The dataset includes a CSV file (‘tree_nuts.csv’) that details the image labels and additional metadata.

An American Sign Language (ASL) dataset is a collection of visual data, such as images or videos, that captures hand gestures and signs used in American Sign Language. These datasets are essential for developing and training machine learning models for sign language recognition, interpretation, and translation.

There are several images in different poses or with different environments for each Minifigure in the dataset. You can use this dataset for the image classification tasks or try to create more interesting things.

A Human Action Recognition (HAR) dataset is a collection of data, often in the form of videos or sequences of images, that is used for the task of recognizing and classifying human actions or activities performed in those videos. HAR datasets are widely used in computer vision and machine learning for applications such as surveillance, human-computer interaction, and activity monitoring.

CVC-ClinicDB is a database of frames extracted from colonoscopy videos. The dataset contains several examples of polyp frames & corresponding ground truth for them.

One-shot learning is a machine learning paradigm where the model is trained to recognize new objects or patterns with very limited examples, often just a single example per class. In the context of face recognition, one-shot learning aims to recognize a person’s face with just one image of that person.

The LOL dataset is composed of 500 low-light and normal-light image pairs and is divided into 485 training pairs and 15 testing pairs.

I found this dataset when it was not uploaded on Kaggle and was scattered across the internet. This dataset is a collection of multiple data sets I found online.

Images were gathered from Google searches and downloaded using app ‘download all images’ . I highly recommend this app as it is very fast and returns a zip file with the images which you can then unzip to a specific directory.

The dataset consists of 2500 images in total with the corresponding segmentation annotation files for seven different types of strawberry diseases.