Demented

'3': Very_Mild_Demented
Dataset Information

Train split:

Name: train

Number of bytes: 22,560,791.2

Number of examples: 5,120

Test split:

Name: test

Number of bytes: 5,637,447.08

Number of examples: 1,280

Download size: 28,289,848 bytes

Dataset size: 28,198,238.28 bytes

A Large Scale Fish Dataset

A Large-Scale Dataset for Fish Segmentation and Classification

The dataset contains 9 different seafood types. For each class, there are 1000 augmented images and their pair-wise augmented ground truths.
Each class can be found in the "Fish_Dataset" file with their ground truth labels. All images for each class are ordered from "00000.png" to "01000.png".

For example, if you want to access the ground truth images of the shrimp in the dataset, the order should be followed is "Fish->Shrimp->Shrimp GT".

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Alzheimer's Disease Multiclass Images Dataset

Alzheimer's Disease dataset split into 4 classes

About Dataset

The Alzheimer's Disease Multiclass Dataset contains approximately 44,000 MRI images categorized into four distinct classes based on the severity of Alzheimer's disease. This dataset is intended for use in machine learning model training and testing. All images are skull-stripped and clean of non-brain tissue.

Dataset Structure
The dataset is organized into the following four directories, each representing a different class of disease severity:
NonDemented: Contains 12,800 MRI images of subjects with no signs of dementia.
VeryMildDemented: Contains 11,200 MRI images of subjects with very mild symptoms of dementia.
MildDemented: Contains 10,000 MRI images of subjects with mild dementia.
ModerateDemented: Contains 10,000 MRI images of subjects with moderate dementia.

Image Details
Total Number of Images: 44,000
Image Format: MRI scans as .JPG files
Image Usage: Suitable for training and testing machine learning models focused on classifying Alzheimer's disease stages.

Disease Severity Classification
The dataset follows a severity ranking system for Alzheimer's disease:
NonDemented: No dementia.
Very Mild Demented: Early signs of dementia, very mild symptoms.
Mild Demented: Clear signs of dementia, but still mild.
Moderate Demented: More pronounced symptoms of dementia, moderate severity.

Mammogram Mass Analyzer Desktop App

A free desktop breast cancer detection app that accepts dicom files.

Mammogram Mass Analyzer

This is a free desktop computer aided diagnosis (CAD) tool that uses computer vision to detect and localize masses on full field digital mammograms. It's a flask app that's running on the desktop. Internally there are two Yolov5L ensembled models that were trained on data from the VinDr-Mammo dataset. The model ensemble has a validation accuracy of 0.65 and a validation recall of 0.63.

My aim was to create a proof of concept for a free desktop computer aided diagnosis (CAD) system that could be used as an aid when diagnosing breast cancer. Unlike a web app, this tool does not need an internet connection and there are no monthly costs for hosting and web server rental. I think a desktop tool could be helpful to radiologists in private practice and to medical non-profits that work in remote areas.

The complete project folder, including the trained models, is stored in this Kaggle dataset.

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Tomato Leaf Disease Detection - YOLOv8 Dataset

Annotated Tomato Leaf Disease Dataset for YOLOv8 Model Training & Detection

About Dataset
Overview
This dataset is designed for Tomato Leaf Disease Detection using YOLOv8. It contains 10,853 labeled images spanning 10 different classes of tomato leaf conditions, including viral, bacterial, and fungal infections, as well as healthy leaves.
Dataset Details
Total Images: 10,853
Train Set: 7,842 images (72%)
Validation Set: 1,960 images (18%)
Test Set: 1,051 images (10%)
Image Resolution: Resized to 640x640 (stretched)
Annotation Format: YOLOv8
Classes (10 Categories)
Tomato Bacterial Spot
Tomato Early Blight
Tomato Late Blight
Tomato Leaf Mold
Tomato Septoria Leaf Spot
Tomato Spider Mites (Two-Spotted Spider Mite)
Tomato Target Spot
Tomato Yellow Leaf Curl Virus
Tomato Healthy
Tomato Mosaic Virus
Preprocessing Applied
Auto-orientation of pixel data (EXIF metadata stripped)
Images resized to 640x640 (stretched)
No augmentation applied