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

Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain.

This dataset was collected in order to carry out segmentation, feature extraction, and classification tasks and compare the common segmentation.

The dataset consists of .dcm files containing MRI scans of the spine of the person with several dystrophic changes

An improved encoding method that restarts the detection process for each video independently. This ensures accuracy by avoiding connections to previous videos.

This dataset contains images of various plastic objects commonly found in everyday life. Each image is annotated with bounding boxes around the plastic items.