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

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.

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

About Dataset
This dataset is part of a project aimed at collecting high-resolution satellite imagery of the Gaza Strip before and after the recent conflict. The images were retrieved using Sentinel Hub API and Planet.com API, covering weekly snapshots from January 2023 to the present.
With over 3,500 images, each accompanied by a metadata JSON file, this dataset enables researchers, analysts, and humanitarian organizations to study urban damage assessment, environmental changes, and infrastructure impact.
Key Features:
Weekly satellite images from multiple sources (Sentinel-2, Landsat-8, PlanetScope)
Configurable grid-based coverage of the entire Gaza Strip
Each image includes metadata (timestamps, coordinates, satellite source, etc.)
Supports before & after visualizations for damage assessment
Open-source processing pipeline available on GitHub

The "Person-Collecting-Waste COCO Dataset," provided by the user ashu009 on Kaggle, is designed for object detection tasks and follows the COCO format. This dataset includes 300 images along with corresponding annotation files in JSON format. The primary goal of this dataset is to identify and detect individuals collecting waste, which can be useful for projects related to environmental protection and waste management.

A total of 5335 images of healthy and affected eye images were collected from Anwara Hamida Eye Hospital in Faridpur and BNS Zahrul Haque Eye Hospital in Faridpur district with the help of the hospital authorities. Then from these original images, a total of 16242 augmented images are produced by using Rotation, Width shifting, Height shifting, Translation, Flipping, and Zooming techniques to increase the number of data.
Worldwide, eye ailments are recognized as significant contributors to nonfatal disabling conditions. In Bangladesh, 1.5% of adults suffer from blindness, while 21.6% experience low vision. Therefore, eye disease detection is crucial for preserving vision, preventing blindness, and maintaining overall health. Early detection allows for prompt intervention and treatment, preventing irreversible damage and preserving quality of life.

YOLO-based Segmented Dataset for Drone vs. Bird Detection for Deep and Machine Learning Algorithms


Formatted in accordance with the YOLOv7 PyTorch specification, the dataset is organized into three folders: Test, Train, and Valid. Each folder contains two sub-folders—Images and Labels—with the Labels folder including the associated metadata in plaintext format. This metadata provides valuable information about the detected objects within each image, allowing the model to accurately learn and detect drones and birds in varying circumstances. The dataset contains a total of 20,925 images, all with a resolution of 640 x 640 pixels in JPEG format, providing comprehensive training and validation opportunities for machine learning models.