The “Lions or Cheetahs – Image Classification” dataset is a collection of images downloaded from the Open Images Dataset V6, containing photographs of both lions and cheetahs.

This dataset contains weapon detection annotation in pascal VOC format. Each image has its corresponding annotation xml file.

The purpose behind the creation of this dataset is the development of a machine learning model to classify fruits and vegetables as fresh or stale.

Pneumonia is an infection that inflames the air sacs in one or both lungs. It kills more children younger than 5 years old each year than any other infectious disease.

A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color.

The TuSimple dataset consists of 6,408 road images on US highways. The resolution of the image is 1280×720. The dataset is composed of 3,626 for training.

To create a dataset for YOLO-based object detection, we compile 1500 images across four classes: buffalo, elephant, rhino, and zebra, preprocessed for optimal model training.

The dataset “Development of Bangladesh’s Infrastructure in Recent Years” captures the significant infrastructural projects undertaken and completed in Bangladesh over recent years.

The dataset features 75 different classes of Butterflies. The dataset contains about 1000+ labelled images including the validation images.

Explore an extensive dataset of 30,000 plant images, with 1,000 images per class and a diverse collection of 30 plant classes and 7 plant types