Caltech101 Image Dataset

==Description Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc Aurelio Ranzato. The size of each image is roughly 300 x 200 pixels. We have carefully clicked outlines of each object in these pictures, these are included under the Annotations.tar . There is also a matlab script to view the annotaitons, show_annotations.m . How to use the dataset If you are using the Caltech 101 dataset for testing your recognition algorithm you should try and make your results comparable to the results of others. We suggest training and testing on fixed number of pictures and repeating the experiment with different random selections of pictures in order to obtain error bars. Popular number of training images: 1, 3, 5, 10, 15, 20, 30. Popular numbers of testing images: 20, 30. See also the discussion below. When you report your results please keep track of wh

描述：包含101个类别对象的图片。每个类别大约有40至800张图片，大多数类别约含50张。该数据集由李菲菲、马尔科·安德雷托和马尔克·奥雷利奥·兰扎托于2003年9月收集。每张图片的尺寸约为300 x 200像素。我们对这些图片中的每个对象均进行了精心绘制的轮廓标注，这些标注包含在Annotations.tar文件中。此外，还附有用于查看标注的matlab脚本，即show_annotations.m。数据集使用说明：若您使用Caltech 101数据集测试您的识别算法，应尽力使您的结果与其他人的结果具有可比性。我们建议在固定数量的图片上进行训练和测试，并通过不同随机图片选择重复实验以获取误差条。常见的训练图片数量：1、3、5、10、15、20、30。常见的测试图片数量：20、30。请参阅以下讨论。在报告结果时，请记录相关信息。