Must-Have Datasets for Deep Learning in Computer Vision

The field of computer vision has great advancement due to advancement in deep learning algorithms. Deep learning models, particularly convolutional neural networks (CNNs), have shown outstanding performance in a variety of computer vision applications. However, these models require a large amount of labelled data for training. In this article, we will look at a complete list of datasets that can be used for deep learning computer vision applications. These datasets span a broad range of vision-related tasks, including as object detection, image classification, segmentation, and others.

Human Pose Dataset

MPII Human Pose Dataset

The MPII Human posture dataset is a cutting-edge benchmark for evaluating articulated human posture estimation. The dataset contains approximately 25K photos of over 40K humans with annotated body joints. The photos were acquired in a systematic manner using an established classification of everyday human activity. The dataset includes 410 images of human activities, each with its own activity label. Each image was taken from a YouTube movie and was accompanied by unannotated frames before and succeeding it. Furthermore, for the test set, we got more detailed annotations such as body part occlusions and 3D torso and head orientations.