What is the name of the large repository of labeled images used for training computer vision models?

What is the name of the large repository of labeled images used for training computer vision models?

• A. OpenImage
• B. Coco Dataset
• C. ImageNet
• D. ImageBank

Answer: (C)

ImageNet is a widely recognized and significant repository of labeled images that serves as a foundational resource for training computer vision models. It contains millions of images categorized into thousands of classes, providing a rich dataset that allows machine learning algorithms to learn visual patterns and features effectively. The comprehensive nature of ImageNet has made it instrumental in benchmarking the performance of various deep learning models, particularly convolutional neural networks, which have shown remarkable advancements in tasks such as image classification and object detection. The dataset has been meticulously curated with annotations, allowing researchers and developers to use it for a variety of applications in computer vision. Its impact is also evident in the organization of the annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which has spurred innovation and competition in the field, leading to significant breakthroughs in AI technology. Other options, while they represent important datasets in their own right, either serve different purposes or may have a narrower focus compared to ImageNet. For instance, the COCO dataset is geared towards object detection and segmentation tasks but does not have the same extensive range of categories or the same historical significance in the evolution of computer vision training. OpenImage contains a variety of labeled images as well, but it does not have the same level of recognition or specificity

ImageNet is a widely recognized and significant repository of labeled images that serves as a foundational resource for training computer vision models. It contains millions of images categorized into thousands of classes, providing a rich dataset that allows machine learning algorithms to learn visual patterns and features effectively. The comprehensive nature of ImageNet has made it instrumental in benchmarking the performance of various deep learning models, particularly convolutional neural networks, which have shown remarkable advancements in tasks such as image classification and object detection.

The dataset has been meticulously curated with annotations, allowing researchers and developers to use it for a variety of applications in computer vision. Its impact is also evident in the organization of the annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which has spurred innovation and competition in the field, leading to significant breakthroughs in AI technology.

Other options, while they represent important datasets in their own right, either serve different purposes or may have a narrower focus compared to ImageNet. For instance, the COCO dataset is geared towards object detection and segmentation tasks but does not have the same extensive range of categories or the same historical significance in the evolution of computer vision training. OpenImage contains a variety of labeled images as well, but it does not have the same level of recognition or specificity