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细粒度分类算法总结(细粒度分类综述):https://blog.csdn.net/qq_50001789/article/details/131362389
细粒度分类论文笔记汇总:https://blog.csdn.net/qq_50001789/article/details/131361910
该数据集由加州理工学院的研究团队在2010年提出,是细粒度分类领域的基准数据集之一,共有200个类别,11788张图片,其中训练集有5994张图片、测试集有5794张图像。
数据集下载地址:http://www.vision.caltech.edu/datasets/cub_200_2011
论文地址:http://www.vision.caltech.edu/visipedia/papers/CUB_200_2011.pdf
引用:
@article{wah2011caltech,
title={The caltech-ucsd birds-200-2011 dataset},
author={Wah, Catherine and Branson, Steve and Welinder, Peter and Perona, Pietro and Belongie, Serge},
year={2011},
publisher={California Institute of Technology}
}
该数据集由斯坦福大学的研究团队在2013年提出,是细粒度分类领域的基准数据集之一,共有196个类别,16185张图片,其中训练集有8144张图片、测试集有8041张图片。
数据集下载地址:https://ai.stanford.edu/~jkrause/cars/car_dataset.html
论文地址:https://ai.stanford.edu/~jkrause/papers/3drr13.pdf
引用:
@inproceedings{KrauseStarkDengFei-Fei_3DRR2013,
title = {3D Object Representations for Fine-Grained Categorization},
booktitle = {4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13)},
year = {2013},
address = {Sydney, Australia},
author = {Jonathan Krause and Michael Stark and Jia Deng and Li Fei-Fei}
}s
该数据集在2013年被提出,是细粒度分类领域的基准数据集之一,共有102个类别,10200张图片,每个类别均有100张图像,数据集以均等的方式划分为训练集、验证集、测试集,前两个子集可用于训练,测试集用于评估模型。
数据集下载地址:https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/
论文地址:https://arxiv.org/pdf/1306.5151.pdf
引用:
@article{maji2013fine,
title={Fine-grained visual classification of aircraft},
author={Maji, Subhransu and Rahtu, Esa and Kannala, Juho and Blaschko, Matthew and Vedaldi, Andrea},
journal={arXiv preprint arXiv:1306.5151},
year={2013}
}
该数据集由斯坦福大学的研究团队在2011年提出,是细粒度分类领域的基准数据集之一,共有120个类别,20580张图片,其中训练集每类有100张图片,其余的用于测试(每类至少50张)。
数据集下载地址:http://vision.stanford.edu/aditya86/ImageNetDogs/
论文地址:http://people.csail.mit.edu/khosla/papers/fgvc2011.pdf
引用:
@inproceedings{khosla2011novel,
title={Novel dataset for fine-grained image categorization: Stanford dogs},
author={Khosla, Aditya and Jayadevaprakash, Nityananda and Yao, Bangpeng and Li, Fei-Fei},
booktitle={Proc. CVPR workshop on fine-grained visual categorization (FGVC)},
volume={2},
number={1},
year={2011},
organization={Citeseer}
}
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