基于ImageNet的网络压缩技术研究数据集

ImageNet数据集是由斯坦福大学教授李飞飞等人于2009年发起并构建的大型图像识别数据集，旨在推动计算机视觉领域的发展。它基于WordNet的语义层次结构，将图像分为多个类别，每个类别对应一种具体的物体、动物、场景或概念。其中，最为经典的版本是ImageNet2012（ILSVRC2012），它包含1000个细粒度类别，训练集有约128万张图像，测试集有10万张图像。这些图像涵盖了从自然场景到人造物体的广泛内容。

The ImageNet dataset is a large-scale image recognition dataset initiated and constructed in 2009 by Professor Fei-Fei Li and other researchers from Stanford University, aiming to promote the development of the computer vision field. It is based on the semantic hierarchy of WordNet, and divides images into multiple categories, each corresponding to a specific object, animal, scene or concept. Among them, the most classic version is ImageNet2012 (ILSVRC2012), which contains 1000 fine-grained categories, with approximately 1.28 million images in the training set and 100,000 images in the test set. These images cover a wide range of content from natural scenes to man-made objects.