Popular Computer Vision Datasets for Medical Imaging

ChestX-ray14

Dataset link: https://www.v7labs.com/open-datasets/chestx-ray14

The ChestXray14 dataset is obtained from seventy hospitals which includes 112,008 frontal view X-ray images of 30,000 patients. Every image has 14 disease label attributes that include pneumonia, emphysema, and fibrosis among others. The dataset is employed in the training and testing of disease diagnostics in medical images.

ISIC (International Skin Imaging Collaboration)

Dataset link: https://challenge.isic-archive.com/data/

ISIC is a large public database that includes more than a thousand dermoscopic images of skin lesions with annotations of different skin diseases such as melanoma. It is one of the contributions to the improvement of research in dermatoscopy automated image analysis for skin cancer; it has data for segmentation of lesion, classification of disease and analysis of skin conditions.

Kinetics-700

Dataset link: https://github.com/cvdfoundation/kinetics-dataset

There are 650,000 clips in this massive video dataset, which covers 700 different human motion types. The videos show both human-to-human and human-to-object interactions, such as embracing and playing instruments. At least seven hundred video clips are included in each action class, and each clip has an action class annotation that lasts for roughly ten seconds.

Cityscapes

Dataset link: https://www.cityscapes-dataset.com/

Cityscapes is a library that includes a wide range of stereo video clips taken in various street settings across fifty different locations. The pictures were taken over time in a range of weather and light circumstances. Cityscapes dataset includes semantic, instance-wise, and dense pixel annotations. They have it for 30 classes divided into 8 categories. It offers 20,000 coarsely annotated frames and 5000 frames with pixel-level annotations.

LabelMe-12–50k

Dataset link: https://www.ais.uni-bonn.de/download/datasets.html

This dataset has fifty thousand JPEG images with twelve classes (thirty thousand for testing and forty thousand for training). The pictures are taken out of LabelMe. Classes comprise things like people, cars, trees, and keyboards. The training and testing set contains 50% of photos with a centered object and 50% with a randomly selected section of an image (referred to as “clutter”). This dataset is suitable for object recognition.

Computer Vision is an area in the field of Artificial Intelligence that enables machines to interpret and understand visual information. As in case of any other AI application, Computer vision also requires huge amount of data to give accurate results. These datasets provide all the necessary training material for these algorithms.

A dataset that will well-prepared and maintained will allow the model to learn from examples, recognize pattern and then make predictions about the unseen data. Therefore, the quality of datasets matters a lot, as it impacts the performance and robustness of computer vision applications.