Popular Computer Vision Datasets for Image Segmentation
Cityscapes
Dataset link: https://www.cityscapes-dataset.com/
The Cityscapes dataset is entirely oriented towards the semantic analysis of the scene in the urban environment. It consists of five thousand higher resolution images and pixel-level tags as well as twenty thousand weak annotations. The images are captured in 50 different cities under various weather conditions, and there are ample data available for activities such as semantic segmentation, instance segmentation, and object detection in the urban setting.
ADE20K
Dataset link: https://groups.csail.mit.edu/vision/datasets/ADE20K/
ADE20K from the MIT Scene Parsing Benchmark is another dataset with more than 20,000 images, shared for scenes and objects. Every picture is provided with pixel-level density of objects and/or stuff. Some tasks that the dataset is used for include scene understanding, objects’ detection and instance segmentation.
CamVid
Dataset link: https://groups.csail.mit.edu/vision/datasets/ADE20K/
CamVid database is a driving video sequences labeled for each pixel in terms of the object class semantics. It has high image resolution with 701 color images labeled on the pixel level for thirty-two classes. This dataset is used for conducting research in autonomous driving and gives a real taste of semantic segmentation.
Dataset for Computer Vision
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.