斯坦福大学-犬类数据集
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The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. Contents of this dataset: 1.Number of categories: 120 2.Number of images: 20,580 3.Annotations: Class labels, Bounding boxes Dataset ReferencePrimary: Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex] Secondary: J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex] baseline Results This section contains baseline results on two tasks: Mean Accuracy The number of training images per class is varied from 1 to 100. Comparison of Accuracy per Class The accuracy of each class is compared for 15 and 100 training images per class. Experimental Setting All of the experiments use image regions from the bounding box only for both training and testing. The remaining parameters are set to the following values: Contact: Aditya Khosla、Nityananda、Jayadevaprakash、Bangpeng Yao、Li Fei-Fei aditya86@cs.stanford.edu bangpeng@cs.stanford.edu
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